← Lex Fridman

Joscha Bach: Nature of Reality, Dreams, and Consciousness

Episode 2122021-08-21English

Guests: Joscha Bach

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§01Transcript · 00:00–08:23

00:00The following is a conversation with Yosha Bach,

00:02his second time on the podcast.

00:04Yosha is one of the most fascinating minds in the world,

00:08exploring the nature of intelligence,

00:10cognition, computation, and consciousness.

00:14To support this podcast, please check out our sponsors,

00:17Coinbase, Codecademy, Linode, NetSuite, and ExpressVPN.

00:22Their links are in the description.

00:26This is the Lex Friedman Podcast,

00:29and here is my conversation with Yosha Bach.

00:32Thank you for once again coming on

00:35to this particular Russian program,

00:38and sticking to the theme of a Russian program.

00:40Let's start with the darkest of topics.

00:43- Привет.

00:43(both laughing)

00:45- So this is inspired by one of your tweets.

00:48You wrote that, quote, "When life feels unbearable,

00:52"I remind myself that I'm not a person.

00:56"I'm a piece of software running on the brain

00:58"of a random ape for a few decades.

01:01"It's not the worst brain to run on."

01:03Have you experienced low points in your life?

01:07Have you experienced depression?

01:09- Of course, we all experience low points in our life,

01:12and we get appalled by the things,

01:15by the ugliness of stuff around us.

01:17We might get desperate about our lack of self-regulation,

01:21and sometimes life is hard,

01:24and I suspect you don't get to your life,

01:27nobody does to get to their life without low points,

01:30and without moments where they're despairing.

01:33And I thought that, let's capture this state,

01:37and how to deal with that state.

01:40And I found that very often,

01:42you realize that when you stop taking things personally,

01:44when you realize that this notion of a person is a fiction,

01:49similar as it is in Westworld,

01:50where the robots realize that their memories and desires

01:53are the stuff that keeps them in the loop,

01:55and they don't have to act on those memories and desires,

01:59that our memories and expectations is what make us unhappy.

02:02And the present rarely does.

02:04The day in which we are, for the most part, it's okay, right?

02:08When we are sitting here, right here, right now,

02:11we can choose how we feel.

02:13And the thing that affects us is the expectation

02:16that something is going to be different

02:18from what we want it to be,

02:19or the memory that something was different

02:21from what you wanted it to be.

02:24And once we basically zoom out from all this,

02:27what's left is not a person.

02:29What's left is this state of being conscious,

02:32which is a software state.

02:33And software doesn't have an identity.

02:35It's a physical law.

02:36And it's a law that acts in all of us,

02:39and it's embedded in a suitable substrate.

02:42And we didn't pick that substrate, right?

02:43We are mostly randomly instantiated on it.

02:46And there are all these individuals,

02:48and everybody has to be one of them.

02:51And eventually you're stuck on one of them,

02:54and have to deal with that.

02:56- So you're like a leaf floating down the river.

02:59You just have to accept that there's a river,

03:01and you just float wherever it takes you.

03:03- You don't have to do this.

03:04The thing is that the illusion

03:06that you are an agent is a construct.

03:09What part of that is actually under your control?

03:13And I think that our consciousness

03:15is largely a control model for our own attention.

03:18So we notice where we are looking,

03:21and we can influence what we are looking,

03:22how we are disambiguating things,

03:24how we put things together in our mind.

03:26And the whole system that runs us

03:28is this big cybernetic motivational system.

03:30So we're basically like a little monkey

03:32sitting on top of an elephant.

03:34And we can prod this elephant here and there

03:37to go this way or that way.

03:39And we might have the illusion that we are the elephant,

03:42or that we are telling it what to do.

03:43And sometimes we notice that it walks

03:45into a completely different direction.

03:47And we didn't set this thing up.

03:49It just is the situation that we find ourselves in.

03:52- How much prodding can we actually do of the elephant?

03:55- A lot.

03:57But I think that our consciousness

04:00cannot create the motive force.

04:03- Is the elephant consciousness in this metaphor?

04:05- No, the monkey is the consciousness.

04:07The monkey is the attentional system

04:09that is observing things.

04:10There is a large perceptual system

04:12combined with a motivational system

04:14that is actually providing the interface to everything

04:17and our own consciousness,

04:18I think is the tool that directs the attention

04:21of that system, which means it singles out features

04:24and performs conditional operations

04:27for which it needs an index memory.

04:28But this index memory is what we perceive

04:31as our stream of consciousness.

04:32But the consciousness is not in charge.

04:34That's an illusion.

04:35- So everything outside of that consciousness

04:40is the elephant.

04:41So it's the physics of the universe,

04:43but it's also society that's outside of your...

04:46- I would say the elephant is the agent.

04:48So there is an environment to which the agent is stomping

04:51and you are influencing a little part of that agent.

04:55- So can you, is the agent a single human being?

04:59What's, which object has agency?

05:02- That's an interesting question.

05:03I think a way to think about an agent

05:06is that it's a controller with a set point generator.

05:09The notion of a controller comes from cybernetics

05:13and control theory.

05:14Control system consists out of a system

05:17that is regulating some value

05:20and the deviation of that value from a set point.

05:24And it has a sensor that measures the system's deviation

05:27from that set point and an effector

05:30that can be parametrized by the controller.

05:32So the controller tells the effector to do a certain thing.

05:35And the goal is to reduce the distance

05:38between the set point and the current value of the system.

05:40And there's environment which disturbs the regulated system,

05:43which brings it away from that set point.

05:45So simplest case is a thermostat.

05:47The thermostat is really simple

05:49because it doesn't have a model.

05:50The thermostat is only trying to minimize

05:52the set point deviation in the next moment.

05:55And if you want to minimize the set point deviation

05:58over a longer time span, you need to integrate it.

06:00You need to model what is going to happen.

06:03So for instance, when you think about

06:05that your set point is to be comfortable in life,

06:08maybe you need to make yourself uncomfortable first.

06:11Right, so you need to make a model

06:12of what's going to happen when.

06:14This is task of the controller is to use its sensors

06:18to measure the state of the environment

06:20and the system that is being regulated

06:22and figure out what to do.

06:24And if the task is complex enough,

06:27the set points are complicated enough.

06:30And if the controller has enough capacity

06:32and enough sensor feedback,

06:34then the task of the controller is to make a model

06:37of the entire universe that it's in,

06:39the conditions under which it exists and of itself.

06:42And this is a very complex agent.

06:43And we are in that category.

06:45And an agent is not necessarily a thing in the universe.

06:49It's a class of models that we use

06:51to interpret aspects of the universe.

06:54And when we notice the environment around us,

06:57a lot of things only make sense

06:59at the level that we're entangled with them

07:00if we interpret them as control systems

07:03that make models of the world

07:04and try to minimize their own set points.

07:07- So but the models are the agents.

07:10- The agent is a class of model.

07:12And we notice that we are an agent ourself.

07:14We are the agent that is using our own control model

07:17to perform actions.

07:18We notice we produce a change in the model

07:22and things in the world change.

07:23And this is how we discover the idea that we have a body,

07:26that we are situated in an environment,

07:28and that we have a first-person perspective.

07:31- Still don't understand what's the best way to think

07:34of which object has agency with respect to human beings.

07:39Is it the body?

07:41Is it the brain?

07:43Is it the contents of the brain that has agency?

07:46Like what's the actuators that you're referring to?

07:49What is the controller and where does it reside?

07:52Or is it these impossible things?

07:54'Cause I keep trying to ground it to space-time,

07:57the three-dimensional space and the one dimension of time.

08:01What's the agent in that for humans?

08:04- There is not just one.

08:06It depends on the way in which you're looking at the thing

08:08and which you're framing it.

08:10Imagine that you are, say, Angela Merkel,

08:13and you are acting on behalf of Germany.

08:16Then you could say that Germany is the agent.

08:19And in the mind of Angela Merkel,

08:21she is Germany to some extent,

08:23because in the way in which she acts,

§02Transcript · 08:25–17:55

08:25the destiny of Germany changes.

08:28There are things that she can change

08:29that basically affect the behavior of that nation state.

08:33- Okay, so it's hierarchies of...

08:35To go to another one of your tweets

08:37with, I think you were playfully mocking Jeff Hawkins

08:42with saying his brain's all the way down.

08:44So it's like, it's agents all the way down?

08:49It's agents made up of agents made up of agents?

08:51Like if Angela Merkel's Germany

08:54and Germany's made up of a bunch of people

08:56and the people are themselves agents in some kind of context

09:01and then the people are made up of cells, each individual.

09:04So is it agents all the way down?

09:07- I suspect that has to be like this

09:08in a world where things are self-organizing.

09:12Most of the complexity that we are looking at,

09:15everything in life is about self-organization.

09:18So I think up from the level of life, you have agents.

09:23And below life, you rarely have agents

09:27because sometimes you have control systems

09:30that emerge randomly in nature

09:31and try to achieve a set point,

09:33but they're not that interesting agents that make models.

09:36And because to make an interesting model of the world,

09:39you typically need a system that is Turing complete.

09:42- Can I ask you a personal question?

09:44What's the line between life and non-life?

09:48It's personal because you're a life form.

09:52So where do you think in this emerging complexity,

09:55at which point does the thing start being living

09:58and have agency?

09:59- Personally, I think that the simplest answer

10:01that is that life is cells because--

10:04- Life is what?

10:05- Cells.

10:06- Cells.

10:07- Biological cells.

10:07So it's a particular kind of principle

10:09that we have discovered to exist in nature.

10:12It's modular stuff that consists out of basically

10:17this DNA tape with a redried head on top of it

10:20that is able to perform arbitrary computations

10:23and state transitions within the cell.

10:25And it's combined with a membrane

10:27that insulates the cell from its environment.

10:30And there are chemical reactions inside of the cell

10:35that are in disequilibrium.

10:36And the cell is running in such a way

10:39that this disequilibrium doesn't disappear.

10:41And if the cell goes into an equilibrium state, it dies.

10:46And it requires something like a neck entropy extractor

10:50to maintain this disequilibrium.

10:52So it's able to harvest neck entropy from its environment

10:56and keep itself running.

10:58- Yeah, so there's information and there's a wall

11:00to maintain this disequilibrium.

11:04But isn't this very earth-centric?

11:07Like what you're referring to as-

11:09- I'm not making a normative notion.

11:11You could say that there are probably other things

11:13in the universe that are cell-like and life-like,

11:16and you could also call them life,

11:18but eventually it's just a willingness

11:21to find an agreement of how to use the terms.

11:24I like cells because it's completely coextensional

11:26with the way that we use the word

11:28even before we knew about cells.

11:30So people were pointing at some stuff

11:32and saying this is somehow animate,

11:34and this is very different from the non-animate stuff,

11:36and what's the difference between the living

11:39and the dead stuff?

11:40And it's mostly whether the cells are working or not.

11:43And also this boundary of life where we say that,

11:46for instance, a virus is basically an information packet

11:49that is subverting the cell and not life by itself.

11:51That makes sense to me.

11:54And it's somewhat arbitrary.

11:56You could, of course, say that systems

11:58that permanently maintain a disequilibrium

12:00and can self-replicate are always life.

12:03And maybe that's a useful definition too,

12:06but this is eventually just how you want to use the word.

12:10- Is it so useful for conversation,

12:12but is it somehow fundamental to the universe?

12:17Do you think there's an actual line

12:19to eventually be drawn between life and non-life,

12:22or is it all a kind of continuum?

12:24- I don't think it's a continuum,

12:25but there's nothing magical that is happening.

12:28Living systems are a certain type of machine.

12:31- What about non-living systems?

12:33Is it also a machine?

12:34- There are non-living machines,

12:36but the question is at which point is a system able

12:39to perform arbitrary state transitions

12:43to make representations?

12:45And living things can do this.

12:47And of course, we can also build non-living things

12:49that can do this, but we don't know anything in nature

12:52that is not a cell and is not created by cellular life

12:56that is able to do that.

12:58Not only do we not know,

13:03I don't think we have the tools to see otherwise.

13:06I always worry that we look at the world too narrowly.

13:11Like there could be life of a very different kind

13:15right under our noses that we're just not seeing

13:19because we're not either limitations

13:21of our cognitive capacity,

13:23or we're just not open-minded enough,

13:27either with the tools of science

13:28or just the tools of our mind.

13:31- Yeah, that's possible.

13:33I find this thought very fascinating.

13:35And I suspect that many of us ask ourselves since childhood,

13:39what are the things that we are missing?

13:40What kind of systems and interconnections exist

13:43that are outside of our gaze?

13:46But we are looking for it,

13:51and physics doesn't have much room at the moment

13:55for opening up something that would not violate

13:59the conservation of information as we know it.

14:02- Yeah, but I wonder about time scale and scale,

14:07spatial scale, whether we just need to open up our idea

14:12of how life presents itself.

14:15It could be operating at a much slower time scale,

14:18a much faster time scale.

14:20And it's almost sad to think that there's all this life

14:24around us that we're not seeing,

14:25because we're just not thinking in terms of the right scale,

14:30both time and space.

14:34- What is your definition of life?

14:36What do you understand as life?

14:37- Entities of sufficiently high complexity

14:44that are full of surprises.

14:46(both laughing)

14:51I don't know, I don't have a free will,

14:54so that just came out of my mouth.

14:55I'm not sure that even makes sense.

14:57There's certain characteristics.

14:59So complexity seems to be a necessary property of life.

15:04And I almost want to say it has ability

15:09to do something unexpected.

15:11- It seems to me that life is the main source

15:15of complexity on earth.

15:17- Yes.

15:19- And complexity is basically a bridgehead

15:22that order builds into chaos by modeling,

15:27by processing information in such a way

15:29that you can perform reactions

15:31that would not be possible for dump systems.

15:33And this means that you can harvest like entropy

15:36that dump systems cannot harvest.

15:37And this is what complexity is mostly about.

15:40In some sense, the purpose of life is to create complexity.

15:45- Yeah, increasing.

15:46I mean, there seems to be some kind of universal drive

15:51towards increasing pockets of complexity.

15:54I don't know what that is.

15:57That seems to be like a fundamental,

16:00I don't know if it's a property of the universe

16:02or it's just a consequence of the way the universe works.

16:05But there seems to be this small pockets

16:08of emergent complexity that builds on top of each other

16:11and starts having like greater and greater complexity

16:15by having like a hierarchy of complexity.

16:18Little organisms building up a little society

16:20that then operates almost as an individual organism itself.

16:24And all of a sudden you have Germany and Merkel.

16:27- But that's not obvious to me.

16:28Everything that goes up has to come down at some point.

16:32Right, so if you see this big exponential curve somewhere,

16:36it's usually the beginning of an S-curve.

16:39Or something eventually reaches saturation

16:41and the S-curve is the beginning of some kind of bump

16:43that goes down again.

16:45And there is just this thing that when you are

16:49in sight of an evolution of life,

16:53you are on top of a puddle of negentropy

16:55that is being sucked dry by life.

16:58And during that happening, you see an increase in complexity

17:02because life forms are competing with each other

17:04to get more and more and finer and finer corner

17:09of that negentropy extraction.

17:11- But I feel like that's a gradual, beautiful process

17:14that almost follows a process akin to evolution.

17:18And the way it comes down is not the same way it came up.

17:23The way it comes down is usually harshly and quickly.

17:26So usually there's some kind of catastrophic event.

17:30- Well, the Roman Empire took a long time.

17:32- But would you classify that

17:37as a decrease in complexity though?

17:39- Yes, I think that this size of the cities

17:42that could be fed has decreased dramatically.

17:44And you could see that the quality of the art decreased

17:47and it did so gradually.

17:50And maybe future generations,

17:53when they look at the history of the United States

17:55in the 21st century,

§03Transcript · 17:57–26:33

17:57will also talk about the gradual decline,

17:59not something that suddenly happens.

18:01- Do you have a sense of where we are?

18:07Are we on the exponential rise?

18:09Are we at the peak?

18:11Or are we at the downslope of the United States empire?

18:15- It's very hard to say from a single human perspective,

18:18but it seems to me that we are probably at the peak.

18:23- I think that's probably the definition

18:26of like optimism and cynicism.

18:29So my nature of optimism is I think we're on the rise.

18:33(both laughing)

18:35But I think this is just all a matter of perspective.

18:39Nobody knows, but I do think that erring

18:41on the side of optimism,

18:43like you need a sufficient number,

18:45you need a minimum number of optimists

18:47in order to make that up thing actually work.

18:50And so I tend to be on the side of the optimists.

18:53- I think that we are basically a species of grasshoppers

18:56that have turned into locusts.

18:58And when you are in that locust mode,

19:00you see an amazing rise of population numbers

19:04and of the complexity of the interactions

19:07between the individuals.

19:08But it's ultimately the question is, is it sustainable?

19:12- See, I think we're a bunch of lions and tigers

19:16that have become domesticated cats,

19:18to use a different metaphor.

19:21And so I'm not exactly sure we're so destructive,

19:24we're just softer and nicer and lazier.

19:27- I think we have monkeys and not the cats.

19:29And if you look at the monkeys, they are very busy.

19:33- The ones that have a lot of sex, those monkeys?

19:35- Not just the bonobos.

19:37I think that all the monkeys are basically

19:38a discontent species that always needs to meddle.

19:41- Well, the gorillas seem to have

19:44a little bit more of a structure,

19:45but it's a different part of the tree.

19:48Okay, you mentioned the elephant

19:52and the monkey riding the elephant.

19:55And consciousness is the monkey.

19:58And there's some prodding that the monkey gets to do.

20:03And sometimes the elephant listens.

20:05I heard you got into some contentious,

20:08maybe you can correct me,

20:09but I heard you got into some contentious

20:11free will discussions.

20:12Is this with Sam Harris or something like that?

20:16- Not that I know of.

20:17- Some people on Clubhouse told me

20:20you made a bunch of big debate points about free will.

20:25Well, let me just then ask you,

20:27where in terms of the monkey and the elephant,

20:31do you think we land in terms of the illusion of free will?

20:35How much control does the monkey have?

20:37- We have to think about what the free will is

20:41in the first place.

20:43We are not the machine.

20:44We are not the thing that is making the decisions.

20:46We are a model of that decision-making process.

20:49And there is a difference between

20:52making your own decisions

20:54and predicting your own decisions.

20:56And that difference is the first person perspective.

20:59And what basically makes decision-making

21:04under conditions of free will distinct

21:06from just automatically doing the best thing

21:09is that we often don't know what the best thing is.

21:13We make decisions under uncertainty.

21:15We make informed bets using a betting algorithm

21:17that we don't yet understand

21:19because we haven't reverse engineered

21:20our own minds sufficiently.

21:22We don't know the expected rewards.

21:23We don't know the mechanism by which we estimate

21:25the rewards and so on.

21:27- But there is an algorithm.

21:28- We observe ourselves performing

21:30where we see that we weigh facts and factors

21:34and the future, and then some kind of possibility,

21:39some motive gets raised to an intention.

21:41And that's informed bet that the system is making.

21:44And that making of the informed bet,

21:46the representation of that is what we call free will.

21:49And it seems to be paradoxical

21:51because we think that's the crucial thing

21:53is that it's somehow indeterministic.

21:56And yet if it was indeterministic, it would be random.

21:59And it cannot be random because if it was random,

22:03if just dice were being thrown in the universe

22:05randomly forces you to do things, it would be meaningless.

22:08So the important part of the decisions

22:10is always the deterministic stuff.

22:12But it appears to be indeterministic to you

22:15because it's unpredictable.

22:16Because if it was predictable,

22:18you wouldn't experience it as a free will decision.

22:21You would experience it as just doing

22:23the necessary right thing.

22:25And you see this continuum between the free will

22:28and the execution of automatic behavior

22:31when you're observing other people.

22:33So for instance, when you are observing your own children,

22:36if you don't understand them,

22:37you will use this agent model

22:40where you have an agent with a set point generator.

22:43And the agent is doing the best it can

22:45to minimize the difference to the set point.

22:47And it might be confused and sometimes impulsive or whatever,

22:51but it's acting on its own free will.

22:53And when you understand what happens

22:55in the mind of the child, you see that it's automatic.

22:58And you can outmodel the child,

23:00you can build things around the child

23:02that will lead the child to making exactly the decision

23:05that you are predicting.

23:06And under these circumstances,

23:08like when you are a stage musician

23:10or somebody who is dealing with people

23:13that you sell a car to,

23:15and you completely understand the psychology

23:17and the impulses and the space of thoughts

23:19that this individual can have at that moment.

23:21Under these circumstances,

23:22it makes no sense to attribute free will

23:24because it's no longer decision-making under uncertainty.

23:28You are already certain.

23:29For them, there's uncertainty,

23:30but you already know what they're doing.

23:32- But what about for you?

23:35So is this akin to systems like cellular automata

23:40where it's deterministic,

23:44but when you squint your eyes a little bit,

23:47it starts to look like there's agents making decisions

23:51at the higher, sort of when you zoom out

23:54and look at the entities that are composed

23:57by the individual cells.

23:59Even though there's underlying simple rules

24:03that make the system evolve in deterministic ways,

24:08it looks like there's organisms making decisions.

24:11Is that where the illusion of free will emerges,

24:15that jump in scale?

24:17- It's a particular type of model,

24:19but this jump in scale is crucial.

24:21The jump in scale happens whenever

24:23you have too many parts to count

24:24and you cannot make a model at that level,

24:26and you try to find some higher level regularity.

24:29And the higher level regularity is a pattern

24:31that you project into the world to make sense of it.

24:35And agency is one of these patterns, right?

24:37You have all these cells that interact with each other,

24:40and the cells in our body are set up in such a way

24:42that they benefit if their behavior is coherent,

24:45which means that they act as if

24:47they were serving a common goal.

24:49And that means that they will evolve regulation mechanisms

24:52that act as if they were serving a common goal.

24:55And now you can make sense of all these cells

24:58by projecting the common goal into them.

25:00- Right, so for you then, free will is an illusion.

25:03- No, it's a model, and it's a construct.

25:06It's basically a model that the system

25:08is making of its own behavior,

25:09and it's the best model that it can come up with

25:11under the circumstances, and it can get replaced

25:14by a different model, which is automatic behavior,

25:16when you fully understand the mechanism

25:18under which you are acting.

25:19- Yeah, but another word for model is what?

25:22Story.

25:24So it's the story you're telling.

25:25I mean, do you actually have control?

25:27Is there such a thing as a you,

25:30and is there such a thing as you having control?

25:34So like, are you manifesting your evolution as an entity?

25:39- In some sense, the you is the model of the system

25:44that is in control.

25:45It's a story that the system tells itself

25:47about somebody who is in control.

25:50- Yeah.

25:51- And the contents of that model are being used

25:53to inform the behavior of the system.

25:54- Okay.

25:57- So the system is completely mechanical,

26:00and the system creates that story like a loom,

26:03and then it uses the contents of that story

26:06to inform its actions and writes the results

26:09of that action into the story.

26:11- So how is that not an illusion?

26:13The story is written then,

26:16or rather, we're not the writers of the story.

26:19- Yes, but we always knew that.

26:22- No, we don't know that.

26:25When did we know that?

26:26- I think that's mostly a confusion about concepts.

26:29The conceptual illusion in our culture comes from the idea

26:33that we live in physical reality,

§04Transcript · 26:35–34:11

26:35and that we experience physical reality,

26:37and that we have ideas about it.

26:39- Yep.

26:40- And then you have this dualist interpretation

26:41where you have two substances, res extensa,

26:45the world that you can touch

26:46and that is made of extended things,

26:48and res cogitans, which is the world of ideas.

26:51And in fact, both of them are mental representations.

26:54One is the representations of the world as a game engine

26:57that your mind generates to make sense

26:59of the perceptual data.

27:01And the other one's-- - That's the physical world?

27:02- Yes, that's what we perceive as the physical world.

27:04But we already know that the physical world

27:05is nothing like that, right?

27:07Quantum mechanics is very different

27:08from what you and me perceive as the world.

27:11The world that you and me perceive is a game engine.

27:14- Yeah.

27:14- And there are no colors and sounds in the physical world.

27:17They only exist in the game engine generated by your brain.

27:20And then you have ideas that cannot be mapped

27:22onto extended regions, right?

27:24So the objects that have a spatial extension

27:26in the game engine are res extensa,

27:29and the objects that don't have a physical extension

27:31in the game engine are ideas.

27:34And they both interact in our mind

27:36to produce models of the world.

27:38- Yep, but when you play video games,

27:41I understand that what's actually happening

27:45is zeros and ones inside of a computer,

27:50inside of a CPU and a GPU,

27:52but you're still seeing the rendering of that.

27:57And you're still making decisions

28:00whether to shoot to turn left or to turn right

28:03if you're playing a shooter.

28:05Every time I start thinking about Skyrim

28:07and Elder Scrolls and walking around in beautiful nature

28:09and swinging a sword.

28:10But it feels like you're making decisions

28:13inside that video game.

28:15So even though you don't have direct access

28:17in terms of perception to the bits, to the zeros and ones,

28:22it still feels like you're making decisions

28:24and your decisions actually feels like they're being applied

28:29all the way down to the zeros and ones.

28:32- Yes.

28:33- So it feels like you have control

28:33even though you don't have direct access to reality.

28:36So there is basically a special character in the video game

28:39that is being created by the video game engine.

28:41- Yeah.

28:42- And this character is serving the aesthetics

28:43of the video game.

28:45And that is you.

28:47- Yes, but I feel like I have control inside the video game.

28:50Like all those 12 year olds

28:53that kick my ass on the internet.

28:55- So when you play the video game,

28:57it doesn't really matter that there's zeros and ones.

28:59You don't care about the widths of the bus.

29:01You don't care about the nature of the CPU that it runs on.

29:04What you care about are the properties of the game

29:06that you're playing.

29:07And you hope that the CPU is good enough.

29:10- Yes.

29:10- And a similar thing happens when we interact with physics.

29:13The world that you and me are in is not the physical world.

29:16The world that you and me are in is a dream world.

29:18- How close is it to the real world though?

29:21- We know that it's not very close,

29:25but we know that the dynamics of the dream world

29:27match the dynamics of the physical world

29:29to a certain degree of resolution.

29:31- Right.

29:31- But the causal structure of the dream world is different.

29:34- So you see, for instance,

29:36waves crashing on your feet, right?

29:38But there are no waves in the ocean.

29:39There's only water molecules that have tangents

29:42between the molecules that are the result of electrons

29:47in the molecules interacting with each other.

29:50- Aren't they like very consistent?

29:52We're just seeing a very crude approximation.

29:55Isn't our dream world very consistent?

29:59Like to the point of being mapped directly one-to-one

30:02to the actual physical world,

30:04as opposed to us being completely tricked.

30:07This is like where you have like Donald Trump.

30:09- It's not a trick.

30:10That's my point.

30:10It's not an illusion.

30:11It's a form of data compression.

30:13- Yeah, yeah.

30:14- It's an attempt to deal with the dynamics

30:15of too many parts to count

30:16at the level at which we're entangled

30:18with the best model that you can find.

30:20- Yeah, so we can act in that dream world

30:22and our actions have impact in the real world,

30:26in the physical world.

30:27- Yes.

30:27- To which we don't have access.

30:28- Yes, but it's basically like accepting the fact

30:31that the software that we live in,

30:33the dream that we live in,

30:34is generated by something outside of this world

30:37that you and me are in.

30:38- So is the software deterministic

30:40and do we not have any control?

30:42Do we have...

30:43So free will is having a conscious being.

30:49Free will is the monkey being able to steer the elephant.

30:52- No, it's slightly different.

30:58Basically in the same way as you are modeling

31:00the water molecules in the ocean

31:02that engulf your feet when you are walking on the beach

31:05as waves and there are no waves,

31:07but only the atoms on more complicated stuff

31:09underneath the atoms and so on.

31:11And you know that, right?

31:13You would accept, yes,

31:15there is a certain abstraction that happens here.

31:17It's a simplification of what happens.

31:19And the simplification that is designed

31:22in such a way that your brain can deal with it,

31:24temporarily and spatially in terms of resources

31:27and tuned for the predictive value.

31:28So you can predict with some accuracy

31:31whether your feet are going to get wet or not.

31:33- But it's a really good interface and approximation.

31:37- Yes.

31:38- It's like E equals MC squared is a good...

31:40Equations are a good approximation

31:42for what they're much better approximation.

31:44So to me, waves is a really nice approximation

31:49of what's all the complexity that's happening underneath.

31:51- Basically it's a machine learning model

31:53that is constantly tuned to minimize surprises.

31:55So it basically tries to predict as well as it can

31:58what you're going to perceive next.

31:59- Are we talking about...

32:01Which is the machine learning,

32:02our perception system or the dream world?

32:05- The machine world is...

32:06Dream world is the result of the machine learning process

32:10of the perception system.

32:11- That's doing the compression.

32:12- Yes.

32:13And the model of you as an agent

32:15is not a different type of model or it's a different type,

32:19but not different as in its model-like nature

32:23from the model of the ocean, right?

32:25Some things are oceans, some things are agents.

32:28And one of these agents is using your own control model,

32:31the output of your model,

32:32the things that you perceive yourself as doing.

32:36And that is you.

32:38- What about the fact that like when you're standing

32:42with the water on your feet

32:45and you're looking out into the vast,

32:48like open water of the ocean,

32:52and then there's a beautiful sunset.

32:54And it...

32:55The fact that it's beautiful

32:56and then maybe you have like friends or a loved one with you

32:59and like you feel love.

33:00What is that?

33:01As the dream world or what is that?

33:02- Yes, it's all happening inside of the dream.

33:05- Okay.

33:06But see, the word dream makes it seem like it's not real.

33:11- No, of course it's not real.

33:12The physical universe is real,

33:16but the physical universe is incomprehensible

33:18and it doesn't have any feeling of realness.

33:21The feeling of realness that you experience

33:22gets attached to certain representations

33:25where your brain assesses,

33:26this is the best model of reality that I have.

33:28- So the only thing that's real to you

33:30is the thing that's happening at the very base of reality,

33:34like the...

33:36- Yeah, for something to be real,

33:37it needs to be implemented.

33:39So the model that you have of reality

33:42is real in as far as it is a model, right?

33:45It's an appropriate description of the world

33:47to say that there are models that are being experienced.

33:51But the world that you experience

33:54is not necessarily implemented.

33:56There is a difference between a reality,

33:59a simulation, and a simulacrum.

34:01The reality that we're talking about

34:04is something that fully emerges

34:06over a causally closed lowest layer.

34:08And the idea of physicalism is that we are in that layer,

34:11that basically our world emerges over that.

§05Transcript · 34:13–42:36

34:13Every alternative to physicalism is a simulation theory,

34:16which basically says that we are

34:18in some kind of simulation universe

34:19and the real world needs to be an apparent universe of that,

34:22where the actual causal structure is, right?

34:24And when you look at the ocean in your own mind,

34:27you are looking at a simulation

34:28that explains what you're going to see next.

34:31- So we are living in a simulation.

34:32- Yes, but a simulation generated by our own brains.

34:35- Yeah.

34:36- And this simulation is different from the physical reality

34:39because the causal structure that is being produced,

34:42what you are seeing,

34:42is different from the causal structure of physics.

34:44- But consistent.

34:45- Hopefully.

34:47If not, then you are going to end up

34:49in some kind of institution

34:51where people will take care of you

34:52because your behavior will be inconsistent, right?

34:54Your behavior needs to work in such a way

34:57that it's interacting

34:58with an accurately predictive model of reality.

35:01And if your brain is unable

35:02to make your model of reality predictive,

35:05you will need help.

35:06- So what do you think about Donald Hoffman's argument

35:10that it doesn't have to be consistent,

35:12the dream world to what he calls like the interface

35:17to the actual physical reality,

35:19where there could be evolution.

35:20I think he makes an evolutionary argument,

35:23which is like it could be an evolutionary advantage

35:26to have the dream world drift away from physical reality.

35:30- I think that only works if you have tenure.

35:32As long as you're still interacting with the ground truth,

35:35your model needs to be somewhat predictive.

35:37- Well, in some sense,

35:40humans have achieved a kind of tenure in the animal kingdom.

35:45- Yeah, and at some point we became too big to fail,

35:47so we became post-modernist.

35:49(laughing)

35:51- It all makes sense now.

35:52- It's a version of reality that we like.

35:55- Oh man, okay.

35:57- Yeah, but basically you can do magic.

36:00You can change your assessment of reality,

36:02but eventually reality is going to come bite you in the ass

36:05if it's not predictive.

36:06- Do you have a sense

36:09of what is that base layer of physical reality?

36:12You have these attempts at the theories of everything,

36:17the very, very small of like string theory,

36:21or what Stephen Wolfram talks about with a hypergrass.

36:25These are these tiny, tiny, tiny, tiny objects.

36:28And then there is more like quantum mechanics

36:31that's talking about objects that are much larger,

36:34but still very, very, very tiny.

36:36Do you have a sense of where the tiniest thing is

36:40that is like at the lowest level,

36:42the turtle at the very bottom?

36:44Do you have a sense of what that turtle is?

36:45- I don't think that you can talk about where it is

36:48because space is emergent over the activity of these things.

36:51So space, the coordinates only exist

36:55in relation to the other things.

36:58And so you could, in some sense, abstract it into locations

37:01that can hold information and trajectories

37:04that the information can take

37:05between the different locations.

37:06And this is how we construct our notion of space.

37:10And physicists usually have a notion of space

37:14that is continuous.

37:15And this is a point where I tend to agree

37:19with people like Stephen Wolfram,

37:20who are very skeptical of the geometric notions.

37:23I think that geometry is the dynamics

37:25of too many parts to count.

37:27And when there are no infinities,

37:30if there were two infinities,

37:32you would be running into contradictions,

37:34which is in some sense what Gödel and Turing discovered

37:37in response to Hilbert's call.

37:39- So there are no infinities.

37:41- There are no infinities.

37:42- Infinity is fake.

37:43- There is unboundedness,

37:44but if you have a language that talks about infinity,

37:46at some point the language is going to contradict itself,

37:49which means it's no longer valid.

37:51In order to deal with infinities in mathematics,

37:54you have to postulate the existence initially.

37:57You cannot construct the infinities.

37:59And that's an issue, right?

38:00You cannot build up an infinity from zero.

38:02But in practice, you never do this, right?

38:04When you perform calculations,

38:06you only look at the dynamics of too many parts to count.

38:09And usually these numbers are not that large.

38:13They're not googles or something.

38:15The infinities that we are dealing with in our universe

38:18are mathematically speaking, relatively small integers.

38:22And still, what we're looking at is dynamics

38:26where a trillion things behave similar

38:30to a hundred trillion things,

38:32or something that is very, very large,

38:37because they're converging.

38:39And these convergent dynamics, these operators,

38:41this is what we deal with when we are doing the geometry.

38:45Geometry is stuff where we can pretend that it's continuous,

38:48because if we subdivide the space sufficiently fine-grained,

38:53these things approach a certain dynamic.

38:56And this approach dynamic, that is what we mean by it.

38:59But I don't think that infinity would work.

39:01So to speak that you would know the last digit of pi,

39:05and that you have a physical process

39:06that rests on knowing the last digit of pi.

39:09- Yeah, that could be just a peculiar quirk

39:12of human cognition that we like discreet.

39:15Discreet makes sense to us.

39:16Infinity doesn't.

39:18So in terms of our intuitions.

39:19- No, the issue is that everything that we think about

39:22needs to be expressed in some kind of mental language,

39:25not necessarily a natural language,

39:27but some kind of mathematical language

39:29that your neurons can speak,

39:31that refers to something in the world.

39:34And what we have discovered is that

39:36we cannot construct a notion of infinity

39:39without running into contradictions,

39:40which means that such a language is no longer valid.

39:43And I suspect this is what made Pythagoras so unhappy

39:46when somebody came up with the notion of irrational numbers

39:49before it was time, right?

39:50There's this myth that he had this person killed

39:52when he blepped out the secret,

39:54that not everything can be expressed

39:55as a ratio between two numbers,

39:57but there are numbers between the ratios.

39:59The world was not ready for this.

40:01And I think he was right.

40:02That has confused mathematicians very seriously

40:06because these numbers are not values, they are functions.

40:09And so you can calculate these functions

40:11to a certain degree of approximation,

40:13but you cannot pretend that pi has actually a value.

40:17Pi is a function that would approach this value

40:20to some degree, but nothing in the world

40:22rests on knowing pi.

40:26- How important is this distinction

40:28between discrete and continuous for you to get to the bottom?

40:32'Cause there's a, I mean,

40:33in discussion of your favorite flavor

40:37of the theory of everything, there's a few on the table.

40:41So there's string theory, there's particular,

40:44there's a loop quantum gravity,

40:48which focused on one particular unification.

40:51There's just a bunch of favorite flavors

40:56of different people trying to propose

40:59a theory of everything.

41:01Eric Weinstein and a bunch of people throughout history.

41:04And then of course, Stephen Wolfram,

41:06who I think is one of the only people doing a discrete.

41:10- No, no, there's a bunch of physicists

41:12who do this right now.

41:13And like Toffoli and Tomasello.

41:17And digital physics is something that is, I think,

41:22growing in popularity.

41:24But the main reason why this is interesting is

41:29because it's important sometimes to settle disagreements.

41:35I don't think that you need infinities at all

41:37and you never needed them.

41:39You can always deal with very large numbers

41:41and you can deal with limits, right?

41:43We are fine with doing that.

41:44You don't need any kind of infinity.

41:46You can build your computer algebra systems just as well

41:49without believing in infinity in the first place.

41:51- So you're okay with limits?

41:52- Yeah, so basically a limit means that something

41:55is behaving pretty much the same

41:58if you make the number larger.

41:59Because it's converging to a certain value

42:02and at some point the difference becomes negligible

42:04and you can no longer measure it.

42:06And in this sense, you have things that,

42:09if you have an n-gon which has enough corners,

42:12then it's going to behave like a circle at some point.

42:15And it's only going to be in some kind of esoteric thing

42:18that cannot exist in the physical universe

42:21that you would be talking about this perfect circle.

42:23And now it turns out that it also wouldn't work

42:25in mathematics because you cannot construct mathematics

42:28that has infinite resolution

42:30without running into contradictions.

42:31So that is itself not that important

42:35because we never did that, right?

42:36It's just a thing that some people thought we could.

§06Transcript · 42:39–51:08

42:39And this leads to confusion.

42:40So for instance, Roger Penrose uses this as an argument

42:43to say that there are certain things

42:46that mathematicians can do dealing with infinities.

42:50And by extension, our mind can do

42:53that computers cannot do.

42:55- Yeah, he talks about that there's the human mind

42:58can do certain mathematical things

43:00that the computer as defined

43:02by the universal Turing machine cannot.

43:06- Yes.

43:07- So that it has to do with infinity.

43:08- Yes, it's one of the things.

43:10So he is basically pointing at the fact

43:13that there are things that are possible

43:15in the mathematical mind and in pure mathematics

43:20that are not possible in machines

43:24that can be constructed in the physical universe.

43:27And because he's an honest guy,

43:29he thinks this means that present physics

43:31cannot explain operations that happen in our mind.

43:34- Do you think he's right on the...

43:36So let's leave his discussion of consciousness aside

43:39for the moment.

43:40Do you think he's right about just

43:42what he's basically referring to as intelligence?

43:46So is the human mind fundamentally more capable

43:50as a thinking machine than a universal Turing machine?

43:53- No.

43:54- So he's suggesting that, right?

43:58- So our mind is actually less than a Turing machine.

44:01There can be no Turing machine

44:02because it's defined as having an infinite tape.

44:05And we always only have a finite tape.

44:07- But he's saying it's better.

44:08- Our minds can only perform finitely many operations.

44:10Yes, he thinks so.

44:11- It can do the kind of computation

44:13that a Turing machine cannot.

44:14- And that's because he thinks that our minds

44:16can do operations that have infinite resolution

44:19in some sense.

44:21And I don't think that's the case.

44:23Our minds are just able to discover these limit operators

44:26over too many parts to count.

44:27- I see.

44:28What about his idea that consciousness

44:32is more than a computation?

44:37So it's more than something that a Turing machine

44:40can do.

44:42So again, saying that there's something special

44:44about our mind that cannot be replicated in the machine.

44:47- The issue is that I don't even know

44:51how to construct a language

44:52to express this statement correctly.

44:56- Well, the basic statement is,

45:01there's a human experience that includes intelligence,

45:08that includes self-awareness,

45:09that includes the hard problem of consciousness.

45:12And the question is, can that be fully simulated

45:16in the computer, in the mathematical model of the computer

45:20as we understand it today?

45:22Roger Penrose says no.

45:25So the universal Turing machine

45:30cannot simulate the universe.

45:32- So the interesting question is,

45:34and you have to ask him this, is why not?

45:36What is the specific thing that cannot be modeled?

45:39And when I looked at his writings,

45:42and I haven't read all of it,

45:43but when I read, for instance,

45:45the section that he writes in the introduction

45:49to "A Road to Infinity,"

45:51the thing that he specifically refers to

45:53is the way in which human minds deal with infinities.

45:56And that itself can, I think, easily be deconstructed.

46:02A lot of people feel that our experience

46:05cannot be explained in a mechanical way,

46:07and therefore it needs to be different.

46:11And I concur, our experience is not mechanical.

46:14Our experience is simulated.

46:16It exists only in a simulation.

46:18Only a simulation can be conscious.

46:19Physical systems cannot be conscious

46:21because they're only mechanical.

46:23Cells cannot be conscious.

46:25Neurons cannot be conscious.

46:26Brains cannot be conscious.

46:27People cannot be conscious,

46:28as far as if you understand them as physical systems.

46:31What can be conscious is the story of the system

46:36in the world where you write all these things

46:37into the story.

46:39You have experiences for the same reason

46:41that a character in a novel has experiences,

46:43because it's written into the story.

46:45And now the system is acting on that story.

46:48And it's not a story that is written in a natural language.

46:50It's written in a perceptual language,

46:52in this multimedia language of the game engine.

46:55And in there, you write in what kind of experience you have

46:59and what this means for the behavior of the system,

47:01for your behavior tendencies, for your focus,

47:03for your attention, for your experience of valence,

47:05and so on.

47:06And this is being used to inform the behavior of the system

47:09in the next step.

47:10And then the story updates with the reactions of the system

47:15and the changes in the world and so on.

47:17And you live inside of that model.

47:19You don't live inside of the physical reality.

47:21- And, I mean, just to linger on it,

47:26like you see, okay, it's in the perceptual language,

47:30the multimodal perceptual language.

47:33That's the experience.

47:34That's what consciousness is within that model,

47:38within that story.

47:40But do you have agency?

47:42When you play a video game, you can turn left

47:46and you can turn right in that story.

47:48So in that dream world, how much control do you,

47:54is there such a thing as you in that story?

47:58Is it right to say the main character,

48:01everybody's NPCs, and then there's the main character,

48:04and you're controlling the main character?

48:07Or is that an illusion?

48:08Is there a main character that you're controlling?

48:10I'm getting to the point of the free will point.

48:14- Imagine that you are building a robot that plays soccer.

48:17And you've been to MIT computer science,

48:19you basically know how to do that.

48:22And so you would say the robot is an agent

48:25that solves a control problem.

48:27How to get the ball into the goal.

48:29And it needs to perceive the world,

48:30and the world is disturbing him in trying to do this.

48:33So he has to control many variables to make that happen

48:35and to project itself and the ball into the future

48:38and understand its position on the field

48:40relative to the ball and so on,

48:42and the position of its limbs

48:44or in the space around it and so on.

48:46So it needs to have an adequate model

48:48that abstracting reality in a useful way.

48:51And you could say that this robot does have agency

48:55over what it's doing in some sense.

48:58And the model is going to be a control model.

49:01And inside of that control model,

49:03you can possibly get to a point

49:05where this thing is sufficiently abstract

49:07to discover its own agency.

49:09Our current robots don't do that.

49:10They don't have a unified model of the universe.

49:13But there's not a reason why we shouldn't be getting there

49:16at some point in the not too distant future.

49:18And once that happens,

49:20you will notice that the robot tells a story

49:23about a robot playing soccer.

49:25So the robot will experience itself playing soccer

49:29in a simulation of the world that it uses

49:32to construct a model of the locations of its legs

49:35and limbs in space on the field

49:38with relationship to the ball.

49:39And it's not going to be at the level of the molecules.

49:42It will be an abstraction that is exactly at the level

49:45that is most suitable for past planning

49:47of the movements of the robot.

49:48Right, it's going to be a high-level abstraction,

49:51but a very useful one that is as predictive

49:53as you can make it.

49:55And in that side of that story,

49:56there is a model of the agency of that system.

49:58So this model can accurately predict

50:03that the contents of the model are going to be driving

50:06the behavior of the robot in the immediate future.

50:08- But there's the hard problem of consciousness,

50:12which I would also,

50:14there's a subjective experience of free will as well,

50:18that I'm not sure where the robot gets that,

50:20where that little leap is.

50:22Because for me right now,

50:24everything I imagine with that robot,

50:26as it gets more and more and more sophisticated,

50:29the agency comes from the programmer of the robot still,

50:33of what was programmed in.

50:35- You could probably do an end-to-end learning system.

50:38You maybe need to give it a few priors,

50:40so you nudge the architecture in the right direction

50:42that it converges more quickly.

50:44But ultimately discovering the suitable hyper parameters

50:47of the architecture is also only a search process, right?

50:50And as the search process was evolution,

50:52that has informed our brain architecture,

50:55so we can converge in a single lifetime

50:57on useful interaction with the world.

50:59- See, the problem is,

51:01if we define hyper parameters broadly,

51:03so it's not just the parameters that control

51:06this end-to-end learning system,

51:08but the entirety of the design of the robot.

§07Transcript · 51:10–59:35

51:10You have to remove the human completely from the picture.

51:15And then in order to build the robot,

51:17you have to create an entire universe.

51:20'Cause you have to go, you can't just shortcut evolution,

51:22you have to go from the very beginning.

51:24In order for it to have,

51:25'cause I feel like there's always a human

51:28pulling the strings,

51:29and that makes it seem like the robot is cheating,

51:33it's getting a shortcut to consciousness.

51:35- And you are looking at the current Boston Dynamics robots,

51:38it doesn't look as if there is somebody pulling the strings,

51:40it doesn't look like cheating anymore.

51:42- Okay, so let's go there,

51:43'cause I got to talk to you about this.

51:44So obviously with the case of Boston Dynamics,

51:47as you may or may not know,

51:49it's always either hard-coded or remote-controlled.

51:54There's no intelligence.

51:55- I don't know how the current generation

51:57of Boston Dynamics robots works,

51:59but what I've been told about the previous ones

52:02was that it's basically all cybernetic control,

52:05which means you still have feedback mechanisms and so on,

52:08but it's not deep learning for the most part

52:11as it's currently done.

52:13It's for the most part just identifying a control hierarchy

52:16that is congruent to the limbs that exist

52:19and the parameters that need to be optimized

52:21for the movement of these limbs,

52:22and then there is a convergence progress.

52:24So it's basically just regression

52:26that you would need to control this.

52:27But again, I don't know whether that's true,

52:29that's just what I've been told about how they work.

52:31- We have to separate several levels of discussions here.

52:34So the only thing they do is pretty sophisticated control

52:39with no machine learning

52:40in order to maintain balance or to right itself.

52:45It's a control problem in terms of using the actuators

52:49to when it's pushed or when it steps on a thing

52:52that's uneven, how to always maintain balance.

52:55And there's a tricky set of heuristics around that,

52:57but that's the only goal.

53:00Everything you see Boston Dynamics doing

53:02in terms of that to us humans is compelling,

53:06which is any kind of higher order movement,

53:09like turning, wiggling its butt,

53:12like jumping back on its two feet, dancing.

53:18Dancing is even worse because dancing is hard coded in.

53:22It's choreographed by humans, it's choreography software.

53:27So there is no, of all that high level movement,

53:30there's no anything that you can call,

53:34certainly can't call AI,

53:35there's no even like basic heuristics,

53:39it's all hard coded in.

53:41And yet we humans immediately project agency onto them,

53:46which is fascinating.

53:48- So the gap here is it doesn't necessarily have agency.

53:53What it has is cybernetic control.

53:55And the cybernetic control means you have a hierarchy

53:57of feedback loops that keep the behavior

53:59in certain boundaries so the robot doesn't fall over

54:02and it's able to perform the movements.

54:04And the choreography cannot really happen

54:06with motion capture because the robot would fall over

54:09because the physics of the robot,

54:10the weight distribution and so on is different

54:12from the weight distribution in the human body.

54:15So if you were using the directly motion captured movements

54:19of a human body to project it into this robot,

54:21it wouldn't work.

54:22You can do this with a computer animation,

54:24it will look a little bit off, but who cares.

54:26But if you want to correct for the physics,

54:29you need to basically tell the robot

54:31where it should move its limbs,

54:33and then the control algorithm is going

54:35to approximate a solution that makes it possible

54:38within the physics of the robot.

54:41And you have to find the basic solution

54:43for making that happen,

54:44and there's probably going to be some regression necessary

54:47to get the control architecture to make these movements.

54:51- But those two layers are separate.

54:52- Yes.

54:53- So the thing, the higher level instruction

54:56of how you should move and where you should move

54:59is a higher level.

54:59- Yes, so I expect that the control level of these robots

55:02at some level is dumb.

55:03This is just the physical control movement,

55:06the motor architecture.

55:07But it's a relatively smart motor architecture.

55:10It's just that there is no high level deliberation

55:12about what decisions to make necessarily.

55:14- But see, it doesn't feel like free will or consciousness.

55:17- No, no, that was not where I was trying to get to.

55:20I think that in our own body, we have that too.

55:24So we have a certain thing that is basically

55:26just a cybernetic control architecture

55:29that is moving our limbs.

55:31And deep learning can help in discovering

55:34such an architecture if you don't have it

55:35in the first place.

55:37If you already know your hardware,

55:38you can maybe handcraft it.

55:40But if you don't know your hardware,

55:41you can search for such an architecture.

55:43And this work already existed in the '80s and '90s.

55:46People were starting to search for control architectures

55:49by motor babbling and so on,

55:51and just use reinforcement learning architectures

55:53to discover such a thing.

55:55And now imagine that you have

55:57the cybernetic control architecture already inside of you.

56:01And you extend this a little bit.

56:03So you are seeking out food, for instance,

56:06or rest, and so on.

56:08And you get to have a baby at some point.

56:11And now you add more and more control layers to this.

56:15And the system is reverse engineering

56:17its own control architecture,

56:19and builds a high level model to synchronize

56:22the pursuit of very different conflicting goals.

56:26And this is how I think you get to purposes.

56:28Purposes are models of your goals.

56:30Your goals may be intrinsic as the result

56:32of the different set point violations that you have,

56:34hunger and thirst for very different things,

56:37and rest and pain avoidance and so on.

56:39And you put all these things together,

56:41and eventually you need to come up with a strategy

56:44to synchronize them all.

56:46And you don't need just to do this alone by yourself,

56:49because we are state building organisms.

56:51We cannot function as isolation

56:53the way that Homo sapiens is set up.

56:55So our own behavior only makes sense

56:58when you zoom out very far into a society,

57:01or even into ecosystemic intelligence on the planet,

57:04and our place in it.

57:06So the individual behavior only makes sense

57:08in these larger contexts.

57:10And we have a number of priors built into us.

57:11So we are behaving as if we were acting

57:14on these high level goals,

57:15pretty much right from the start.

57:17And eventually in the course of our life,

57:19we can reverse engineer the goals that we're acting on,

57:22what actually are our higher level purposes.

57:25And the more we understand that,

57:27the more our behavior makes sense.

57:28But this is all at this point,

57:30complex stories within stories

57:32that are driving our behavior.

57:34- Yeah, I just don't know how big of a leap it is

57:38to start create a system that's able to tell stories

57:41within stories.

57:42Like how big of a leap that is

57:45from where currently Boston Dynamics is,

57:48or any robot that's operating in the physical space.

57:53And that leap might be big

57:56if it requires to solve the hard problem of consciousness,

57:59which is telling a hell of a good story.

58:01- I suspect that consciousness itself is relatively simple.

58:05What's hard is perception,

58:07and the interface between perception and reasoning.

58:09That's for instance, the idea of the consciousness prior

58:14that would be built into such a system by Joshua Bengio.

58:18And what he describes, and I think that's accurate,

58:22is that our own model of the world

58:27can be described through something like an energy function.

58:29The energy function is modeling the contradictions

58:32that exist within the model at any given point.

58:34And you try to minimize these contradictions,

58:36the tangents in the model.

58:38And to do this, you need to sometimes test things.

58:41You need to conditionally disambiguate figure and ground.

58:43You need to distinguish whether this is true

58:46or that is true, and so on.

58:47Eventually you get to an interpretation,

58:49but you will need to manually depress a few points

58:52in your model to let it snap into a state that makes sense.

58:55And this function that tries to get the biggest dip

58:57in the energy function in your model,

58:59according to Joshua Bengio, is related to consciousness.

59:02It's a low dimensional discrete function

59:04that tries to maximize this dip in the energy function.

59:08- Yeah, I think I would need to dig into details

59:13because I think the way he uses the word consciousness

59:15is more akin to self-awareness,

59:17like modeling yourself within the world,

59:20as opposed to the subjective experience, the hard problem.

59:23- No, it's not even the self is in the world.

59:26The self is the agent, and you don't need to be aware

59:28of yourself in order to be conscious.

59:31The self is just a particular content that you can have,

59:34but you don't have to have.

59:35But you can be conscious in, for instance, a dream at night

§08Transcript · 59:39–1:07:52

59:39or during a meditation state, but you don't have a self.

59:42- Right.

59:43- You're just aware of the fact that you are aware.

59:45And what we mean by consciousness in the colloquial sense

59:49is largely this reflexive self-awareness,

59:53that we become aware of the fact

59:55that we are paying attention,

59:57that we are the thing that pays attention.

59:59- We are the thing that pays attention, right.

60:02I don't see where the awareness that we're aware,

60:07the hard problem doesn't feel like it's solved.

60:10I mean, it's called a hard problem for a reason

60:14because it seems like there needs to be a major leap.

60:19- Yeah, I think the major leap is to understand

60:21how it is possible that a machine can dream,

60:25that a physical system is able to create a representation

60:29that the physical system is acting on,

60:31and that is spun force and so on.

60:33But once you accept the fact that you are not in physics,

60:36but that you exist inside of the story,

60:39I think the mystery disappears.

60:40Everything is possible in a story.

60:42- You exist inside the story.

60:43Okay, so the machine--

60:44- Consciousness is being written into the story.

60:45The fact that you experience things

60:47is written to the story.

60:48You ask yourself, is this real what I'm seeing?

60:51And your brain writes into the story, yes, it's real.

60:53- So what about the perception of consciousness?

60:56So to me, you look conscious.

60:59So the illusion of consciousness,

61:02the demonstration of consciousness,

61:04I ask for the legged robot,

61:07how do we make this legged robot conscious?

61:10So there's two things,

61:12and maybe you can tell me if they're neighboring ideas.

61:16One is actually make it conscious,

61:18and the other is make it appear conscious to others.

61:21Are those related?

61:23- Let's ask it from the other direction.

61:27What would it take to make you not conscious?

61:30So when you are thinking about how you perceive the world,

61:35can you decide to switch from looking at qualia

61:39to looking at representational states?

61:42- And it turns out you can.

61:44There is a particular way in which you can look at the world

61:48and recognize its machine nature, including your own.

61:51And in that state, you don't have that conscious experience

61:54in this way anymore.

61:55It becomes apparent as a representation.

61:59Everything becomes opaque.

62:01And I think this thing that you recognize

62:04everything as a representation,

62:05this is typically what we mean with enlightenment states.

62:08And it can happen at the motivational level,

62:11but you can also do this on the experiential level,

62:14on the perceptual level.

62:16- See, but then I can come back to a conscious state.

62:18Okay, I particularly,

62:22I'm referring to the social aspect,

62:26that the demonstration of consciousness

62:30is a really nice thing at a party

62:32when you're trying to meet a new person.

62:34It's a nice thing to know that they're conscious,

62:38and they can, I don't know how fundamental consciousness

62:42is in human interaction,

62:43but it seems like to be at least an important part.

62:48And I ask that in the same kind of way for robots.

62:53You know, in order to create a rich,

62:55compelling human robot interaction,

62:58it feels like there needs to be elements of consciousness

63:00within that interaction.

63:02- And my cat is obviously conscious.

63:04And so my cat can do this party trick.

63:07She also knows that I am conscious,

63:09be able to have feedback about the fact

63:11that we are both acting on models of our own awareness.

63:14- The question is how hard is it for the robot,

63:19artificially created robot,

63:21to achieve cat-level and party tricks?

63:24- Yes, so the issue for me is currently not so much

63:27on how to build a system that creates a story

63:30about a robot that lives in the world,

63:32but to make an adequate representation of the world.

63:36And the model that you and me have is a unified one.

63:40It's one where you basically make sense of everything

63:44that you can perceive.

63:45Every feature in the world that enters your perception

63:47can be relationally mapped

63:49to a unified model of everything.

63:51And we don't have an AI that is able to construct

63:54such a unified model yet.

63:55- So you need that unified model to do the party trick?

63:58- Yes, I think that it doesn't make sense

64:01if this thing is conscious,

64:03but not in the same universe as you,

64:04because you could not relate to each other.

64:06- So what's the process, would you say,

64:09of engineering consciousness in a machine?

64:12Like, what are the ideas here?

64:14- So you probably want to have

64:16some kind of perceptual system.

64:19This perceptual system is a processing agent

64:21that is able to track sensory data

64:23and predict the next frame and the sensory data

64:26from the previous frames of the sensory data

64:29and the current state of the system.

64:31So the current state of the system is,

64:33in perception, instrumental

64:35to predicting what happens next.

64:37And this means you build lots and lots of functions

64:39that take all the blips that you feel on your skin

64:42and that you see on your retina, or that you hear,

64:45and puts them into a set of relationships

64:48that allows you to predict what kind of sensory data,

64:51what kind of sensor of blips,

64:52vector of blips you're going to perceive

64:54in the next frame, right?

64:56This is tuned, and it's constantly tuned

64:59until it gets as accurate as it can.

65:01- You build a very accurate prediction mechanism

65:05that is step one of the perception.

65:08So first you predict, then you perceive

65:09and see the error in your prediction.

65:11- And you have to do two things to make that happen.

65:13One is you have to build a network of relationships

65:16that are constraints,

65:18that take all the variants in the world,

65:21put each of the variances into a variable

65:24that is connected with relationships to other variables.

65:27And these relationships are computable functions

65:30that constrain each other.

65:31So when you see a nose

65:32that points at a certain direction in space,

65:34you have a constraint that says

65:36there should be a face nearby that has the same direction.

65:39And if that is not the case,

65:40you have some kind of contradiction

65:41that you need to resolve

65:42because it's probably not a nose what you're looking at.

65:44It just looks like one.

65:45So you have to reinterpret the data

65:48until you get to a point where your model converges.

65:52And this process of making the sensory data

65:54fit into your model structure

65:56is what Piaget calls the assimilation.

66:01And accommodation is the change of the models

66:04where you change your model in such a way

66:05that you can assimilate everything.

66:08- So you're talking about building

66:09a hell of an awesome perception system

66:12that's able to do prediction and perception correct

66:15and keep improving.

66:15- No, wait, just figure it out.

66:17- Wait, there's more.

66:18- Yes, there's more.

66:19So the first thing that we wanted to do

66:21is we want to minimize the contradictions in the model.

66:24And of course, it's very easy to make a model

66:26in which you minimize the contradictions

66:28just by allowing that it can be

66:29in many, many possible states, right?

66:31So if you increase degrees of freedom,

66:34you will have fewer contradictions.

66:35But you also want to reduce the degrees of freedom

66:37because degrees of freedom mean uncertainty.

66:40You want your model to reduce uncertainty

66:42as much as possible.

66:44But reducing uncertainty is expensive.

66:46So you have to have a trade-off

66:47between minimizing contradictions and reducing uncertainty.

66:52And you have only a finite amount of compute

66:54and experimental time and effort available

66:57to reduce uncertainty in the world.

66:59So you need to assign value to what you observe.

67:02So you need some kind of motivational system

67:05that is estimating what you should be looking at

67:07and what you should be thinking about it,

67:09how you should be applying your resources

67:10to model what that is, right?

67:13So you need to have something like convergence links

67:16that tell you how to get from the present state of the model

67:18to the next one.

67:19You need to have these compatibility links

67:20that tell you which constraints exist

67:23and which constraint violations exist.

67:25And you need to have some kind of motivational system

67:29that tells you what to pay attention to.

67:30So now we have a second agent next to the perceptual agent.

67:33We have a motivational agent.

67:34This is a cybernetic system

67:36that is modeling what the system needs,

67:38what's important for the system,

67:40and that interacts with the perceptual system

67:42to maximize the expected reward.

67:44- And you're saying the motivational system

67:46is some kind of, like,

67:49what is it, a higher level narrative over some lower level?

67:52- No, it's just your brainstem stuff,

§09Transcript · 1:07:54–1:16:35

67:54the limbic system stuff that tells you,

67:55okay, now you should get something to eat

67:57because I've just measured your blood sugar.

67:59- So you mean like motivational system,

68:00like the lower level stuff, like hungry?

68:03- Yes, there's basically physiological needs

68:05and some cognitive needs and some social needs,

68:07and they all interact.

68:08And they all implemented different parts

68:10in your nervous system as the motivational system.

68:12But they're basically cybernetic feedback loops.

68:14It's not that complicated.

68:16It's just a lot of code.

68:18And so you now have a motivational agent

68:21that makes your robot go for the ball,

68:23or that makes your worm go to eat food and so on.

68:27And you have the perceptual system

68:29that lets it predict the environment,

68:30so it's able to solve that control problem to some degree.

68:33And now what we learned is that it's very hard

68:35to build a machine learning system

68:37that looks at all the data simultaneously

68:39to see what kind of relationships could exist between them.

68:43So you need to selectively model the world.

68:45You need to figure out,

68:46where can I make the biggest difference

68:48if I would put the following things together?

68:51Sometimes you find a gradient for that, right?

68:53When you have a gradient,

68:54you don't need to remember where you came from.

68:56You just follow the gradient

68:57until it doesn't get any better.

68:59But if you have a world where the problems are discontinuous

69:02and the search spaces are discontinuous,

69:04you need to retain memory of what you explored.

69:07You need to construct a plan of what to explore next.

69:10And this thing means that you have,

69:13next to this perceptual construction system

69:15and the motivational cybernetics,

69:17an agent that is paying attention

69:20to what it should select at any given moment

69:22to maximize reward.

69:24And this scanning system,

69:25this attention agent is required for consciousness

69:28and consciousness is its control model.

69:31So it's the index memories that this thing retains

69:36when it manipulates the perceptual representations

69:39to maximize the value and minimize the conflicts

69:43and to increase coherence.

69:44So the purpose of consciousness is to create coherence

69:47in your perceptual representations,

69:49remove conflicts, predict the future,

69:52construct counterfactual representations

69:54so you can coordinate your actions and so on.

69:56And in order to do this, it needs to form memories.

70:00These memories are partial binding states

70:02of the working memory contents

70:04that are being revisited later on to backtrack,

70:07to undo certain states, to look for alternatives.

70:10And these index memories that you can recall,

70:13that is what you perceive as your stream of consciousness.

70:15And being able to recall these memories,

70:17this is what makes you conscious.

70:19If you could not remember what you paid attention to,

70:21you wouldn't be conscious.

70:22- So consciousness is the index in the memory database.

70:29Okay.

70:30But let me sneak up to the questions of consciousness

70:35a little further.

70:37So we usually relate suffering to consciousness.

70:42So the capacity to suffer.

70:44I think to me, that's a really strong sign of consciousness,

70:49is a thing that can suffer.

70:51How is that useful?

70:54Suffering.

70:55And like in your model, what you just described,

70:59which is indexing of memories,

71:01and what is the coherence with the perception,

71:05with this predictive thing that's going on in the perception,

71:09how does suffering relate to any of that?

71:12You know, the higher level suffering that humans do?

71:15- Basically pain is a reinforcement signal.

71:20Pain is a signal that one part of your brain

71:23sends to another part of your brain,

71:25or in an abstract sense, part of your mind

71:27sends to another part of the mind to regulate its behavior,

71:30to tell it the behavior that you're currently exhibiting

71:33should be improved.

71:34And this is the signal that I tell you

71:37to move away from what you're currently doing

71:40and push into a different direction.

71:42So pain gives part of you an impulse

71:46to do something differently.

71:47But sometimes this doesn't work,

71:49because the training part of your brain

71:52is talking to the wrong region,

71:54or because it has the wrong model

71:55of the relationships in the world.

71:57Maybe you're mismodeling yourself,

71:58or you're mismodeling the relationship

72:00of yourself to the world,

72:01or you're mismodeling the dynamics of the world.

72:03So you're trying to improve something

72:04that cannot be improved by generating more pain.

72:07But the system doesn't have any alternative.

72:10So it doesn't get better.

72:12What do you do if something doesn't get better,

72:14and you want it to get better?

72:15You increase the strength of the signal.

72:17And when the signal becomes chronic,

72:19when it becomes permanent,

72:20without a change inside, this is what we call suffering.

72:24And the purpose of consciousness

72:26is to deal with contradictions,

72:28with things that cannot be resolved.

72:30The purpose of consciousness, I think,

72:32is similar to a conductor in an orchestra.

72:35When everything works well,

72:36the orchestra doesn't need much of a conductor,

72:38as long as it's coherent.

72:40But when there is a lack of coherence,

72:42or something is consistently producing disharmony

72:45and mismatches, then the conductor becomes alert

72:48and interacts with it.

72:49So suffering attracts the activity of our consciousness.

72:52And the purpose of that is ideally

72:54that we bring new layers online,

72:56new layers of modeling that are able

72:59to create a model of the dysregulation,

73:02so we can deal with it.

73:04And this means that we typically get

73:06higher level consciousness, so to speak.

73:08We get some consciousness above our pay grade, maybe,

73:11if we have some suffering early in our life.

73:13Most of the interesting people

73:14had trauma early on in their childhood.

73:17And trauma means that you are suffering an injury

73:20for which the system is not prepared,

73:23which it cannot deal with,

73:24which it cannot insulate itself from.

73:26So something breaks.

73:27And this means that the behavior of the system

73:29is permanently disturbed

73:33in a way that some mismatch exists now in the regulation,

73:37that just by following your impulses,

73:39by following the pain in the direction which it hurts,

73:41the situation doesn't improve, but get worse.

73:44And so what needs to happen is that you grow up.

73:46And that part that has grown up is able to deal

73:50with the part that is stuck in this earlier phase.

73:53- Yeah, so it leads to growth,

73:54you're adding extra layers to your cognition.

73:58Let me ask you then, 'cause I gotta stick on suffering,

74:02the ethics of the whole thing.

74:03So not our consciousness, but the consciousness of others.

74:08You've tweeted, "One of my biggest fears

74:13is that insects could be conscious.

74:16The amount of suffering on earth would be unthinkable."

74:19So when we think of other conscious beings,

74:24is suffering a property of consciousness

74:30that we're most concerned about?

74:32So I'm still thinking about robots,

74:37how to make sense of other non-human things

74:43that appear to have the depth of experience

74:48that humans have.

74:49And to me, that means consciousness

74:53and the darkest side of that, which is suffering,

74:57the capacity to suffer.

75:00And so I started thinking, how much responsibility

75:03do we have for those other conscious beings?

75:06That's where the definition of consciousness

75:10becomes most urgent.

75:13Like having to come up with a definition of consciousness

75:15becomes most urgent, is who should we,

75:19and should we not be torturing?

75:21- There's no general answer to this.

75:26Was Genghis Khan doing anything wrong?

75:29It depends on how you look at it.

75:31- Well, he drew a line somewhere

75:35where this is us and that's them.

75:38It's the circle of empathy.

75:40It's like these, you don't have to use the word consciousness

75:44but these are the things that matter to me

75:48if they suffer or not.

75:50And these are the things that don't matter to me.

75:52- Yeah, but when one of his commanders failed him,

75:54he broke his spine and let him die in a horrible way.

75:59And so in some sense, I think he was indifferent

76:02to suffering or he was not indifferent in the sense

76:05that he didn't see it as useful if he inflicted suffering,

76:09but he did not see it as something that had to be avoided.

76:14That was not the goal.

76:15The question was, how can I use suffering

76:18and the infliction of suffering to reach my goals

76:21from his perspective?

76:23- I see, so like different societies throughout history

76:26put different value on the--

76:29- Different individuals, different psyches.

76:31- But also even the objective of avoiding suffering.

76:35Like some societies probably,

§10Transcript · 1:16:37–1:25:29

76:37I mean, this is where like religious belief really helps

76:40that afterlife, that doesn't matter that you suffer or die,

76:45what matters is you suffer honorably, right?

76:49So that you enter the afterlife.

76:52- It seems to be superstitious to me,

76:53basically beliefs that assert things

76:57for which no evidence exists,

76:59are incompatible with sound epistemology.

77:02And I don't think that religion has to be superstitious,

77:04otherwise it should be condemned in all cases.

77:06- You're somebody who's saying we live in a dream world,

77:09we have zero evidence for anything.

77:11- So it's not the case.

77:12There are limits to what languages can be constructed.

77:16Mathematics breaks solid evidence for its own structure.

77:19And once we have some idea of what languages exist

77:23and how a system can learn

77:24and what learning itself is in the first place,

77:26and so on, we can begin to realize that our intuitions

77:31that we are able to learn about the regularities

77:34of the world and minimize appraisal

77:36and understand the nature of our own agency

77:38to some degree of abstraction.

77:40That's not an illusion.

77:42It's a useful approximation.

77:44- Just because we live in a dream world

77:46doesn't mean mathematics can't give us a consistent glimpse

77:51of physical, of objective reality.

77:54- We can basically distinguish useful encodings

77:57from useless encodings.

77:59And when we apply our truth-seeking to the world,

78:03we know we usually cannot find out

78:05whether a certain thing is true.

78:07What we typically do is we take the state vector

78:10of the universe, separate it into separate objects

78:12that interact with each other, so interfaces.

78:14And this distinction that we are making

78:16is not completely arbitrary.

78:17It's done to optimize the compression

78:21that we can apply to our models of the universe.

78:23So we can predict what's happening

78:25with our limited resources.

78:27In this sense, it's not arbitrary.

78:29But the separation of the world into objects

78:32that are somehow discrete and interacting with each other

78:34is not the true reality, right?

78:36The boundaries between the objects

78:38are projected into the world, not arbitrarily projected,

78:41but still, it's only an approximation

78:44of what's actually the case.

78:46And we sometimes notice that we run into contradictions

78:49when we try to understand high-level things

78:51like economic aspects of the world and so on,

78:53or political aspects or psychological aspects

78:57where we make simplifications.

78:58And the objects that we are using to separate the world

79:00are just one of many possible projections

79:03of what's going on.

79:04And so it's not in this postmodernist sense

79:07completely arbitrary and you're free to pick

79:09what you want or dismiss what you don't like

79:11because it's all stories.

79:12No, that's not true.

79:13You have to show for every model

79:15of how well it predicts the world.

79:17So the confidence that you should have

79:19in the entities of your models

79:21should correspond to the evidence that you have.

79:23- Can I ask you on a small tangent

79:26to talk about your favorite set of ideas and people,

79:32which is postmodernism?

79:35- What? (laughs)

79:38- What is postmodernism?

79:40How would you define it?

79:41And why to you is it not a useful framework of thought?

79:46- Postmodernism is something

79:51that I'm really not an expert on.

79:53And postmodernism is a set of philosophical ideas

79:57that is difficult to lump together,

79:59that is characterized by some useful thinkers,

80:04some of them poststructuralist and so on.

80:06And I'm mostly not interested in it

80:08because I think that it's not leading me anywhere

80:11that I find particularly useful.

80:14It's mostly, I think, born out of the insight

80:16that the ontologies that we impose on the world

80:20are not literally true,

80:21and that we can often get to a different interpretation

80:23by the world by using a different ontology

80:25that is different separation of the world

80:27into interacting objects.

80:29But the idea that this makes the world

80:32and a set of stories that are arbitrary, I think is wrong.

80:36And the people that are engaging in this type of philosophy

80:40are working in an area

80:42that I largely don't find productive.

80:44There's nothing useful coming out of this.

80:46So this idea that truth is relative

80:48is not something that has in some sense,

80:50informed physics or theory of relativity.

80:52And there is no feedback between those.

80:54There is no meaningful influence

80:57of this type of philosophy on the sciences

80:59or in engineering or in politics.

81:02But there is a very strong information of this on ideology,

81:07because it basically has become an ideology

81:10that is justifying itself by the notion

81:14that truth is a relative concept.

81:16And it's not being used in such a way

81:18that the philosophers or sociologists

81:21that take up these ideas say,

81:23oh, I should doubt my own ideas

81:25because maybe my separation of the world into objects

81:27is not completely valid,

81:28and I should maybe use a different one

81:30and be open to a pluralism of ideas.

81:33But it mostly exists to dismiss the ideas of other people.

81:37- It becomes, yeah, it becomes a political weapon of sorts.

81:40- Yes. - To achieve power.

81:42- Basically, there's nothing wrong, I think,

81:45with developing a philosophy around this,

81:48but to develop norms around the idea

81:51that truth is something that is completely negotiable

81:55is incompatible with the scientific project.

81:58And I think if the academia has no defense

82:02against the ideological parts

82:04of the postmodernist movement, it's doomed.

82:09- Right, you have to acknowledge

82:10the ideological part of any movement, actually,

82:13including postmodernism.

82:15- Well, the question is what an ideology is.

82:17And to me, an ideology is basically a viral memeplex

82:21that is changing your mind in such a way

82:24that reality gets warped.

82:26It gets warped in such a way that you're being cut off

82:28from the rest of human thought space,

82:29and you cannot consider things outside

82:32of the range of ideas of your own ideology

82:35as possibly true.

82:35- Right, so, I mean, there's certain properties

82:37to an ideology that make it harmful.

82:39One of them is that dogmatism of just certainty,

82:44dogged certainty in that you're right,

82:46you have the truth, and nobody else does.

82:48- Yeah, but what is creating the certainty?

82:50It's very interesting to look at the type of model

82:53that is being produced.

82:54Is it basically just a strong prior?

82:56And you tell people, oh, this idea

82:58that you consider to be very true,

83:00the evidence for this is actually just much weaker

83:02than you thought, and look here at some studies.

83:04No, this is not how it works.

83:06It's usually normative, which means some thoughts

83:09are unthinkable because they would change your identity

83:13into something that is no longer acceptable.

83:16And this cuts you off from considering an alternative,

83:20and many de facto religions use this trick

83:23to lock people into a certain mode of thought,

83:25and this removes agency over your own thoughts,

83:27and it's very ugly to me.

83:28It's basically not just a process of domestication,

83:32but it's actually an intellectual castration that happens.

83:36It's an inability to think creatively

83:39and to bring forth new thoughts.

83:40- Can I ask you about substances, chemical substances

83:48that affect the video game, the dream world?

83:53So psychedelics that increasingly have been getting

83:57a lot of research done on them.

83:58So in general, psychedelics, psilocybin, MDMA,

84:02but also a really interesting one, the big one,

84:05which is DMT.

84:06What and where are the places that these substances take

84:12the mind that is operating in the dream world?

84:15Do you have an interesting sense how this throws a wrinkle

84:20into the prediction model?

84:22Is it just some weird little quirk,

84:24or is there some fundamental expansion

84:27of the mind going on?

84:28- I suspect that a way to look at psychedelics

84:34is that they induce particular types

84:36of lucid dreaming states.

84:38So it's a state in which certain connections

84:41are being severed in your mind, are no longer active.

84:45Your mind basically gets free to move in a certain direction

84:48because some inhibition, some particular inhibition

84:51doesn't work anymore.

84:52And as a result, you might stop having a self,

84:55or you might stop perceiving the world as three-dimensional.

85:00And you can explore that state.

85:04And I suppose that for every state that can be induced

85:07with psychedelics, there are people that are naturally

85:09in that state.

85:11So sometimes psychedelics shift you through a range

85:14of possible mental states, and they can also shift you

85:17out of the range of permissible mental states,

85:19that is where you can make predictive models of reality.

85:22And what I observe in people that use psychedelics a lot

85:27is that they tend to be overfitting.

85:29Overfitting means that you are using more bits

§11Transcript · 1:25:34–1:34:07

85:34for modeling the dynamics of a function than you should.

85:38And so you can fit your curve to extremely detailed things

85:41in the past, but this model is no longer predictive

85:44for the future.

85:45- What is it about psychedelics that forces that?

85:48I thought it would be the opposite.

85:51I thought that it's a good mechanism

85:54for generalization, for regularization.

85:59So it feels like psychedelics expansion of the mind,

86:03like taking you outside of, like forcing your model

86:06to be non-predictive is a good thing.

86:09Meaning like, it's almost like, okay,

86:14what I would say is psychedelics are akin to is traveling

86:17to a totally different environment.

86:19Like going, if you've never been to like India

86:22or something like that from the United States,

86:24very different set of people, different culture,

86:26different food, different roads and values

86:30and all those kinds of things.

86:31- Yeah, so psychedelics can, for instance,

86:33teleport people into a universe that is hyperbolic,

86:37which means that if you imagine a room that you're in,

86:41you can turn around 360 degrees

86:43and you didn't go full circle.

86:44You need to go 720 degrees to go full circle.

86:47- Exactly.

86:48- So the things that people learn in that state

86:50cannot be easily transferred in this universe

86:53that we are in.

86:54It could be that if they're able to abstract

86:56and understand what happened to them,

86:58that they understand that some part

87:00of their spatial cognition has been desynchronized

87:03and has found a different synchronization.

87:05And this different synchronization

87:06happens to be a hyperbolic one, right?

87:08So you learn something interesting about your brain.

87:11It's difficult to understand what exactly happened,

87:13but we get a pretty good idea once we understand

87:15how the brain is representing geometry.

87:17- Yeah, but doesn't give you a fresh perspective

87:20on the physical reality?

87:21(knocking)

87:24Who's making that sound?

87:27Is it inside my head or is it external?

87:30- Well, there is no sound outside of your mind,

87:33but it's making sense of phenomenon physics.

87:37(laughing)

87:39- Yeah, in the physical reality, there's sound waves

87:42traveling through air.

87:45Okay.

87:47- That's our model of what's happened.

87:48- That's our model of what happened, right.

87:51So, don't psychedelics give you a fresh perspective

87:56on this physical reality?

87:58Not this physical reality, but this more,

88:03what do you call the dream world?

88:08That's mapped directly to--

88:09- The purpose of dreaming at night, I think,

88:11is data augmentation.

88:13- Well, exactly.

88:14So that's very different.

88:16That's very similar to psychedelics.

88:17- So you basically change parameters

88:19about the things that you have learned.

88:21And for instance, when you are young,

88:24you have seen things from certain perspectives,

88:26but not from others.

88:27So your brain is generating new perspectives

88:29of objects that you already know,

88:31which means they can learn to recognize them later

88:34from different perspectives.

88:35And I suspect that's the reason that many of us

88:37remember to have flying dreams as children,

88:39because it's just different perspectives

88:41of the world that you already know.

88:43And that it starts to generate

88:45these different perspective changes,

88:47and then it fluidly turns this into a flying dream

88:50to make sense of what's happening, right?

88:52So you fill in the gaps,

88:53and suddenly you see yourself flying.

88:55And similar things can happen with semantic relationships.

88:58So it's not just spatial relationships,

89:00but it can also be the relationships

89:02between ideas that are being changed.

89:05And it seems that the mechanisms

89:06that make that happen during dreaming

89:09are interacting with these same receptors

89:14that are being simulated by psychedelics.

89:17So I suspect that there is a thing

89:19that I haven't read really about,

89:22the way in which dreams are induced in the brain.

89:24It's not just that the activity of the brain

89:27gets tuned down because your eyes are closed

89:30and you no longer get enough data from your eyes,

89:33but there is a particular type of neurotransmitter

89:37that is saturating your brain during these phases,

89:40during the RM phases,

89:41and you produce controlled hallucinations.

89:44And psychedelics are linking into these mechanisms,

89:48I suspect.

89:49- So isn't that another trickier form of data augmentation?

89:54- Yes, but it's also data augmentation

89:57that can happen outside of the specification

89:59that your brain is tuned to.

90:00So basically people are overclocking their brains,

90:03and that produces states

90:05that are subjectively extremely interesting.

90:09- Yeah, I just-

90:10- But from the outside, very suspicious.

90:12- So I think I'm over applying the metaphor

90:15of a neural network in my own mind,

90:17which I just think that doesn't lead to overfitting, right?

90:22But you were just sort of anecdotally saying

90:26my experiences with people that have done psychedelics

90:28are that kind of quality.

90:30- I think it typically happens.

90:31So if you look at people like Timothy Leary,

90:34and he has written beautiful manifestos

90:36about the effect of LSD on people.

90:40He genuinely believed, he writes in his manifestos,

90:42that in the future, science and art

90:44will only be done on psychedelics

90:46because it's so much more efficient and so much better.

90:49And he gave LSD to children in this community

90:52of a few thousand people that he had near San Francisco.

90:55And basically he was losing touch with reality.

91:00He did not understand the effects,

91:02the things that he was doing would have

91:04on the reception of psychedelics by society,

91:07because he was unable to think critically

91:09about what happened.

91:10What happened was that he got in a euphoric state.

91:13That euphoric state happened because he was overfitting.

91:16He was taking this sense of euphoria

91:19and translating it into a model

91:21of actual success in the world, right?

91:23He was feeling better.

91:25Limitations had disappeared,

91:26that he experienced to be existing,

91:29but he didn't get superpowers.

91:30- I understand what you mean by overfitting now.

91:33There's a lot of interpretation

91:35to the term overfitting in this case, but I got you.

91:38So he was getting positive rewards

91:42from a lot of actions that he shouldn't have been doing.

91:44- But not just this.

91:45So if you take, for instance, John Lilly,

91:46who was studying dolphin languages and aliens and so on,

91:51a lot of people that use psychedelics became very loopy.

91:54And the typical thing that you notice

91:58when people are on psychedelics is that they are in a state

92:00where they feel that everything can be explained now.

92:03Everything is clear.

92:04Everything is obvious.

92:06And sometimes they have indeed discovered

92:09a useful connection, but not always.

92:12Very often these connections are over-interpretations.

92:15- I wonder, you know, there's a question

92:17of correlation versus causation.

92:21And also I wonder if it's the psychedelics

92:23or if it's more the social, like being the outsider

92:27and having a strong community of outside

92:31and having a leadership position

92:32in an outsider cult-like community,

92:35that could have a much stronger effect of overfitting

92:38than do psychedelics themselves, the actual substances,

92:41because it's a counterculture thing.

92:43So it could be that as opposed to the actual substance.

92:46If you're a boring person who wears a suit and tie

92:49and works at a bank and takes psychedelics,

92:53that could be a very different effect

92:55of psychedelics on your mind.

92:57I'm just sort of raising the point

92:59that the people you referenced are already weirdos.

93:02I'm not sure exactly.

93:04- No, not necessarily.

93:05A lot of the people that tell me

93:07that they use psychedelics in a useful way

93:10started out as squares and were liberating themselves

93:14because they were stuck.

93:16They were basically stuck in local optimum

93:17of their own self-model, of their relationship to the world.

93:20And suddenly they had data augmentation.

93:23They basically saw and experienced a space of possibilities.

93:26They experienced what it would be like to be another person.

93:29And they took important lessons

93:32from that experience back home.

93:34(inhales deeply)

93:36- Yeah.

93:37I mean, I love the metaphor of data augmentation

93:40because that's been the primary driver

93:44of self-supervised learning in the computer vision domain

93:48is data augmentation.

93:50So it's funny to think of data augment,

93:53like chemically induced data augmentation in the human mind.

93:58- There's also a very interesting effect that I noticed.

94:03I know several people who are severe to me

94:07that LSD has cured their migraines.

§12Transcript · 1:34:10–1:42:46

94:10So severe cluster eight headaches or migraines

94:14that didn't respond to standard medication

94:16that disappeared after a single dose.

94:19And I don't recommend anybody doing this,

94:21especially not in the US where it's illegal.

94:24And there are no studies on this for that reason.

94:27But it seems that anecdotally

94:29that it basically can reset the serotonergic system.

94:34So it's basically pushing them

94:37outside of their normal boundaries.

94:39And as a result, it needs to find a new equilibrium.

94:41And in some people, that equilibrium is better.

94:44But it also follows that in other people, it might be worse.

94:47So if you have a brain that is already teetering

94:50on the boundary to psychosis,

94:52it can be permanently pushed over that boundary.

94:55- Well, that's why you have to do good science,

94:57which they're starting to do

94:58on all these different substances

94:59of how well it actually works for the different conditions

95:01like MDMA seems to help with PTSD, same with psilocybin.

95:06That you need to do good science,

95:09meaning large studies of large N.

95:11- Yeah, so based on the existing studies with MDMA,

95:14it seems that if you look at Rick Doblin's work

95:18and what he has published about this and talks about,

95:21MDMA seems to be a psychologically relatively safe drug,

95:24but it's physiologically not very safe.

95:26That is, there is neurotoxicity

95:30if you would use too large dose.

95:31And if you combine this with alcohol,

95:34which a lot of kids do in party settings during raves

95:37and so on, it's very hepatotoxic.

95:40So basically you can kill your liver.

95:42And this means that it's probably something that is best

95:45and most productively used in a clinical setting

95:48by people who really know what they're doing.

95:50And I suspect that's also true for the other psychedelics.

95:53That is, while the other psychedelics

95:56are probably not as toxic as say alcohol,

95:59the effects on Nisaki can be much more profound and lasting.

96:03- Yeah, well, as far as I know, psilocybin,

96:06so mushrooms, magic mushrooms,

96:08as far as I know in terms of the studies they're running,

96:11I think have no, like they're allowed to do

96:15what they're calling heroic doses.

96:17So that one does not have a toxicity.

96:19So they could do like huge doses in a clinical setting

96:21when they're doing study on psilocybin,

96:23which is kind of fun.

96:25- Yeah, it seems that most of the psychedelics

96:27work in extremely small doses,

96:29which means that the effect on the rest of the body

96:32is relatively low.

96:33And MDMA is probably the exception.

96:36Maybe ketamine can be dangerous in larger doses

96:38because it can depress breathing and so on.

96:41But the LSD and psilocybin work in very, very small doses,

96:46at least the active part of them,

96:47of psilocybin and LSD is only the active part.

96:50And the, but the effect that it can have

96:54on your mental wiring can be very dangerous, I think.

96:57- Let's talk about AI a little bit.

97:00What are your thoughts about GPT-3 and language models

97:05trained with self-supervised learning?

97:07It came out quite a bit ago,

97:11but I wanted to get your thoughts on it.

97:13- Yeah.

97:14In the '90s, I was in New Zealand

97:16and I had an amazing professor, Ian Witton,

97:21who realized I was bored in class and put me in his lab.

97:25And he gave me the task to discover grammatical structure

97:28in an unknown language.

97:30And the unknown language that I picked was English

97:33because it was the easiest one to find,

97:36Corpus 4, construct one.

97:38And he gave me the largest computer at the whole university.

97:42It had two gigabytes of RAM, which was amazing.

97:44And I wrote everything in C

97:45with some in-memory compression to do statistics

97:47over the language.

97:49And I first would create a dictionary of all the words,

97:53which basically tokenizes everything and compresses things

97:57so that I don't need to store the whole word,

97:58but just a code for every word.

98:02And then I was taking this all apart in sentences

98:05and I was trying to find all the relationships

98:09between all the words in the sentences

98:10and do statistics over them.

98:12And that proved to be impossible

98:15because the complexity is just too large.

98:18So if you want to discover the relationship

98:20between an article and a noun,

98:21and there are three adjectives in between,

98:23you cannot do N-gram statistics

98:25and look at all the possibilities that can exist,

98:28at least not with the resources that we had back then.

98:30So I realized I need to make some statistics

98:33over what I need to make statistics over.

98:35So I wrote something that was pretty much a hack

98:38that did this for at least first order relationships.

98:42And I came up with some kind of mutual information graph

98:45that was indeed discovering something

98:47that looks exactly like the grammatical structure

98:49of the sentence, just by trying to encode the sentence

98:52in such a way that the words would be written

98:54in the optimal order inside of the model.

98:58And what I also found is that if we would be able

99:02to increase the resolution of that

99:03and not just use this model

99:06to reproduce grammatically correct sentences,

99:09we would also be able to correct

99:10stylistically correct sentences

99:11by just having more bits in these relationships.

99:14And if we wanted to have meaning,

99:16we would have to go much higher order.

99:18And I didn't know how to make higher order models back then

99:21without spending way more years in research

99:23on how to make the statistics

99:25over what we need to make statistics over.

99:27And this thing that we cannot look at the relationships

99:31between all the bits in your input

99:33is being solved in different domains in different ways.

99:35So in computer graphics, the computer vision,

99:39standard methods for many years now

99:41is convolutional neural networks.

99:43Convolutional neural networks are hierarchies of filters

99:46that exploit the fact that neighboring pixels in images

99:49are usually semantically related

99:51and distance pixels in images

99:53are usually not semantically related.

99:55So you can just by grouping the pixels

99:57that are next to each other hierarchically together,

100:00reconstruct the shape of objects.

100:02And this is an important prior

100:04that we built into these models

100:06so they can converge quickly.

100:08But this doesn't work in language

100:09for the reason that adjacent words are often

100:12but not always related

100:14and distant words are sometimes related

100:16while the words in between are not.

100:18So how can you learn the topology of language?

100:22And I think for this reason that this difficulty existed,

100:26the transformer was invented

100:28in natural language processing, not in vision.

100:32And what the transformer is doing,

100:34it's a hierarchy of layers

100:36where every layer learns what to pay attention to

100:39in the given context in the previous layer.

100:42So what to make the statistics over.

100:44- And the context is significantly larger

100:49than the adjacent word.

100:51- Yes.

100:51So the context that GPT-3 has been using,

100:55the transformer itself is from 2017

100:58and it wasn't using that large of a context.

101:01OpenAI has basically scaled up this idea

101:04as far as they could at the time.

101:06And the context is about 2048 symbols,

101:10tokens in the language.

101:12These symbols are not characters,

101:15but they take the words and project them

101:17into a vector space where words

101:20that are statistically co-occurring a lot

101:22are neighbors already.

101:23So it's already a simplification

101:24of the problem a little bit.

101:26And so every word is basically a set of coordinates

101:29in a high dimensional space.

101:31And then they use some kind of trick

101:33to also encode the order of the words in a sentence

101:36or in the not just sentence,

101:37but 2048 tokens is about a couple pages of text

101:41or two and a half pages of text.

101:43And so they managed to do pretty exhaustive statistics

101:46over the potential relationships

101:49between two pages of text, which is tremendous, right?

101:51I was just using a single sentence back then

101:55and I was only looking for first order relationships

101:58and they were really looking for

102:01much, much higher level relationships.

102:02And what they discover after they fed this

102:05with an enormous amount of training data,

102:07pretty much the written internet

102:09or a subset of it that had some quality,

102:12but substantial portion of the common crawl

102:15that they're not only able to reproduce style,

102:18but they're also able to reproduce

102:19some pretty detailed semantics,

102:21like being able to add three digit numbers

102:24and multiply two digit numbers

102:26or to translate between pro-Eng languages

102:28and things like that.

102:30So the results that GPT-3 got, I think were amazing.

102:34- By the way, I actually didn't check carefully.

102:38It's funny you just mentioned

102:40how you coupled semantics to the multiplication.

102:42Is it able to do some basic math on two digit numbers?

102:46- Yes.

§13Transcript · 1:42:47–1:51:46

102:47- Okay, interesting.

102:48I thought there's a lot of failure cases.

102:53- Yeah, it basically fails if you take larger digit numbers.

102:56So four digit numbers and so on

102:58makes carrying mistakes and so on.

103:00And if you take larger numbers,

103:02you don't get useful results at all.

103:05And this could be an issue of the training set,

103:09where there are not many examples

103:10of successful long form addition

103:13and standard human written text.

103:15- And humans aren't very good

103:16at doing three digit numbers either.

103:19- Yeah, and you're not writing a lot about it.

103:22And the other thing is that the loss function

103:24that is being used is only minimizing surprises.

103:27So it's predicting what comes next in a typical text.

103:29It's not trying to go for causal closure first as we do.

103:33- Yeah.

103:34- But the fact that that kind of prediction works

103:39to generate text that's semantically rich

103:42and consistent is interesting.

103:45- Yeah.

103:45- So yeah, so it's amazing that it's able

103:47to generate semantically consistent text.

103:50- It's not consistent.

103:51So the problem is that it loses coherence at some point.

103:54But it's also, I think, not correct to say

103:57that GPT-3 is unable to deal with semantics at all,

104:01because you ask it to perform certain transformations

104:04in text and it performs these transformation in text.

104:07And the kind of additions that it's able

104:09to perform are transformations in text, right?

104:12And there are proper semantics involved.

104:15You can also do more.

104:16There was a paper that was generating lots and lots

104:20of mathematically correct text

104:24and was feeding this into a transformer.

104:26And as a result, it was able to learn how

104:29to do differentiation integration in race

104:32that according to the authors, Mathematica could not.

104:35To which some of the people in Mathematica responded

104:39that they were not using Mathematica in the right way

104:42and so on.

104:43I have not really followed the resolution of this conflict.

104:46- This part, as a small tangent,

104:48I really don't like in machine learning papers,

104:51which they often do anecdotal evidence.

104:56They'll find like one example in some kind

104:58of specific use of Mathematica and demonstrate,

105:01look, here's, they'll show successes and failures,

105:04but they won't have a very clear representation

105:07of how many cases this actually represents.

105:09- Yes, but I think as a first paper,

105:11this is a pretty good start.

105:12And so the take home message, I think,

105:15is that the authors could get better results from this

105:19and their experiments than they could get from the vein,

105:23which they were using computer algebra systems,

105:25which means that was not nothing.

105:29And it's able to perform substantially better

105:32than GPT-3 can based on a much larger amount

105:35of training data using the same underlying algorithm.

105:38- Well, let me ask again.

105:41So I'm using your tweets as if this is like Plato, right?

105:44(both laughing)

105:47As if this is well thought out novels that you've written.

105:51You tweeted, "GPT-4 is listening to us now."

105:58This is one way of asking,

106:00what are the limitations of GPT-3 when it scales?

106:04So what do you think will be the capabilities of GPT-4,

106:07GPT-5, and so on?

106:10What are the limits of this approach?

106:11- So obviously when we are writing things right now,

106:15everything that we are writing now

106:16is going to be training data

106:18for the next generation of machine learning models.

106:20So yes, of course, GPT-4 is listening to us.

106:23And I think the tweet is already a little bit older

106:25and we now have WUDAO and we have a number of other systems

106:30that basically are placeholders for GPT-4.

106:33Don't know what OpenAI's plans are in this regard.

106:35- I read that tweet in several ways.

106:39So one is obviously everything you put on the internet

106:42is used as training data.

106:44But in a second way, I read it is in a,

106:49we talked about agency.

106:51I read it as almost like GPT-4 is intelligent enough

106:55to be choosing to listen.

106:58So not only did a programmer tell it to collect this data

107:02and use it for training,

107:03I almost saw the humorous angle,

107:06which is like it has achieved AGI kind of thing.

107:09- Well, the thing is,

107:10could we be already be living in GPT-5?

107:13(both laughing)

107:15- So GPT-4 is listening and GPT-5 actually constructing

107:18the entirety of the reality.

107:20- Of course, in some sense,

107:22what everybody is trying to do right now in AI

107:25is to extend the transformer to be able to deal with video.

107:28And there are very promising extensions, right?

107:32There's a book by Google that is called Perceiver,

107:36and that is overcoming some of the limitations

107:39of the transformer by letting it learn the topology

107:42of the different modalities separately,

107:45and by training it to find better input features.

107:50So the basically feature abstractions that are being used

107:52by this successor to GPT-3 are chosen such a way

107:57that it's able to deal with video input.

108:00And there is more to be done.

108:02So one of the limitations of GPT-3 is that it's amnesiac.

108:07So it forgets everything beyond the two pages

108:10that it currently reads, also during generation,

108:12not just during learning.

108:14- Do you think that's fixable

108:16within the space of deep learning?

108:18Can you just make a bigger, bigger, bigger input?

108:21- No, I don't think that our own working memory

108:24is infinitely large.

108:25It's probably also just a few thousand bits.

108:28But what you can do is you can structure

108:31this working memory.

108:31So instead of just force feeding this thing,

108:34a certain thing that it has to focus on,

108:37and it's not allowed to focus on anything else

108:39as its network, you allow it to construct

108:42its own working memory, as we do, right?

108:44When we are reading a book,

108:46it's not that we are focusing our attention

108:48in such a way that we can only remember

108:50the current page.

108:52We will also try to remember other pages

108:54and try to undo what we learned from them

108:56or modify what we learned from them.

108:58We might get up and take another book from the shelf.

109:01We might go out and ask somebody,

109:02and we can edit our working memory in any way

109:06that is useful to put a context together

109:08that allows us to draw the right inferences

109:11and to learn the right things.

109:13So this ability to perform experiments on the world

109:16based on an attempt to become fully coherent

109:20and to achieve causal closure,

109:22to achieve a certain aesthetic of your modeling,

109:24that is something that eventually needs to be done.

109:28And at the moment, we are skirting this in some sense

109:31by building systems that are larger and faster

109:33so they can use dramatically larger resources

109:36and human beings can do much more training data

109:38to get to models that in some sense

109:40are already very superhuman,

109:42and in other ways are laughingly incoherent.

109:45- So do you think sort of making the systems like,

109:50what would you say, multi-resolutional?

109:51So like some of the language models are focused on two pages,

109:56some are focused on two books,

110:03some are focused on two years of reading,

110:06some are focused on a lifetime.

110:08So it's like stacks, it's a GPT-3s all the way down.

110:11- You want to have gaps in between them.

110:13So it's not necessarily two years, there's no gaps.

110:17It's things out of two years or out of 20 years

110:19or 2,000 years or 2 billion years

110:22where you are just selecting those bits

110:24that are predicted to be the most useful ones

110:27to understand what you're currently doing.

110:29And this prediction itself requires a very complicated model

110:32and that's the actual model that you need to be making.

110:34It's not just that you are trying to understand

110:36the relationships between things,

110:38but what you need to make relationships,

110:40discover relationships over.

110:42- I wonder what that thing looks like,

110:45what the architecture for the thing

110:47that's able to have that kind of model.

110:49- I think it needs more degrees of freedom

110:52than the current models have.

110:54So it starts out with the fact that you possibly

110:57don't just want to have a feed-forward model,

110:59but you want it to be fully recurrent.

111:02And to make it fully recurrent,

111:04you probably need to loop it back into itself

111:06and allow it to skip connections.

111:08Once you do this, when you are predicting the next frame

111:12and your internal next frame in every moment,

111:15and you are able to skip connection,

111:17it means that signals can travel from the output

111:21of the network into the middle of the network

111:24faster than the inputs do.

111:25- Do you think it could still be differentiable?

111:28Do you think it still could be a neural network?

111:30- Sometimes it can, and sometimes it cannot.

111:33So it can still be a neural network,

111:35but not a fully differentiable one.

111:37And when you want to deal with non-differentiable ones,

111:40you need to have an attention system

111:42that is discrete and two-dimensional

111:44and can perform grammatical operations.

111:46You need to be able to perform program synthesis.

§14Transcript · 1:51:49–2:00:49

111:49You need to be able to backtrack in these operations

111:52that you perform on this thing.

111:54And this thing needs a model of what it's currently doing.

111:56And I think this is exactly the purpose

111:58of our own consciousness.

111:59- Yeah, the program things

112:03that trick you on your own networks.

112:05So let me ask you, it's not quite program synthesis,

112:09but the application of these language models

112:12to generation, to program synthesis,

112:15but generation of programs.

112:16So if you look at GitHub OpenPilot,

112:19which is based on OpenAI's codecs,

112:21I don't know if you got a chance to look at it,

112:22but it's the system that's able to generate code

112:26once you prompt it with, what is it?

112:30Like the header of a function with some comments.

112:32It seems to do an incredibly good job,

112:34or not a perfect job, which is very important,

112:39but an incredibly good job of generating functions.

112:42What do you make of that?

112:44Are you, is this exciting,

112:45or is this just a party trick, a demo?

112:49Or is this revolutionary?

112:50- I haven't worked with this yet,

112:53so it's difficult for me to judge it,

112:55but I would not be surprised

112:57if it turns out to be revolutionary.

112:59And that's because the majority of programming tasks

113:01that are being done in the industry right now

113:04are not creative.

113:05- Yeah.

113:06- People are writing code that other people have written,

113:08or they're putting things together

113:09from code fragments that others have had.

113:11And a lot of the work that programmers do in practice

113:14is to figure out how to overcome the gaps

113:17in their current knowledge,

113:19in the things that people have already done.

113:20- How to copy and paste from Stack Overflow, that's right.

113:23- Yes, and so of course we can automate that.

113:26- Yeah, to make it much faster

113:29to copy and paste from Stack Overflow.

113:30- Yes, but it's not just copying and pasting,

113:32it's also basically learning which parts you need to modify

113:36to make them fit together.

113:38- Yeah, like literally sometimes as simple

113:41as just changing the variable names

113:43so it fits into the rest of your code.

113:45- Yes, but this requires that you understand

113:46the semantics of what you're doing to some degree.

113:48- Yeah, and you can automate some of those things.

113:51The thing that makes people nervous, of course,

113:53is that a little bit wrong in a program

113:57can have a dramatic effect

114:00on the actual final operation of that program.

114:03So that's one little error,

114:05which in the space of language doesn't really matter,

114:08but in the space of programs can matter a lot.

114:12- Yes, but this is already what is happening

114:14when humans program code.

114:15- Yeah, this is--

114:16- So we have a technology to deal with this.

114:20- Somehow it becomes scarier

114:22when you know that a program generated code

114:25that's running a nuclear power plant.

114:27It becomes scarier.

114:29You know humans have errors too.

114:31- Exactly.

114:32- But it's scarier when a program is doing it because,

114:35why, why?

114:38- I mean, there's a fear that a program,

114:43like a program may not be as good as humans

114:48to know when stuff is important to not mess up.

114:51Like there's a misalignment of priorities,

114:58of values, that's potential.

115:01Maybe that's the source of the worry.

115:03I mean, okay, if I give you code generated by

115:08GitHub OpenPilot and code generated by a human

115:12and say, here, use one of these,

115:15which, how do you select today and in the next 10 years,

115:20which code to use?

115:21Wouldn't you still be comfortable with the human?

115:24- At the moment, when you go to Stanford to get an MRI,

115:29they will write a bill to the insurance over $20,000.

115:34And of this, maybe half of that gets paid by the insurance

115:38and a quarter gets paid by you.

115:40And the MRI cost them $600 to make, maybe, probably less.

115:44And what are the values of the person

115:47that writes the software and deploys this process?

115:50It's very difficult for me to say whether I trust people.

115:56I think that what happens there is a mixture

115:58of proper Anglo-Saxon Protestant values

116:01where somebody is trying to serve an abstract greater whole

116:04and organized crime.

116:06- Well, that's a very harsh,

116:08I think that's a harsh view of humanity.

116:15There's a lot of bad people, whether incompetent

116:18or just malevolent in this world, yes.

116:21But it feels like the more malevolent,

116:25so the more damage you do to the world,

116:29the more resistance you have in your own human heart.

116:34- But don't explain with malevolence or stupidity

116:37what can be explained by just people

116:38acting on their incentives.

116:40So what happens in Stanford is not that somebody is evil.

116:45It's just that they do what they're being paid for.

116:48- No, it's not evil.

116:50I tend to, no, I see that as malevolence.

116:53I see as I, even like being a good German,

116:58as I told you offline, is some,

117:01it's not absolute malevolence, but it's a small amount.

117:06It's cowardice.

117:07I mean, when you see there's something wrong with the world,

117:10it's either incompetence and you're not able to see it,

117:15or it's cowardice that you're not able to stand up,

117:17not necessarily in a big way, but in a small way.

117:21So I do think that is a bit of malevolence.

117:25I'm not sure the example you're describing

117:27is a good example of that. - So the question is,

117:28what is it that you are aiming for?

117:31And if you don't believe in the future,

117:34if you, for instance, think that the dollar

117:36is going to crash, why would you try to save dollars?

117:39If you don't think that humanity will be around

117:42in 100 years from now because global warming

117:45will wipe out civilization,

117:47why would you need to act as if it were?

117:49So the question is, is there an overarching aesthetics

117:53that is projecting you and the world into the future,

117:56which I think is the basic idea of religion,

117:59that you understand the interactions

118:01that we have with each other

118:02as some kind of civilization level agent

118:04that is projecting itself into the future.

118:07If you don't have that shared purpose,

118:09what is there to be ethical for?

118:12So I think when we talk about ethics and AI,

118:16we need to go beyond the insane bias discussions and so on,

118:20where people are just measuring the distance

118:22between a statistic to their preferred current world model.

118:27- The optimism, wait, wait, wait,

118:29I was a little confused by the previous thing,

118:31just to clarify.

118:32There is a kind of underlying morality

118:39to having an optimism that human civilization

118:43will persist for longer than 100 years.

118:45I think a lot of people believe

118:50that it's a good thing for us to keep living.

118:53- Yeah, of course. - And thriving.

118:54- This morality itself is not an end to itself.

118:56It's instrumental to people living in 100 years from now.

119:00- Right. - Or 500 years from now.

119:03So it's only justifiable if you actually think

119:06that it will lead to people

119:08or increase the probability of people being around

119:10in that timeframe.

119:12And a lot of people don't actually believe that,

119:14at least not actively.

119:16- But believe what exactly?

119:18So I was-- - Most people don't believe

119:20that they can afford to act on such a model.

119:23Basically what happens in the US

119:25is I think that the healthcare system

119:26is for a lot of people no longer sustainable,

119:29which means that if they need the help

119:30of the healthcare system,

119:31they're often not able to afford it.

119:33And when they cannot help it,

119:35they are often going bankrupt.

119:37I think the leading cause of personal bankruptcy

119:40in the US is the healthcare system.

119:42- Yeah. - And that would not

119:44be necessary.

119:44It's not because people are consuming

119:46more and more medical services

119:48and are achieving a much, much longer life as a result.

119:51That's not actually the story that is happening

119:53because you can compare it to other countries.

119:55And life expectancy in the US is currently not increasing

119:58and it's not as high as in all the other

120:00industrialized countries.

120:01So some industrialized countries are doing better

120:03with a much cheaper healthcare system.

120:06And what you can see is, for instance, administrative load.

120:10The healthcare system has maybe to some degree deliberately

120:14set up its job placement program to allow people

120:18to continue living a middle-class existence,

120:21despite not having a useful use case in productivity.

120:26So they are being paid to push paper around.

120:29And the number of administrators in the healthcare system

120:32has been increasing much faster

120:33than the number of practitioners.

120:35And this is something that you have to pay for, right?

120:37And also the revenues that are being generated

120:41in the healthcare system are relatively large

120:42and somebody has to pay for them.

120:44And the result why they are so large

120:46is because market mechanisms are not working.

120:49The FDA is largely not protecting people

§15Transcript · 2:00:52–2:09:19

120:52from malpractice of healthcare providers.

120:56The FDA is protecting healthcare providers

120:58from competition.

120:59- Right, okay, okay.

121:00- So this is a thing that has to do with values.

121:03And this is not because people are malicious on all levels.

121:06It's because they are not incentivized to act

121:09on a greater whole, on this idea that you treat somebody

121:12who comes to you as a patient

121:14like you would treat a family member.

121:15- Yeah, yeah, but we're trying, I mean,

121:18you're highlighting a lot of the flaws

121:20of the different institutions

121:21the systems we're operating under.

121:23But I think there's a continued throughout history

121:25mechanism design of trying to design incentives

121:29in such a way that these systems behave better

121:31and better and better.

121:32I mean, it's a very difficult thing to operate

121:35a society of hundreds of millions of people

121:38effectively with--

121:39- Yes, so do we live in a society

121:41that is ever correcting?

121:42Is this, do we observe that our models

121:46of what we are doing are predictive of the future

121:49and when they are not, we improve them.

121:51Our laws adjudicated with clauses

121:54that you put into every law,

121:56what is meant to be achieved by that law

121:57and the law will be automatically repealed

122:00if it's not achieving that, right?

122:01If you are optimizing your own laws,

122:03if you're writing your own source code,

122:05you probably make an estimate of what is the thing

122:08that's currently wrong in my life?

122:09What is it that I should change about my own policies?

122:12What is the expected outcome?

122:14And if that outcome doesn't manifest,

122:16I will change the policy back, right?

122:18Or I would change it into something different.

122:20Are we doing this on a societal level?

122:22- I think so.

122:23I think it's easy to sort of highlight the,

122:25I think we're doing it in the way that,

122:27like I operate my current life.

122:30I didn't sleep much last night.

122:32You would say that, Lex, the way you need

122:35to operate your life is you need to always get sleep.

122:37The fact that you didn't sleep last night

122:39is totally the wrong way to operate in your life.

122:43Like you should have gotten all your shit done in time

122:46and gotten to sleep because sleep is very important

122:48for health and you're highlighting,

122:50look, this person is not sleeping.

122:52Look, the medical, the healthcare system is operating poor.

122:56But the point is that we just,

122:59it seems like this is the way,

123:00especially in the capitalist society we operate,

123:02we keep running into trouble and last minute,

123:06we try to get our way out through innovation

123:09and it seems to work.

123:10You have a lot of people that ultimately are trying

123:13to build a better world and get urgency about them

123:18when the problem becomes more and more imminent.

123:22And that's the way this operates.

123:24But if you look at the history, the long arc of history,

123:29it seems like that operating on deadlines produces progress

123:35and builds better and better systems.

123:36- You probably agree with me that the US

123:39should have engaged in mask production in January, 2020.

123:44And that we should have shut down the airports early on

123:47and that we should have made it mandatory

123:50that the people that work in nursing homes

123:53on campus, rather than living at home

123:57and then coming in and infecting people in the nursing homes

124:01that had no immune response to COVID.

124:03And that is something that was, I think, visible back then.

124:08The correct decisions haven't been made.

124:10We would have the same situation again.

124:12How do we know that these wrong decisions

124:14are not being made again?

124:15Have the people that made the decisions

124:17to not protect the nursing homes been punished?

124:20Have the people that made the wrong decisions

124:23with respect to testing that prevented the development

124:26of testing by startup companies and the importing of tests

124:30from countries that already had them,

124:32have these people been held responsible?

124:34- First of all, so what do you want to put

124:37before the firing squad?

124:38I think they are being held responsible.

124:39- No, just make sure that this doesn't happen again.

124:41- No, but it's not that.

124:44Yes, they're being held responsible by many voices,

124:47by people being frustrated.

124:48There's new leaders being born now

124:50that are going to see rise to the top in 10 years.

124:54This moves slower than, there's obviously a lot

124:57of older incompetence and bureaucracy

125:01and these systems move slowly.

125:03They move like science, one death at a time.

125:06So yes, I think the pain that's been felt

125:11in the previous year is reverberating throughout the world.

125:15- Maybe I'm getting old.

125:16I suspect that every generation in the US

125:19after the war has lost the plot even more.

125:21I don't see this development.

125:23- The war, World War II?

125:24- Yes, so basically there was a time

125:26when we were modernist.

125:29And in this modernist time, the US felt actively threatened

125:33by the things that happened in the world.

125:35The US was worried about possibility of failure.

125:38And this imminence of possible failure led to decisions.

125:44There was a time when the government would listen

125:47to physicists about how to do things.

125:50And the physicists were actually concerned

125:52about what the government should be doing.

125:53So they would be writing letters to the government.

125:56And so for instance, the decision

125:57for the Manhattan Project was something

125:59that was driven in a conversation

126:01between physicists and the government.

126:04I don't think such a discussion would take place today.

126:06- I disagree.

126:07I think if the virus was much deadlier,

126:10we would see a very different response.

126:12I think the virus was not sufficiently deadly.

126:14And instead, because it wasn't very deadly,

126:17what happened is the current system

126:20started to politicize it.

126:22The mask, this is what I realized with masks early on.

126:25They were not very quickly became not as a solution,

126:29but they became a thing that politicians used

126:32to divide the country.

126:33So the same things happened with vaccines, same thing.

126:37So like nobody's really,

126:38people weren't talking about solutions to this problem

126:41because I don't think the problem was bad enough.

126:43When you talk about the war,

126:45I think our lives are too comfortable.

126:47I think in the developed world, things are too good

126:52and we have not faced severe dangers.

126:54When the danger, the severe dangers,

126:57existential threats are faced, that's when we step up

127:00on a small scale and a large scale.

127:03Now, I don't, that's sort of my argument here,

127:08but I did think the virus,

127:10I was hoping that it was actually sufficiently dangerous

127:14for us to step up because especially in the early days,

127:18it was unclear.

127:19It still is unclear because of mutations,

127:23how bad it might be, right?

127:25And so I thought we would step up and even,

127:30so the masks point is a tricky one because to me,

127:35the manufacture of masks isn't even the problem.

127:38I'm still to this day and I was involved

127:41with a bunch of this work,

127:42have not seen good science done on whether masks work or not.

127:45Like there still has not been a large scale study.

127:49To me, that should be, there should be large scale studies

127:51on every possible solution, like aggressive,

127:55in the same way that the vaccine development was aggressive.

127:57There should be masks, which tests,

127:59what kind of tests work really well,

128:02what kind of, like even the question

128:04of how the virus spreads,

128:06there should be aggressive studies on that to understand.

128:09I'm still, as far as I know,

128:12there's still a lot of uncertainty about that.

128:14Nobody wants to see this as an engineering problem

128:17that needs to be solved.

128:18It's that I was surprised about,

128:21but I would-- - I find that our views

128:23are largely convergent, but not completely.

128:25So I agree with the thing that,

128:27because our society in some sense perceives itself

128:30as too big to fail.

128:32- Right. - And the virus

128:34did not alert people to the fact

128:35that we are facing possible failure.

128:37That basically put us into the postmodernist mode.

128:41And I don't mean in a philosophical sense,

128:43but in a societal sense,

128:45the difference between the postmodern society

128:47and the modern society is that the modernist society

128:50has to deal with the ground truths,

128:52and the postmodernist society has to deal with appearances.

128:55Politics becomes a performance,

128:57and the performance is done for an audience,

128:59and the organized audience is the media,

129:02and the media evaluates itself via other media, right?

129:05So you have an audience of critics

129:07that evaluate themselves.

129:09And I don't think it's so much the failure

129:10of the politicians, because to get in power

129:12and to stay in power, you need to be able

129:15to deal with the published opinion.

129:17- Well, I think it goes in cycles,

129:19because what's going to happen

§16Transcript · 2:09:21–2:18:59

129:21is all of the small business owners,

129:24all the people who truly are suffering

129:26and will suffer more because the effects

129:29of the closure of the economy

129:31and the lack of solutions to the virus,

129:34they're going to apprise.

129:36And hopefully, I mean, this is where charismatic leaders

129:40can get the world in trouble.

129:42But hopefully, we'll elect great leaders

129:47that will break through this postmodernist idea

129:51of the media and the perception

129:55and the drama on Twitter and all that kind of stuff.

129:57- But you know this can go either way.

129:59- Yeah.

130:00- When the Weimar Republic was unable

130:02to deal with the economic crisis that Germany was facing,

130:07there was an option to go back.

130:09And there were people which thought,

130:11let's get back to a constitutional monarchy

130:14and let's get this to work,

130:16because democracy doesn't work.

130:18And eventually, there was no way back.

130:21People decided there was no way back.

130:23They needed to go forward.

130:24And the only options for going forward

130:26was to become a Stalinist communist,

130:29basically an option to completely expropriate

130:34the factories and so on and nationalize them

130:36and to reorganize Germany in communist terms

130:40and ally itself with Stalin and fascism.

130:44And both options were obviously very bad.

130:48And the one that the Germans picked led to a catastrophe

130:51that devastated Europe.

130:54And I'm not sure if the US has an immune response

130:57against that.

130:58I think that the far right is currently very weak in the US,

131:01but this can easily change.

131:03- Do you think from a historical perspective,

131:08Hitler could have been stopped from within Germany

131:12or from outside or this?

131:15Well, depends on who you want to focus,

131:17whether you want to focus on Stalin or Hitler,

131:20but it feels like Hitler was the one

131:22as a political movement that could have been stopped.

131:25- I think that the point was that

131:28a lot of people wanted Hitler.

131:29So he got support from a lot of quarters.

131:32It was a number of industrialists who supported him

131:35because they thought that the democracy

131:36is obviously not working and unstable

131:38and you need a strong man.

131:40And he was willing to play that part.

131:43There were also people in the US

131:45who thought that Hitler would stop Stalin

131:47and would act as a bulwark against Bolshevism,

131:51which he probably would have done, right?

131:54But at which cost?

131:56And then many of the things that he was going to do,

131:59like the Holocaust,

132:01was something where people thought this is rhetoric.

132:04He's not actually going to do this.

132:07Especially many of the Jews themselves,

132:09which were humanists.

132:10And for them, this was outside of the scope

132:12that was thinkable.

132:13- Right.

132:14I mean, I wonder if Hitler is uniquely,

132:18I want to carefully use this term, but uniquely evil.

132:23So if Hitler was never born,

132:26if somebody else would come in his place.

132:29So like, just thinking about the progress of history,

132:33how important are those singular figures

132:36that lead to mass destruction and cruelty?

132:41Because my sense is Hitler was unique.

132:45It wasn't just about the environment

132:49and the context that gave him.

132:50Another person would not come in his place

132:54to do as destructive of the things that he did.

132:58There was a combination of charisma,

133:01of madness, of psychopathy, of just ego,

133:06all of those things,

133:07which are very unlikely to come together

133:09in one person in the right time.

133:11- It also depends on the context of the country

133:14that you're operating in.

133:16If you tell the Germans that they have a historical destiny

133:21in this romantic country,

133:23the effect is probably different

133:25than it is in other countries.

133:27But Stalin has killed a few more people than Hitler did.

133:32And if you look at the probability

133:35that you survived under Stalin,

133:37Hitler killed people if he thought

133:43they were not worth living,

133:45or if they were harmful to his racist project.

133:49He basically felt that the Jews would be too cosmopolitan

133:52and would not be willing to participate

133:55in the racist redefinition of society

133:57and the value of society and an ethnostate in this way,

134:01as he wanted it to have it.

134:03So he saw them as a harmful danger,

134:06especially since they played such an important role

134:09in the economy and culture of Germany.

134:13And so he had basically had some radical,

134:18but rational reason to murder them.

134:20And Stalin just killed everyone.

134:23He basically, the Stalinist purges were such a random thing

134:26where he said that there's a certain possibility

134:31that this particular part of the population

134:34has a number of German collaborators or something,

134:36and we just kill them all.

134:38Or if you look at what Mao did,

134:40the number of people that were killed in absolute numbers

134:44were much higher under Mao than they were under Stalin.

134:47So it's super hard to say.

134:49The other thing is that you look at Genghis Khan and so on,

134:53how many people he killed.

134:55When you see there are a number of things

134:58that happen in human history

134:59that actually really put a substantial dent

135:02in the existing population or Napoleon.

135:05And it's very difficult to eventually measure it

135:09because what's happening is basically evolution

135:12on a human scale,

135:15where one monkey figures out a way to become viral

135:19and is using this viral technology

135:22to change the patterns of society

135:24at the very, very large scale.

135:26And what we find so abhorrent about these changes

135:29is the complexity that is being destroyed by this.

135:32That it's basically like a big fire

135:33that burns out a lot of the existing culture and structure

135:36that existed before.

135:38- Yeah, and it all just starts with one monkey,

135:42one charismatic ape,

135:44and there's a bunch of them throughout history.

135:46- Yeah, but it's in a given environment.

135:48It's basically similar to wildfires in California, right?

135:51The temperature is rising, there is less rain falling,

135:55and then suddenly a single spark can have an effect

135:57that in other times would be contained.

135:59- Okay, speaking of which,

136:03I love how we went to Hitler and Stalin

136:05from 20, 30 minutes ago,

136:09GPT-3 generating, doing program synthesis.

136:13The argument was about morality of AI versus human.

136:18And specifically in the context of writing programs,

136:26specifically in the context of programs

136:28that can be destructive.

136:29So running nuclear power plants

136:31or autonomous weapons systems, for example.

136:35And I think your inclination was to say

136:39that it's not so obvious

136:40that AI would be less moral than humans,

136:43or less effective at making a world

136:46that would make humans happy.

136:48- So I'm not talking about self-directed systems

136:52that are making their own goals at a global scale.

136:57If you just talk about the deployment

136:59of technological systems that are able

137:01to see order and patterns and use this as control models

137:05to act on the goals that we give them,

137:08then if you have the correct incentives

137:11to set the correct incentives for these systems,

137:13I'm quite optimistic.

137:14- But so humans versus AI, let me give you an example.

137:20Autonomous weapons systems.

137:22Let's say there's a city somewhere in the Middle East

137:26that has a number of terrorists.

137:30And the question is, what's currently done

137:33with drone technology is you have information

137:36about the location of a particular terrorist,

137:38and you have a targeted attack,

137:40you have a bombing of that particular building.

137:42And that's all directed by humans

137:45at the high level strategy,

137:47and also at the deployment of individual bombs

137:50and missiles like that the actual,

137:52everything is done by human except the final targeting.

137:56And the like the, it's like with spot similar thing,

137:59like control the flight.

138:01Okay, what if you give AI control and saying,

138:05write a program that says,

138:10here's the best information I have available

138:12about the location of these five terrorists.

138:14Here's the city, make sure it's,

138:16all the bombing you do is constrained to the city.

138:19Make sure it's precision based, but you take care of it.

138:22So you do one level of abstraction out and saying,

138:26take care of the terrorists in the city.

138:29Which are you more comfortable with,

138:31the humans or the JavaScript GPT-3 generated code

138:35that's doing the deployment?

138:38I mean, that's, this is the kind of question I'm asking,

138:42is the kind of bugs that we see in human nature,

138:47are they better or worse than the kind of bugs we see in AI?

138:51- They're different bugs.

138:52There is an issue that if people are creating

138:56an imperfect automation of a process

138:59that normally requires a mobile judgment.

§17Transcript · 2:19:02–2:28:18

139:02And this mobile judgment is the reason why

139:06it cannot be automated often.

139:07It's not because the computation is too expensive,

139:12but because the model that you give the AI

139:14is not an adequate model of the dynamics

139:17of the world because the AI does not understand

139:19the context that it's operating in the right way.

139:22And this is something that already happens with Excel.

139:24Right, you don't need to have an AI system to do this.

139:27If you have an automated process in place

139:30where humans decide using automated criteria,

139:33whom to kill when and whom to target when,

139:36which already happens, right?

139:38And you have no way to get off the kill list

139:40once that happens.

139:41Once you have been targeted according

139:43to some automatic criterion by people, right?

139:45In a bureaucracy.

139:46That is the issue.

139:49The issue is not the AI, it's the automation.

139:52- So there's something about, right, it's automation.

139:56But there's something about the,

139:58there's a certain level of abstraction

140:00where you give control to AI to do the automation.

140:04There's a scale that could be achieved

140:07that it feels like the scale of bug and scale mistake

140:10and scale of destruction that could be achieved

140:14of the kind that humans cannot achieve.

140:17So AI is much more able to destroy

140:19an entire country accidentally versus humans.

140:22It feels like the more civilians die as a react

140:27or suffer as the consequences of your decisions,

140:31the more weight there is on the human mind

140:34to make that decision.

140:35And so like, it becomes more and more unlikely

140:39to make that decision for humans.

140:41For AI, it feels like it's harder

140:43to encode that kind of weight.

140:47- In a way, the AI that we're currently building

140:49is automating statistics, right?

140:52Intelligence is the ability to make models

140:54so you can act on them.

140:55And AI is the tool to make better models.

140:57So in principle, if you're using AI wisely,

141:01you're able to prevent more harm.

141:04And I think that the main issue is not on the side

141:07of the AI, it's on the side of the human command hierarchy

141:10that is using technology irresponsibly.

141:12- So the question is, how hard is it to encode,

141:15to properly encode the right incentives into the AI?

141:19- So for instance, there's this idea,

141:21what happens if we let our airplanes being flown

141:24with AI systems and then neural network is a black box

141:27and so on.

141:28And it turns out our neural networks

141:30are actually not black boxes anymore.

141:32There are functional approximators using linear algebra

141:36and there are performing things that we can understand.

141:40But we can also, instead of letting the neural network fly

141:43the airplane, use the neural network to generate approval

141:46of the correct program.

141:47There's a degree of accuracy of the proof

141:49that a human could not achieve.

141:51And so we can use our AI by combining different technologies

141:55to build systems that are much more reliable

141:57than the systems that a human being could create.

142:00And so in this sense, I would say that if you use

142:04an early stage of technology to save labor

142:08and don't employ competent people,

142:11but just to hack something together, because you can,

142:14that is very dangerous.

142:15And if people are acting under these incentives

142:17that they get away with delivering shoddy work

142:20more cheaply using AI, there's less human oversight

142:23than before, that's very dangerous.

142:25- The thing is though, AI is still going to be unreliable,

142:29perhaps less so than humans,

142:30but it will be unreliable in novel ways.

142:33And-

142:35- Yeah, but this is an empirical question

142:37and it's something that we can figure out and work with.

142:39So the issue is, do we trust the systems,

142:43the social systems that we have in place

142:45and the social systems that we can build and maintain

142:48that they're able to use AI responsibly?

142:50If they can, then AI is good news.

142:53If they cannot, then it's going to make

142:54the existing problems worse.

142:56- Well, and also who creates the AI, who controls it,

143:00who makes money from it, because it's ultimately humans.

143:03And then you start talking about

143:05how much you trust the humans.

143:07- So the question is, what does who mean?

143:08I don't think that we have identity per se.

143:11I think that the story of a human being is somewhat random.

143:15What happens is more or less that everybody

143:17is acting on their local incentives,

143:19what they perceive to be their incentives.

143:22And the question is, what are the incentives

143:24that the one that is pressing the button

143:27is operating under?

143:28- Yeah.

143:29It's nice for those incentives to be transparent.

143:32So for example, I'll give you an example.

143:36There seems to be a significant distrust of tech,

143:39like entrepreneurs in the tech space,

143:44or people that run, for example, social media companies,

143:47like Mark Zuckerberg.

143:48There's not a complete transparency of incentives

143:53under which that particular human being operates.

143:56We can listen to the words he says,

144:00or what the marketing team says for a company,

144:03but we don't know.

144:04And that becomes a problem when the algorithms

144:08and the systems created by him and other people

144:12in that company start having more and more impact on society.

144:16If the incentives were somehow,

144:21the definition and the explainability of the incentives

144:26was decentralized such that nobody can manipulate it,

144:30no propaganda type manipulation

144:34of how these systems actually operate could be done,

144:38then yes, I think AI could achieve much fairer,

144:43much more effective solutions

144:50to difficult ethical problems.

144:53But when there's humans in the loop

144:55manipulating the dissemination,

144:59the communication of how the system actually works,

145:02that feels like you can run into a lot of trouble.

145:05And that's why there's currently a lot of distrust

145:07for people at the heads of companies

145:10that have increasingly powerful AI systems.

145:12- I suspect what happened traditionally in the US

145:16was that since our decision-making is much more central,

145:19or decentralized than in an authoritarian state, right?

145:23People are making decisions autonomously

145:24at many, many levels in a society.

145:26What happened that was we created coherence

145:30and cohesion in society by controlling what people thought

145:33and what information they had.

145:35The media synchronized public opinion

145:38and social media have disrupted this.

145:40It's not, I think, so much Russian influence or something.

145:43It's everybody's influence.

145:45It's that a random person can come up

145:47with a conspiracy theory and disrupt what people think.

145:52And if that conspiracy theory is more compelling

145:55or more attractive than the standardized

145:58public conspiracy theory that we give people as a default,

146:01then it might get more traction, right?

146:03You suddenly have the situation that a single individual

146:05somewhere on a farm in Texas has more listeners than CNN.

146:10- Which particular farmer are you referring to in Texas?

146:13(both laughing)

146:16- Probably no one.

146:19- Yes, I had dinner with him a couple of times.

146:20- Okay. - Right.

146:22It's an interesting situation because you cannot get

146:24to be an anchor in CNN if you don't go

146:26through a complicated gatekeeping process.

146:30And suddenly you have random people

146:32without that gatekeeping process

146:34just optimizing for attention.

146:36Not necessarily with a lot of responsibility

146:39for the long-term effects of projecting these theories

146:42into the public.

146:43And now there is a push of making social media

146:46more like traditional media, which means that the opinion

146:50that is being projected in social media

146:52is more limited to an acceptable range.

146:54With the goal of getting society into safe waters

146:58and increase the stability and cohesion of society again,

147:00which I think is a laudable goal.

147:03But of course it also is an opportunity

147:05to seize the means of indoctrination.

147:08And the incentives that people are under when they do this

147:11are in such a way that the AI ethics that we would need

147:17becomes very often something like AI politics,

147:20which is basically partisan and ideological.

147:23And this means that whatever one side says,

147:26another side is going to be disagreeing with.

147:28In the same way as when you turn masks

147:30or the vaccine into a political issue,

147:33if you say that it is politically virtuous

147:35to get vaccinated, it will mean that the people

147:37that don't like you will not want to get vaccinated.

147:41And as soon as you have this partisan discourse,

147:43it's going to be very hard to make the right decisions

147:47because the incentives get to be the wrong ones.

147:48AI ethics needs to be super boring.

147:51It needs to be done by people who do statistics all the time

147:54and have extremely boring, long-winded discussions

147:58that most people cannot follow

147:59because they are too complicated, but that are dead serious.

148:02These people need to be able to be better at statistics

148:05than the leading machine learning researchers.

148:07And at the moment, the AI ethics debate is the one

148:12where you don't have any barrier to entry.

148:14Everybody who has a strong opinion

148:16and is able to signal that opinion in the right way-

148:18- Strong words from Joshua Bach.

§18Transcript · 2:28:20–2:37:31

148:20(Laura laughs)

148:22- And to me, that's a very frustrating thing

148:24because the field is so crucially important to our future.

148:26- It's so crucially important,

148:28but the only qualification you currently need

148:31is to be outraged by the injustice in the world.

148:34- It's more complicated, right?

148:36Everybody seems to be outraged.

148:37But let's just say that the incentives

148:40are not always the right ones.

148:42So basically, I suspect that a lot of people

148:45that enter this debate don't have a vision

148:48for what society should be looking like

148:50in a way that is non-violent,

148:51where we preserve liberal democracy,

148:53where we make sure that we all get along

148:56and we are around in a few hundred years from now,

149:00preferably with a comfortable technological civilization

149:03around us.

149:04- I generally have a very foggy view of that world,

149:10but I tend to try to follow,

149:12and I think society should in some degree

149:13follow the gradient of love,

149:16increasing the amount of love in the world.

149:19And whenever I see different policies or algorithms

149:22or ideas that are not doing so,

149:24obviously that's the ones that kind of resist.

149:27- So the thing that terrifies me about this notion

149:30is I think that German fascism was driven by love.

149:34It was just a very selective love.

149:37It was a love that basically-

149:38- But now you're just manipulating.

149:40I mean, that's, you have to be very careful.

149:45You're talking to the wrong person in this way about love.

149:50- So let's talk about what love is.

149:52And I think that love is the discovery of shared purpose.

149:56It's the recognition of the sacred and the other.

149:59And this enables non-transactional interactions.

150:02- But the size of the other that you include

150:07needs to be maximized.

150:09So it's basically appreciation,

150:14like deep appreciation of the world around you fully,

150:19like including the people that are very different than you,

150:26the people that disagree with you completely,

150:27including people, including living creatures

150:30outside of just people, including ideas.

150:33And it's like appreciation of the full mess of it.

150:36And also it has to do with like empathy,

150:39which is coupled with a lack of confidence,

150:44uncertainty about, of your own rightness.

150:47It's like an open, a radical open-mindedness

150:50to the way forward.

150:51- I agree with every part of what you said.

150:53And now if you scale it up, what you recognize

150:56is that love is in some sense the service

150:59to a next level agency, to the highest level agency

151:02that you can recognize.

151:04It could be, for instance, life on earth

151:06or beyond that, where you could say intelligent complexity

151:10in the universe that you try to maximize in a certain way.

151:14But when you think it's true,

151:15it basically means a certain aesthetic.

151:17And there is not one possible aesthetic.

151:20There are many possible aesthetics.

151:22And once you project an aesthetic into the future,

151:25you can see that there are some which defect from it,

151:29which are in conflict with it,

151:30that are corrupt, that are evil.

151:33You and me would probably agree that Hitler was evil

151:37because the aesthetic of the world that he wanted

151:40is in conflict with the aesthetic of the world

151:41that you and me have in mind.

151:43And so the thing that he destroyed,

151:48we want to keep them in the world.

151:50- There's kind of ways to deal.

151:55I mean, Hitler is an easier case,

151:56but perhaps he wasn't so easy in the '30s

151:59to understand who is Hitler and who is not.

152:02- No, it was just that there was no consensus

152:04that the aesthetics that he had in mind were unacceptable.

152:07- Yeah.

152:08I mean, it's difficult.

152:09Love is complicated because you can't just be so open-minded

152:16that you let evil walk into the door,

152:20but you can't be so self-assured

152:24that you can always identify evil perfectly

152:29because that's what leads to Nazi Germany,

152:31having a certainty of what is and wasn't evil,

152:34like always drawing lines of good versus evil.

152:37There seems to be,

152:39there has to be a dance between

152:43like hard stances extending up against what is wrong,

152:51and at the same time, empathy and open-mindedness

152:55of towards not knowing what is right and wrong,

152:59and like a dance between those.

153:01- I found that when I watched the Miyazaki movies

153:03that there is nobody who captures my spirituality

153:06as well as he does.

153:07It's very interesting and just vicious.

153:10There is something going on in his movies

153:13that is very interesting.

153:14So for instance, Mononoke is discussing

153:17not only an answer to Disney's simplistic notion of Mowgli,

153:22the jungle boy who was raised by wolves,

153:24and as soon as he sees people, realizes that he's one of them

153:27and the way in which the moral life and nature

153:32is simplified and romanticized and turned into kitsch.

153:36It's disgusting in the Disney movie,

153:38and he answers to this.

153:39You see, he's replaced by Mononoke,

153:41this wolf girl who was raised by wolves

153:43and who was fierce and dangerous

153:45and who cannot be socialized because she cannot be tamed,

153:49cannot be part of human society.

153:50And you see, human society,

153:52it's something that is very, very complicated.

153:54You see people extracting resources and destroying nature,

153:58but the purpose is not to be evil,

154:01but to be able to have a life that is free from,

154:04for instance, oppression and violence

154:07and to curb death and disease.

154:10And you basically see this conflict,

154:13which cannot be resolved in a certain way.

154:15You see this moment when nature is turned into a garden

154:18and it loses most of what it actually is

154:20and humans no longer submitting to life

154:23and death and nature.

154:24And to these questions, there is no easy answer.

154:26So he just turns it into something that is being observed

154:29as a journey that happens.

154:31And that happens with a certain degree of inevitability.

154:34And the nice thing about all his movies

154:37is there's a certain main character,

154:38and it's the same in all movies.

154:41It's this little girl that is basically Heidi.

154:45And I suspect that happened because when he did field work

154:50for working on the Heidi movies,

154:52back then the Heidi animations,

154:54before he did his own movies,

154:55he traveled to Switzerland and Southeastern Europe

155:00and the Adriatic and so on,

155:02and got an idea about a certain aesthetic

155:04and a certain way of life that informed his future thinking.

155:08And Heidi has a very interesting relationship

155:11to herself and to the world.

155:13There's nothing that she takes for herself.

155:15She is in a way fearless because she is committed

155:18to a service, to a greater whole.

155:20Basically, she is completely committed to serving God.

155:24And it's not an institutionalized God.

155:26It has nothing to do with the Roman Catholic Church

155:28or something like this.

155:30But in some sense, Heidi is an embodiment

155:32of the spirit of European Protestantism.

155:34It's this idea of a being

155:37that is completely perfect and pure.

155:40And it's not a feminist vision

155:42because she is not a girl boss or something like this.

155:48She is the justification for the men in the audience

155:52to protect her, to build a civilization around her

155:54that makes her possible.

155:56So she is not just the sacrifice of Jesus,

155:59who is innocent and therefore nailed to the cross.

156:02She is not being sacrificed.

156:04She is being protected by everybody around her

156:06who recognizes that she is sacred.

156:08And there are enough around her to see that.

156:11So that's a very interesting perspective.

156:13There's a certain notion of innocence.

156:16And this notion of innocence is not universal.

156:18It's not in all cultures.

156:20Hitler wasn't innocent.

156:21His idea of Germany was not that there is an innocence

156:25that is being protected.

156:26There was a predator that was going to triumph.

156:29And it's also something that is not

156:30at the core of every religion.

156:32There are many religions which don't care about innocence.

156:34They might care about increasing the status of something.

156:39And that's a very interesting notion that is quite unique

156:44and not claiming it's the optimal one.

156:47It's just a particular kind of aesthetic,

156:49which I think makes Miyazaki

156:51into the most relevant Protestant philosopher today.

156:55- And you're saying in terms of all the ways

156:59that a society can operate,

157:00perhaps the preservation of innocence

157:02might be one of the best.

157:07- No, it's just my aesthetic.

157:09- Your aesthetic, gotcha.

157:11- It's a particular way in which I feel

157:13that I relate to the world

157:14that is natural to my own specialization.

157:16And maybe it's not an accident

157:18that I have cultural roots in Europe,

157:21in a particular world.

157:23And so maybe it's a natural convergence point

157:26and it's not something that you will find

157:28in all other times in history.

157:31- So I'd like to ask you about Solzhenitsyn

§19Transcript · 2:37:34–2:47:30

157:34and our individual role as ants in this very large society.

157:39So he says that some version of the line

157:42between good and evil runs to the heart of every man.

157:44Do you think all of us are capable of good and evil?

157:47What's our role in this play,

157:50in this game we're all playing?

157:55Is all of us capable to play any role?

157:57Is there an ultimate responsibility to,

158:01you mentioned maintaining innocence

158:04or whatever the highest ideal for a society you want,

158:09are all of us capable of living up to that?

158:11And that's our responsibility.

158:13Or is there significant limitations

158:15to what we're able to do in terms of good and evil?

158:18- So there is a certain way, if you are not terrible,

158:24if you are committed to some kind of civilizational agency,

158:29the next level agent that you are serving,

158:31some kind of transcendent principle.

158:33In the eyes of that transcendental principle,

158:36you are able to discern good from evil.

158:38Otherwise you cannot,

158:39otherwise you have just individual aesthetics.

158:41The cat that is torturing a mouse

158:43is not evil because the cat does not envision

158:46or no part of the world,

158:47of the cat is envisioning a world

158:50where there is no violence and nobody is suffering.

158:53If you have an aesthetic

158:55where you want to protect innocence,

158:56then torturing somebody needlessly is evil,

159:00but only then.

159:02- No, but within, I guess the question is within the aesthetic,

159:05within your sense of what is good and evil,

159:10are we still,

159:12it seems like we're still able to commit evil.

159:17- Yes, so basically if you are committing

159:19to this next level agent,

159:20you are not necessarily are this next level agent, right?

159:23You are a part of it.

159:24You have a relationship to it,

159:26like a cell does to its organism, its hyperorganism.

159:29And it only exists to the degree

159:31that it's being implemented by you and others.

159:34And that means that you're not completely fully serving it.

159:38You have freedom in what you decide,

159:40whether you are acting on your impulses and local incentives

159:43and your feral impulses, so to speak,

159:45or whether you're committing to it.

159:47And what you perceive then is a tension

159:50between what you would be doing

159:52with respect to the thing that you recognize

159:55as the sacred if you do,

159:57and what you're actually doing.

159:58And this is the line between good and evil, right?

160:01Where you see, oh, I'm here acting

160:03on my local incentives or impulses.

160:05And here I'm acting on what I consider to be sacred.

160:08And there's a tension between those.

160:09And this is the line between good and evil

160:12that might run through your heart.

160:14And if you don't have that,

160:15if you don't have this relationship

160:17to a transcendental agent,

160:18you could call this relationship

160:20to the next level agent soul, right?

160:21It's not a thing.

160:22It's not an immortal thing that is intrinsically valuable.

160:25It's a certain kind of relationship

160:27that you project to understand what's happening.

160:29Somebody is serving this transcendental sacredness

160:31or they're not.

160:33If you don't have this soul, you cannot be evil.

160:35You're just a complex, natural phenomenon.

160:39- So if you look at life, like starting today

160:42or starting tomorrow, when we leave here today,

160:45there's a bunch of trajectories

160:48that you can take through life, maybe countless.

160:53Do you think some of these trajectories

160:57in your own conception of yourself,

160:59some of those trajectories are the ideal life?

161:04A life that if you were to be the hero of your life story,

161:09you would want to be?

161:10Like, is there some Josh or Bach that you're striving to be?

161:14Like, this is the question I ask myself

161:16as an individual trying to make a better world

161:20in the best way that I could conceive of.

161:22What is my responsibility there?

161:24And how much am I responsible for the failure to do so?

161:28'Cause I'm lazy and incompetent too often

161:33in my own perception.

161:35- In my own world view, I'm not very important.

161:38So I don't have place for me as a hero in my own world.

161:42I'm trying to do the best that I can,

161:46which is often not very good.

161:48And so it's not important for me to have status

161:52or to be seen in a particular way.

161:55It's helpful if others can see me,

161:57if a few people can see me, that can be my friends.

161:59- No, sorry, I want to clarify.

162:01The hero, I didn't mean status or perception

162:05or like some kind of marketing thing,

162:09but more in private, in the quiet of your own mind.

162:13Is there the kind of man you want to be

162:16and would consider it a failure if you don't become that?

162:20That's what I meant by hero.

162:22- Yeah, not really.

162:23I don't perceive myself as having such an identity.

162:26And it's also sometimes frustrating,

162:32but it's basically a lack of having this notion

162:37of father that I need to be emulating.

162:40- It's interesting.

162:45I mean, it's the leaf floating down the river.

162:47I worry that...

162:50- Sometimes it's more like being the river.

162:59- I'm just a fat frog sitting in a leaf

163:02on a dirty, muddy lake.

163:06I wish I was-- - Full of love.

163:09- Waiting for a princess to kiss me.

163:13Or the other way, I forgot which way it goes.

163:15Somebody kisses somebody.

163:17Can I ask you, I don't know if you know who Michael Malice is

163:21but in terms of constructing systems of incentives,

163:27it's interesting to ask.

163:29I don't think I've talked to you about this before.

163:32Malice espouses anarchism.

163:35So he sees all government as fundamentally

163:38getting in the way or even being destructive

163:42to collaborations between human beings thriving.

163:47What do you think?

163:50What's the role of government in a society that thrives?

163:56Is anarchism at all compelling to you as a system?

164:00So like not just small government, but no government at all.

164:04- Yeah, I don't see how this would work.

164:07The government is an agent that imposes an offset

164:12on your reward function, on your payout metrics.

164:15So your behavior becomes compatible with the common good.

164:19- So the argument there is that you can have collectives

164:25like governing organizations, but not government.

164:28Like where you're born on a particular set of land

164:32and therefore you must follow this rule or else.

164:37You're forced by what they call violence

164:41because there's an implied violence here.

164:44So with government, the key aspect of government

164:48is it protects you from the rest of the world with an army

164:54and with police, right?

164:56So it has a monopoly on violence.

165:00It's the only one that's able to do violence.

165:02- So there are many forms of government,

165:03not all governments do that, right?

165:05But we find that in successful countries,

165:09the government has a monopoly on violence.

165:11And that means that you cannot get ahead

165:15by starting your own army because the government

165:17will come down on you and destroy you if you try to do that.

165:20And in countries where you can build your own army

165:23and get away with it, some people will do it, right?

165:25In these countries is what we call failed countries

165:28in a way.

165:30And if you don't want to have violence,

165:33the point is not to appeal to the moral intentions of people

165:36because some people will use strategies

165:39if they get ahead with them that feel a particular kind

165:41of ecological niche.

165:42So you need to destroy that ecological niche.

165:45And if effective government has a monopoly on violence,

165:50it can create a world where nobody is able to use violence

165:53and get ahead, right?

165:54So you want to use that monopoly on violence,

165:57not to exert violence, but to make violence impossible,

166:00to raise the cost of violence.

166:02So people need to get ahead with nonviolent means.

166:06- So the idea is that you might be able to achieve that

166:08in an anarchist state with companies.

166:12So with the forces of capitalism is create security companies

166:18where the one that's most ethically sound

166:20rises to the top, basically,

166:22it would be a much better representative of the people

166:25because there is a less sort of stickiness

166:29to the big military force sticking around,

166:33even though it's long overlived, outlived.

166:36- So you have groups of militants

166:39that are hopefully efficiently organized

166:40because otherwise they're going to lose

166:42against the other groups of militants.

166:44And they are coordinating themselves

166:46with the rest of society

166:48until they are having a monopoly on violence.

166:51How is that different from a government?

166:53So it's basically converging to the same thing.

166:56- So I think it always,

166:57I was trying to argue with Malice,

167:00I feel like it always converges towards government at scale.

167:03But I think the idea is you can have a lot of collectives

167:06that are, you basically never let anything scale too big.

167:11So one of the problems with governments is it gets too big

167:15in terms of the size of the group

167:19over which it has control.

167:21My sense is that would happen anyway.

167:26So a successful company like Amazon or Facebook,

167:30I mean, it starts forming a monopoly

§20Transcript · 2:47:33–2:56:20

167:33over entire populations,

167:36not over just the hundreds of millions,

167:37but billions of people.

167:39So I don't know.

167:41But there is something about the abuses of power,

167:45the government can have

167:46when it has a monopoly on violence.

167:47And so that's a tension there.

167:51- So the question is how can you set the incentives

167:55for government correctly?

167:56And this mostly applies at the highest levels of government.

167:59And because we haven't found a way to set them correctly,

168:02we made the highest levels of government relatively weak.

168:06And this is, I think, part of the reason

168:08why we had difficulty to coordinate the pandemic response.

168:12And China didn't have that much difficulty.

168:14And there is, of course, a much higher risk

168:17of the abuse of power that exists in China

168:19because the power is largely unchecked.

168:22And that's basically what happens in the next generation.

168:26For instance, imagine that we would agree

168:28that the current government of China

168:29is largely correct and benevolent.

168:31And maybe we don't agree on this, but if we did,

168:35how can we make sure that this stays like this?

168:37And if you don't have checks and balances

168:40and division of power, it's hard to achieve.

168:43We don't have a solution for that problem.

168:45But the abolishment of government basically

168:47would remove the control structure.

168:49From a cybernetic perspective,

168:51there is an optimal point in the system

168:54that the regulation should be happening, right?

168:56But you can measure the current incentives

168:59and the regulator would be properly incentivized

169:01to make the right decisions

169:03and change the payout metrics of everything below it

169:06in such a way that the local prisoners' dilemmas

169:08get resolved, right?

169:09You cannot resolve the prisoners' dilemma

169:12without some kind of eternal control

169:14that emulates an infinite game in a way.

169:17- Yeah, I mean, there's a sense in which

169:22it seems like the reason government,

169:24the parts of government that don't work well currently

169:27is because there's not good mechanisms

169:32through which to interact for the citizenry

169:36to interact with government.

169:37It's basically, it hasn't caught up in terms of technology.

169:41And I think once you integrate

169:43some of the digital revolution

169:46of being able to have a lot of access to data,

169:48be able to vote on different ideas at a local level,

169:52at all levels, at the optimal level, like you're saying,

169:55that can resolve the prisoner dilemmas,

169:58and to integrate AI to help you out,

170:00automate things that are like,

170:02that don't require the human ingenuity,

170:06I feel like that's where government could operate that well

170:10and can also break apart the inefficient bureaucracies

170:14if needed.

170:15There'll be a strong incentive

170:16to be efficient and successful.

170:20- So our human history, we see an evolution

170:23and evolutionary competition of modes of government

170:25and of individual governments is in these modes.

170:28And every nation state in some sense

170:29is some kind of organism that has found different solutions

170:33for the problem of government.

170:34And you could look at all these different models

170:37and the different scales at which it exists

170:39as empirical attempts to validate the idea

170:42of how to build a better government.

170:45And I suspect that the idea of anarchism,

170:49similar to the idea of communism,

170:51is the result of being disenchanted

170:54with the ugliness of the real existing solutions

170:57and the attempt to get to an utopia.

171:00And I suspect that communism originally was not a utopia.

171:04I think that in the same way as original Christianity,

171:07it had a particular kind of vision.

171:10And this vision is a society,

171:12a mode of organization within the society

171:15in which humans can coexist at scale without coercion.

171:20The same way as we do in a healthy family, right?

171:23In a good family,

171:24you don't terrorize each other into compliance,

171:28but you understand what everybody needs

171:30and what everybody is able to contribute

171:32and what the intended future of the whole thing is.

171:35And everybody coordinates their behavior in the right way

171:38and informs each other about how to do this.

171:40And all the interactions that happen

171:42are instrumental to making that happen, right?

171:45Could this happen at scale?

171:47And I think this is the idea of communism.

171:49Communism is opposed to the idea

171:51that we need economic terror

171:53or other forms of terror to make that happen.

171:55But in practice, what happened

171:56is that the proto-communist countries,

171:59the real existing socialism,

172:01replaced a part of the economic terror with moral terror.

172:04So we were told to do the right thing for moral reasons.

172:07And of course it didn't really work

172:09and the economy eventually collapsed.

172:11And the moral terror had actual real cost, right?

172:14People were in prison

172:15because they were morally non-compliant.

172:17And the other thing is that the idea of communism

172:23became a utopia.

172:24So it basically was projected into the afterlife.

172:26We were told in my childhood

172:29that communism was a hypothetical society

172:32to which we were in a permanent revolution

172:34that justified everything

172:35that was presently wrong with society morally.

172:38But it was something that our grandchildren

172:40probably would not ever see

172:42because it was too ideal and too far in the future

172:44to make it happen right now.

172:45And people were just not there yet morally.

172:48And the same thing happened with Christianity, right?

172:51This notion of heaven was mythologized

172:53and projected into an afterlife.

172:55And I think this was just the idea of God's kingdom,

172:57of this world in which we instantiate

173:00the next level transcendental agent in the perfect form.

173:03So everything goes smoothly and without violence

173:05and without conflict and without this human messiness

173:08on this economic messiness and the terror and coercion

173:12that existed in the present societies.

173:14And the idea of whether humans can exist at scale

173:17in a harmonious way and non-coercively is untested.

173:21A lot of people tested it,

173:22but didn't get it to work so far.

173:25And the utopia is a world in where you get

173:27all the good things without any of the bad things.

173:30And you are, I think, very susceptible to believe

173:33in utopias when you are very young

173:35and don't understand that everything has to happen

173:38in causal patterns, that there's always feedback loops

173:40that ultimately are closed.

173:42There's nothing that just happens because it's good or bad.

173:45Good or bad don't exist in isolation.

173:47They only exist with respect to larger systems.

173:50- So can you intuit why utopias fail as systems?

173:55So like having a utopia that's out there

174:00beyond the horizon, is it because then,

174:02so it's not only because it's impossible to achieve utopias,

174:08but it's because what certain humans,

174:11certain small number of humans start to

174:15sort of greedily attain power and money

174:22and control and influence as they become,

174:28as they see the power in using this idea

174:33of a utopia for propaganda.

174:34- That's a bit like saying, why is my garden not perfect?

174:37It's because some evil weeds are overgrowing it

174:39and they always do.

174:40- Yeah.

174:41- But this is not how it works.

174:43A good garden is a system that is in balance

174:45and requires minimal interactions by the gardener.

174:48And so you need to create a system

174:51that is designed to self-stabilize.

174:54And the design of social systems requires

174:56not just the implementation of the desired functionality,

174:58but the next level design, also in biological systems.

175:01You need to create a system that wants to converge

175:04to the intended function.

175:06And so instead of just creating an institution

175:08like the FDA that is performing a particular kind of role

175:11in society, you need to make sure that the FDA

175:15is actually driven by a system that wants

175:16to do this optimally, that is incentivized

175:19to do it optimally and then makes the performance

175:22that is actually enacted in every generation

175:24instrumental to that thing, that actual goal, right?

175:27And that is much harder to design and to achieve.

175:30- So you have to design a system where,

175:32I mean, listen, communism also was quote unquote

175:35incentivized to be a feedback loop system

175:40that achieves that utopia.

175:43It's just, it wasn't working given human nature.

175:45The incentives were not correct given human nature.

175:48- So how do you incentivize people

175:50when they are getting coal off the ground

175:52to work as hard as possible?

175:53Because it's a terrible job

175:55and it's very bad for your health.

175:57And right, how do you do this?

175:59And you can give them prices and metals

176:02and status to some degree, right?

176:04There's only so much status to give for that.

176:06And most people will not fall for this, right?

176:09Or you can pay them and you probably have to pay them

176:12in an asymmetric way because if you pay everybody the same

176:15and you nationalize the coal mines,

176:19eventually people will figure out

176:20that they can game the system.

§21Transcript · 2:56:21–3:04:38

176:21- Yes, so you're describing capitalism.

176:25So capitalism is the present solution to the system.

176:28And what we also noticed that I think that Marx was correct

176:32in saying that capitalism is prone to crisis,

176:35that capitalism is a system that in its dynamics

176:38is not convergent, but divergent.

176:40It's not a stable system.

176:42And that eventually it produces an enormous potential

176:47for productivity, but it also is systematically

176:50misallocating resources.

176:52So a lot of people cannot participate in the production

176:55and consumption anymore.

176:57And this is what we observe.

176:58We observe that the middle class in the US is tiny.

177:01A lot of people think that they're middle class,

177:05but if you are still flying economy,

177:07you're not middle class.

177:08Every class is a magnitude smaller than the previous class.

177:14(laughing)

177:16- I think about classes, it's really like airline classes.

177:23(laughing)

177:24- A lot of people are economy class,

177:25a lot of people are economy class.

177:26- Have we really-

177:27- Business class and very few are first class

177:30and some are budget.

177:30- I mean, I understand.

177:32I think there is a, yeah, maybe some people,

177:37probably I would push back against that definition

177:39of the middle class.

177:40It does feel like the middle class is pretty large,

177:41but yes, there's a discrepancy in terms of wealth.

177:44There's a big wealth gap.

177:46- So if you think about in terms of the productivity

177:48that our society could have,

177:50there is no reason for anybody to fly economy.

177:54We would be able to let everybody travel in style.

177:57- Well, but also some people like to be frugal

178:00even when they're billionaires.

178:01Okay, so let's take that into account.

178:03- Yes, but I mean, we probably don't need

178:05to be traveling lavish,

178:07but you also don't need to be tortured, right?

178:09There is a difference between frugal

178:11and subjecting yourself to torture.

178:14- Listen, I love economy.

178:15I don't understand why you're comparing

178:16a flying economy to torture.

178:19I don't, although the fight here,

178:22there's two crying babies next to me.

178:24So that, but that has nothing to do with the calm

178:26as to do with crying babies.

178:28They're very cute though.

178:29So they kind of-

178:30- Yeah, I have two kids and sometimes I have to go back

178:32to visit the grandparents.

178:35And that means going from the West Coast to Germany

178:40and it's a long flight.

178:42- Is it true that sort of when you're a father,

178:45you grow immune to the crying and all that kind of stuff?

178:48Like, because like me just not having kids,

178:52it can be other people's kids can be quite annoying

178:54when they're crying and screaming

178:55and all that kind of stuff.

178:57- When you have children and you're wired up

178:59in the default natural way, you're lucky in this regard,

179:02you fall in love with them.

179:04And this falling in love with them means

179:07that you basically start to see the world

179:09through their eyes and you understand

179:11that in a given situation, they cannot do anything

179:13but being expressing despair.

179:17And so it becomes more differentiated.

179:19I noticed that for instance,

179:21my son is typically acting on pure experience

179:25of what things are like right now.

179:28And he has to do this right now.

179:30And you have this small child that is,

179:32when he was a baby and so on,

179:35where he was just immediately expressing what he felt.

179:37And if you cannot regulate this from the outside,

179:40there's no point to be upset about it, right?

179:42It's like dealing with weather or something like this.

179:45You all have to get through it.

179:46And it's not easy for him either.

179:48But if you also have a daughter,

179:51maybe she is planning for that.

179:53Maybe she understands that she's sitting

179:57in the car behind you and she's screaming

179:58at the top of her lungs and you're almost doing an accident

180:01and you really don't know what to do.

180:03What should I have done to make you stop screaming?

180:06You could have given me candy.

180:08(laughing)

180:10- I think that's like a cat versus dog discussion.

180:12I love it.

180:13'Cause you said like a fundamental aspect

180:17of that is love that makes it all worth it.

180:21What in this monkey riding an elephant in a dream world,

180:27what role does love play in the human condition?

180:30- I think that love is the facilitator

180:34of non-transactional interaction.

180:36When you are observing your own purposes,

180:41some of these purposes go beyond your ego.

180:43They go beyond the particular organism that you are

180:46and your local interests.

180:47- That's what you mean by non-transactional.

180:49- Yes, so basically when you are acting

180:50in a transactional way,

180:51it means that you are expecting something in return for you

180:54from the one that you're interacting with.

180:58You are interacting with a random stranger.

181:00You buy something from them on eBay.

181:01You expect a fair value for the money that you sent them

181:04and vice versa because you don't know that person.

181:07You don't have any kind of relationship to them.

181:09But when you know this person a little bit better

181:11and you know the situation that they're in

181:13and you understand what they're trying to achieve

181:14in their life and you approve because you realize

181:17that they're in some sense serving

181:19the same human sacredness as you are.

181:22And they need to think that you have.

181:24Maybe you give it to them as a present.

181:26- But I mean, the feeling itself of joy

181:30is a kind of benefit, is a kind of transaction.

181:34- Yes, but the joy is not the point.

181:36The joy is the signal that you get.

181:38It's the reinforcement signal that your brain sends to you

181:41because you are acting on the incentives

181:44of the agent that you're a part of.

181:46We are meant to be part of something larger.

181:48This is the way in which we out-competed other hominins.

181:51- Take that Neanderthals.

181:56- Yeah, right.

181:57And also other humans.

181:59There was a population bottleneck for human society

182:03that leads to an extreme lack

182:06of genetic diversity among humans.

182:07If you look at Bushmen in the Kalahari,

182:11that basically tribes that are not that far distant

182:14to each other have more genetic diversity

182:16than exists between Europeans and Chinese.

182:19And it's because basically the out-of-Africa population

182:23at some point had a bottleneck

182:25of just a few thousand individuals.

182:28And what probably happened is not that at any time

182:31the number of people shrunk below a few hundred thousand.

182:35What probably happened is that there was a small group

182:37that had a decisive mutation that produced an advantage.

182:40And this group multiplied and killed everybody else.

182:44And we are descendants of that group.

182:46- Yeah, I wonder what the peculiar characteristics

182:50of that group.

182:52- Yeah.

182:53- I mean, we can never know.

182:53- Me too, and a lot of people do.

182:55- We can only just listen to the echoes in our,

182:58like the ripples that are still within us.

183:01- So I suspect what eventually made a big difference

183:04was the ability to organize at scale,

183:07be able to program each other.

183:10- With ideas.

183:11- That we became programmable,

183:12that we are willing to work in lockstep,

183:14that we went below, above the tribal level,

183:17that we no longer were groups of a few hundred individuals

183:20and acted on direct reputation systems transactionally,

183:24but that we basically evolved an adaptation

183:27to become state building.

183:29- Yeah.

183:29To form collectives outside of the direct collectives.

183:35- Yes, and that's basically a part of us

183:37became committed to serving something outside

183:40of what we know. - Bigger than ourselves.

183:41Yeah, then that's kind of what love is.

183:44- And it's terrifying because it meant

183:45that we eradicated the others.

183:47It's a force, it's an adaptive force

183:50that gets us ahead in evolution,

183:52which means we displace something else

183:54that doesn't have that.

183:55- Oh, so we had to murder a lot of people

183:58that weren't about love.

184:00So love led to destruction.

184:01- We didn't have the same strong love as we did.

184:04Right, that's why I mentioned this thing with fascism

184:07and you see this, these speeches,

184:10do you want total war?

184:12And everybody says, yes, right?

184:14There's this big, oh my God, be a part of something

184:17that is more important than me

184:18that gives meaning to my existence.

184:20(laughing)

184:22- Fair enough.

184:23Do you have advice for young people today

184:30in high school, in college,

184:33that are thinking about what to do

184:36with their career, with their life,

184:38so that at the end of the whole thing,

§22Transcript · 3:04:40–3:12:15

184:40they can be proud of what they did?

184:42- Don't cheat.

184:44Have integrity, aim for integrity.

184:48- So what does integrity look like

184:49when you're at the river or at the leaf

184:51or the fat frog in a lake?

184:53- It basically means that you try to figure out

184:57what the thing is that is the most right.

185:02And this doesn't mean that you have to look

185:04for what other people tell you what's right,

185:07but you have to aim for moral autonomy.

185:09So things need to be right

185:11independently of what other people say.

185:14I always felt that when people told me

185:17to listen to what others say,

185:20like read the room, build your ideas

185:24of what's true based on the high status people

185:26of your in-group, that does not protect me from fascism.

185:29The only way to protect yourself from fascism

185:31is to decide it's the world that is being built here,

185:35the world that I want to be in.

185:37In some sense, try to make your behavior sustainable,

185:41act in such a way that you would feel comfortable

185:44on all sides of the transaction.

185:46Realize that everybody is you in a different timeline,

185:48but is seeing things differently and has reasons to do so.

185:52- Yeah, I've come to realize this recently,

185:58that there is an inner voice

185:59that tells you what's right and wrong.

186:02And speaking of reading the room,

186:06there's times what integrity looks like

186:08is there's times when a lot of people

186:10are doing something wrong.

186:12And what integrity looks like is not going on Twitter

186:14and tweeting about it, but not participating quietly,

186:19not doing, so it's not like signaling

186:21or not all this kind of stuff,

186:24but actually living your, what you think is right.

186:27Like living it, not signaling.

186:29- There's also sometimes this expectation

186:30that others are like us.

186:32So imagine the possibility that some of the people

186:35around you are space aliens that only look human.

186:38So they don't have the same prayers as you do.

186:41They don't have the same impulses,

186:44that's what's right and wrong.

186:45There's a large diversity in these basic impulses

186:48that people can have in a given situation.

186:51And now realize that you are a space alien.

186:54You are not actually human.

186:55You think that you're human,

186:57but you don't know what it means,

186:58like what it's like to be human.

187:00You just make it up as you go along like everybody else.

187:03And you have to figure that out,

187:05what it means that you are a full human being,

187:09what it means to be human in the world

187:11and how to connect with others on that.

187:13And there's also something, don't be afraid

187:17in the sense that if you do this, you're not good enough.

187:20Because if you are acting on these incentives of integrity,

187:23you become trustworthy.

187:25That's the way in which you can recognize each other.

187:28There is a particular place where you can meet

187:30and you can figure out what that place is,

187:33where you will give support to people

187:35because you realize that they act with integrity

187:38and they will also do that.

187:40So in some sense, you are safe if you do that.

187:42You're not always protected.

187:44There are people which will abuse you

187:47and that are bad actors in a way that it's hard to imagine

187:51before you meet them.

187:52But there is also people which will try to protect you.

187:57- Yeah, thank you for saying that.

188:00That's such a hopeful message

188:03that no matter what happens to you,

188:05there'll be a place,

188:06there's people you'll meet

188:10that also have what you have

188:15and you will find happiness there and safety there.

188:20- Yeah, but it doesn't need to end well.

188:21It can also all go wrong.

188:23So there's no guarantees in this life.

188:26So you can do everything right and you still can fail

188:29and you can still horrible things happening to you

188:32that traumatize you and mutilate you

188:35and you have to be grateful if it doesn't happen.

188:37- And ultimately be grateful no matter what happens

188:43'cause even just being alive is pretty damn nice.

188:45- Yeah, even that, you know.

188:49The gratefulness in some sense is also just generated

188:52by your brain to keep you going.

188:54It's all a trick.

188:57- Speaking of which, Kamu said,

189:01"I see many people die because they judge

189:05that life is not worth living.

189:08I see others paradoxically getting killed

189:10for the ideas or illusions

189:12that give them a reason for living.

189:15What is called the reason for living

189:16is also an excellent reason for dying.

189:19I therefore conclude that the meaning of life

189:22is the most urgent of questions."

189:24So I have to ask what,

189:27Jasha Bach, is the meaning of life?

189:30It is an urgent question according to Kamu.

189:33- I don't think that there's a single answer to this.

189:37Nothing makes sense unless the mind makes it so.

189:41So you basically have to project a purpose.

189:44And if you zoom out far enough,

189:47there's the heat test of the universe

189:49and everything is meaningless.

189:50Everything is just a blip in between.

189:52And the question is,

189:53do you find meaning in this blip in between?

189:55Do you find meaning in observing squirrels?

189:59Do you find meaning in raising children

190:01and projecting a multi-generational organism

190:04into the future?

190:05Do you find meaning in projecting an aesthetic

190:08of the world that you like into the future

190:10and trying to serve that aesthetic?

190:12And if you do, then life has that meaning.

190:15And if you don't, then it doesn't.

190:18- I kind of enjoy the idea that you just create

190:21the most vibrant, the most weird,

190:25the most unique kind of blip you can,

190:28given your environment, given your set of skills.

190:32Just be the most weird set of,

190:36like local pocket of complexity you can be.

190:41So that like when people study the universe,

190:44they'll pause and be like, "Uh, that's weird."

190:48- That looks like a useful strategy,

190:50but of course it's still motivated reasoning.

190:52(laughing)

190:55You're obviously acting on your incentives here.

190:57- It's still a story we tell ourselves within a dream

191:00that's hardly in touch with reality.

191:03- It's definitely a good strategy if you are a podcaster.

191:06(laughing)

191:08- And a human, which I'm still trying to figure out if I am.

191:13- Yeah, there's a mutual relationship somehow.

191:15- Somehow.

191:16- Josh, you're one of the most incredible people I know.

191:20I really love talking to you.

191:22I love talking to you again.

191:23And it's really an honor that you spend

191:26your valuable time with me.

191:27I hope we get to talk many times

191:28throughout our short and meaningless lives.

191:33- Or meaningful.

191:34- Or meaningful.

191:35- Thank you, Alex.

191:36I enjoyed this conversation very much.

191:39- Thanks for listening to this conversation with Josip Bach.

191:41A thank you to Coinbase, Codecademy, Linode,

191:45NetSuite, and ExpressVPN.

191:48Check them out in the description to support this podcast.

191:52Now, let me leave you with some words from Carl Jung.

191:55"People will do anything, no matter how absurd,

191:58"in order to avoid facing their own souls.

192:01"One does not become enlightened

192:03"by imagining figures of light,

192:05"but by making the darkness conscious."

192:09Thank you for listening, and hope to see you next time.

192:12(upbeat music)

192:15(upbeat music)

— A LITTLE MORE TO THINK ABOUT. —