David Ferrucci: IBM Watson, Jeopardy & Deep Conversations with AI
嘉宾：David Ferrucci
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本篇目录17 章节
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The following is a conversation with David Ferrucci.
He led the team that built Watson,
the IBM question answering system
that beat the top humans in the world
at the game of Jeopardy.
For spending a couple hours with David,
I saw a genuine passion,
not only for abstract understanding of intelligence,
but for engineering it to solve real world problems
under real world deadlines and resource constraints.
Where science meets engineering
is where brilliant, simple ingenuity emerges.
People who work at joining it to
have a lot of wisdom earned
through failures and eventual success.
David is also the founder, CEO,
and chief scientist of Elemental Cognition,
a company working to engineer AI systems
that understand the world the way people do.
This is the Artificial Intelligence Podcast.
If you enjoy it, subscribe on YouTube,
give it five stars on iTunes,
support it on Patreon,
or simply connect with me on Twitter
at Lex Friedman, spelled F-R-I-D-M-A-N.
And now, here's my conversation with David Ferrucci.
Your undergrad was in biology
with an eye toward medical school
before you went on for the PhD in computer science.
So let me ask you an easy question.
What is the difference between biological systems
and computer systems?
When you sit back, look at the stars,
and think philosophically.
- I often wonder whether or not
there is a substantive difference.
I mean, I think the thing that got me
into computer science,
into artificial intelligence,
was exactly this presupposition that
if we can get machines to think,
or I should say this question,
this philosophical question,
if we can get machines to think,
to understand, to process information the way we do,
so if we can describe a procedure,
describe a process,
even if that process were the intelligence process itself,
then what would be the difference?
So from a philosophical standpoint,
I'm not sure I'm convinced that there is.
I mean, you can go in the direction of spirituality,
you can go in the direction of the soul,
but in terms of what we can experience
from an intellectual and physical perspective,
I'm not sure there is.
Clearly, there are different implementations,
but if you were to say,
is a biological information processing system
fundamentally more capable
than one we might be able to build out of silicon
or some other substrate,
I don't know that there is.
- How distant do you think is the biological implementation?
So fundamentally, they may have the same capabilities,
but is it really a far mystery
where a huge number of breakthroughs are needed
to be able to understand it,
or is it something that, for the most part,
in the important aspects,
echoes of the same kind of characteristics?
- Yeah, that's interesting.
So your question presupposes that there's this goal
to recreate what we perceive as biological intelligence.
I'm not sure that's how I would state the goal.
I mean, I think that's--
- What is the goal?
- Good, so I think there are a few goals.
I think that understanding the human brain
and how it works is important
for us to be able to diagnose and treat issues
for us to understand our own strengths and weaknesses,
both intellectual, psychological, and physical.
So neuroscience and understanding the brain
from that perspective, there's a clear goal there.
From the perspective of saying,
I wanna mimic human intelligence,
that one's a little bit more interesting.
Human intelligence certainly has a lot of things we envy.
It's also got a lot of problems, too.
So I think we're capable of sort of stepping back
and saying, what do we want out of an intelligence?
How do we wanna communicate with that intelligence?
How do we want it to behave?
How do we want it to perform?
Now, of course, it's somewhat of an interesting argument
because I'm sitting here as a human with a biological brain
and I'm critiquing the strengths and weaknesses
of human intelligence and saying that we have
the capacity to step back and say,
gee, what is intelligence and what do we really want
out of it, and that in and of itself suggests
that human intelligence is something quite enviable,
that it can introspect that way.
- And the flaws, you mentioned the flaws.
So humans have flaws.
- Yeah, I think that flaws that human intelligence has
is extremely prejudicial and biased
in the way it draws many inferences.
- Do you think those are, sorry to interrupt,
do you think those are features or are those bugs?
Do you think the prejudice, the forgetfulness, the fear,
what are the flaws?
List them all.
What, love?
Maybe that's a flaw.
You think those are all things that can be,
get in the way of intelligence
or the essential components of intelligence?
- Well, again, if you go back and you define intelligence
as being able to sort of accurately, precisely,
rigorously reason, develop answers,
and justify those answers in an objective way,
yeah, then human intelligence has these flaws
in that it tends to be more influenced
by some of the things you said.
And it's largely an inductive process,
meaning it takes past data, uses that to predict the future,
very advantageous in some cases,
but fundamentally biased and prejudicial in other cases,
'cause it's gonna be strongly influenced
by its priors, whether they're right or wrong
from some objective reasoning perspective,
you're gonna favor them because those are the decisions
or those are the paths that succeeded in the past.
And I think that mode of intelligence makes a lot of sense
for when your primary goal is to act quickly
and survive and make fast decisions.
And I think those create problems
when you wanna think more deeply
and make more objective and reasoned decisions.
Of course, human's capable of doing both.
They do sort of one more naturally than they do the other,
but they're capable of doing both.
- You're saying they do the one
that responds quickly more naturally.
- Right.
- 'Cause that's the thing we kinda need
to not be eaten by the predators in the world.
- For example, but then we've learned to reason
through logic, we've developed science,
we've trained people to do that.
I think that's harder for the individual to do.
I think it requires training and teaching.
I think we are, the human mind certainly is capable of it,
but we find it more difficult.
And then there are other weaknesses, if you will,
as you mentioned earlier, just memory capacity
and how many chains of inference can you actually go through
without losing your way, so just focus.
- So the way you think about intelligence,
and we're really sort of floating
in this philosophical space,
but I think you're the perfect person to talk about this
because we'll get to Jeopardy and beyond.
That's one of the most incredible accomplishments in AI,
in the history of AI,
but hence the philosophical discussion.
So let me ask, you've kind of alluded to it,
but let me ask again, what is intelligence?
Underlying the discussion we'll have
with Jeopardy and beyond.
How do you think about intelligence?
Is it a sufficiently complicated problem,
being able to reason your way through solving that problem?
Is that kind of how you think about
what it means to be intelligent?
- So I think of intelligence primarily two ways.
One is the ability to predict.
So in other words, if I have a problem,
can I predict what's gonna happen next,
whether it's to predict the answer of a question
or to say, look, I'm looking at all the market dynamics
and I'm gonna tell you what's gonna happen next,
or you're in a room and somebody walks in
and you're gonna predict what they're gonna do next
or what they're gonna say next.
- In a highly dynamic environment full of uncertainty,
be able to predict.
- The more variables, the more complex.
The more possibilities, the more complex.
But can I take a small amount of prior data
and learn the pattern and then predict
what's gonna happen next accurately and consistently?
That's certainly a form of intelligence.
- What do you need for that, by the way?
You need to have an understanding of the way the world works
in order to be able to unroll it into the future.
What do you think is needed to predict?
- Depends what you mean by understanding.
§02Transcript · 09:30–18:39
I need to be able to find that function.
This is very much what deep learning does,
machine learning does, is if you give me enough prior data
and you tell me what the output variable is that matters,
I'm gonna sit there and be able to predict it.
And if I can predict it accurately
so that I can get it right more often than not, I'm smart.
If I can do that with less data and less training time,
I'm even smarter.
If I can figure out what's even worth predicting,
I'm smarter, meaning I'm figuring out
what path is gonna get me toward a goal.
- What about picking a goal?
Sorry, you're up again.
- Well, that's interesting about picking a goal.
Sort of an interesting thing.
I think that's where you bring in
what are you pre-programmed to do?
We talk about humans and, well,
humans are pre-programmed to survive.
So sort of their primary driving goal,
what do they have to do to do that?
And that can be very complex, right?
So it's not just figuring out
that you need to run away from the ferocious tiger,
but we survive in a social context as an example.
So understanding the subtleties of social dynamics
becomes something that's important for surviving,
finding a mate, reproducing, right?
So we're continually challenged
with complex sets of variables, complex constraints,
rules, if you will, or patterns.
And we learn how to find the functions
and predict the things.
In other words, represent those patterns efficiently
and be able to predict what's gonna happen.
And that's a form of intelligence.
That doesn't really require anything specific
other than the ability to find that function
and predict that right answer.
It's certainly a form of intelligence.
But then when we say, well, do we understand each other?
In other words, would you perceive me as intelligent
beyond that ability to predict?
So now I can predict, but I can't really articulate
how I'm going through that process,
what my underlying theory is for predicting.
And I can't get you to understand what I'm doing
so that you can follow,
you can figure out how to do this yourself
if you did not have, for example,
the right pattern-managing machinery that I did.
And now we potentially have this breakdown
where in effect, I'm intelligent,
but I'm sort of an alien intelligence relative to you.
- You're intelligent, but nobody knows about it.
Or I can't-- - Well, I can see the output.
- So you're saying, let's sort of separate the two things.
One is you explaining why you were able
to predict the future.
And the second is me being able to,
impressing me that you're intelligent.
Me being able to know
that you successfully predicted the future.
Do you think that's--
- Well, it's not impressing you that I'm intelligent.
In other words, you may be convinced
that I'm intelligent in some form.
Because of my ability to predict.
- So I would look at the metrics.
- When you pass, I say, wow, you're right more times
than I am, you're doing something interesting.
That's a form of intelligence.
But then what happens is, if I say, how are you doing that?
And you can't communicate with me,
and you can't describe that to me,
now I may label you a savant.
I may say, well, you're doing something weird,
and it's just not very interesting to me,
because you and I can't really communicate.
And so now, so this is interesting, right?
Because now this is, you're in this weird place
where for you to be recognized as intelligent
the way I'm intelligent, then you and I
sort of have to be able to communicate.
And then we start to understand each other,
and then my respect and my appreciation,
my ability to relate to you starts to change.
So now you're not an alien intelligence anymore,
you're a human intelligence now,
because you and I can communicate.
And so I think when we look at animals, for example,
animals can do things we can't quite comprehend,
we don't quite know how they do them,
but they can't really communicate with us.
They can't put what they're going through in our terms.
And so we think of them as sort of,
well, they're these alien intelligences,
and they're not really worth necessarily what we're worth,
we don't treat them the same way as a result of that.
But it's hard, because who knows what's going on.
- So just a quick elaboration on that,
the explaining that you're intelligent,
explaining the reasoning that went into the prediction
is not some kind of mathematical proof.
If we look at humans, look at political debates
and discourse on Twitter, it's mostly just telling stories.
So your task is, sorry, your task is not to tell
an accurate depiction of how you reason,
but to tell a story, real or not,
that convinces me that there was a mechanism
by which you--
- Ultimately, that's what a proof is.
I mean, even a mathematical proof is that.
Because ultimately, the other mathematicians
have to be convinced by your proof.
Otherwise, in fact, there have been--
- That's the metric of success, yeah.
- Yeah, there have been several proofs out there
where mathematicians would study for a long time
before they were convinced
that it actually proved anything, right?
You never know if it proved anything
until the community mathematicians decided that it did.
So, I mean, but it's a real thing, right?
And that's sort of the point, right?
Is that ultimately, this notion of understanding us,
understanding something is ultimately a social concept.
In other words, I have to convince enough people
that I did this in a reasonable way.
I did this in a way that other people can understand
and replicate and that it makes sense to them.
So, human intelligence is bound together in that way.
We're bound up in that sense.
We sort of never really get away with it
until we can sort of convince others
that our thinking process makes sense.
- Did you think the general question of intelligence
is then also a social construct?
So, if we ask questions
of an artificial intelligence system,
is this system intelligent?
The answer will ultimately be a socially constructed concept.
- So, I think, I'm making two statements.
I'm saying we can try to define intelligence
in this super objective way that says,
here's this data, I wanna predict this type of thing,
learn this function, and then if you get it right,
often enough, we consider you intelligent.
- But that's more like a savant.
- I think it is.
It doesn't mean it's not useful.
It could be incredibly useful.
It could be solving a problem we can't otherwise solve
and can solve it more reliably than we can.
But then there's this notion of,
can humans take responsibility
for the decision that you're making?
Can we make those decisions ourselves?
Can we relate to the process that you're going through?
And now, you as an agent,
whether you're a machine or another human, frankly,
are now obliged to make me understand
how it is that you're arriving at that answer
and allow me, me or obviously a community
or a judge of people to decide whether or not
that makes sense.
And by the way, that happens with humans as well.
You're sitting down with your staff, for example,
and you ask for suggestions about what to do next,
and someone says, oh, I think you should buy,
and I think you should buy this much,
or whatever, or sell, or whatever it is,
or I think you should launch the product today or tomorrow,
or launch this product versus that product,
whatever the decision may be, and you ask why,
and the person says, I just have a good feeling about it.
And you're not very satisfied.
Now, that person could be, you might say,
well, you've been right before,
but I'm gonna put the company on the line.
Can you explain to me why I should believe this?
- And that explanation may have nothing to do
with the truth.
- You just gotta convince the other person.
It could still be wrong.
It could still be wrong.
- It's just gotta be convincing.
- But it's ultimately gotta be convincing.
And that's why I'm saying we're bound together.
Our intelligences are bound together in that sense.
We have to understand each other.
And if, for example, you're giving me an explanation,
I mean, this is a very important point,
you're giving me an explanation,
and I'm not good,
and then I'm not good at reasoning well
and being objective and following logical paths
and consistent paths,
and I'm not good at measuring
§03Transcript · 18:41–27:55
and sort of computing probabilities across those paths,
what happens is collectively,
we're not gonna do well.
- How hard is that problem, the second one?
So I think we'll talk quite a bit about the first
on a specific objective metric benchmark performing well.
But being able to explain the steps, the reasoning,
how hard is that problem?
- I think that's very hard.
I mean, I think that that's,
well, it's hard for humans.
- The thing that's hard for humans, as you know,
may not necessarily be hard for computers
and vice versa.
So, sorry, so how hard is that problem for computers?
- I think it's hard for computers,
and the reason why I related to,
or saying that it's also hard for humans
is because I think when we step back
and we say we wanna design computers to do that,
one of the things we have to recognize
is we're not sure how to do it well.
I'm not sure we have a recipe for that,
and even if you wanted to learn it,
it's not clear exactly what data we use
and what judgments we use to learn that well.
And so what I mean by that is,
if you look at the entire enterprise of science,
science is supposed to be about
objective reasoning reason, right?
So we think about, gee, who's the most intelligent person
or group of people in the world?
Do we think about the savants who can close their eyes
and give you a number?
We think about the think tanks,
or the scientists or the philosophers
who kinda work through the details and write the papers
and come up with the thoughtful, logical proofs
and use the scientific method,
and I think it's the latter.
And my point is that, how do you train someone to do that?
And that's what I mean by it's hard.
What's the process of training people to do that well?
That's a hard process.
We work, as a society, we work pretty hard
to get other people to understand our thinking
and to convince them of things.
Now we could persuade them,
obviously we talked about this,
like human flaws or weaknesses,
we can persuade them through emotional means,
but to get them to understand and connect to
and follow a logical argument is difficult.
We try it, we do it as scientists,
we try to do it as journalists,
we try to do it as even artists in many forms,
as writers, as teachers.
We go through a fairly significant training process
to do that, and then we could ask,
well, why is that so hard?
But it's hard, and for humans, it takes a lot of work.
And when we step back and say,
well, how do we get a machine to do that?
It's a vexing question.
- How would you begin to try to solve that?
And maybe just a quick pause,
because there's an optimistic notion
in the things you're describing,
which is being able to explain something through reason.
But if you look at algorithms that recommend things
that we'll look at next,
whether it's Facebook, Google,
advertisement-based companies,
you know, their goal is to convince you to buy things
based on anything.
So that could be reason,
'cause the best of advertisement
is showing you things that you really do need
and explain why you need it.
But it could also be through emotional manipulation.
The algorithm that describes why a certain reason,
a certain decision was made,
how hard is it to do it through emotional manipulation?
And why is that a good or a bad thing?
So you've kind of focused on reason, logic,
really showing in a clear way why something is good.
One, is that even a thing that us humans do?
And two, how do you think of the difference
in the reasoning aspect and the emotional manipulation?
- So you call it emotional manipulation,
but more objectively, it's essentially saying,
there are certain features of things
that seem to attract your attention.
I mean, it kind of give you more of that stuff.
- Manipulation is a bad word.
- Yeah, I mean, I'm not saying it's right or wrong.
It works to get your attention,
and it works to get you to buy stuff.
And when you think about algorithms
that look at the patterns of features
that you seem to be spending your money on,
and say, I'm gonna give you something
with a similar pattern,
so I'm gonna learn that function,
because the objective is to get you to click on it
or get you to buy it or whatever it is.
I don't know, I mean, it is what it is.
I mean, that's what the algorithm does.
You can argue whether it's good or bad.
It depends what your goal is.
- I guess this seems to be very useful for convincing,
for telling a story.
- For convincing humans, it's good,
because again, this goes back to,
what is the human behavior like?
How does the human brain respond to things?
I think there's a more optimistic view of that, too,
which is that if you're searching
for certain kinds of things,
you've already reasoned that you need them.
And these algorithms are saying, look, that's up to you
to reason whether you need something or not.
That's your job.
You may have an unhealthy addiction to this stuff,
or you may have a reasoned and thoughtful explanation
for why it's important to you,
and the algorithms are saying, hey, that's whatever.
That's your problem.
All I know is you're buying stuff like that,
you're interested in stuff like that.
Could be a bad reason, could be a good reason.
That's up to you.
I'm gonna show you more of that stuff.
And I think that it's not good or bad.
It's not reasoned or not reasoned.
The algorithm is doing what it does,
which is saying, you seem to be interested in this.
I'm gonna show you more of that stuff.
And I think we're seeing this not just in buying stuff,
but even in social media.
You're reading this kind of stuff.
I'm not judging on whether it's good or bad.
I'm not reasoning at all.
I'm just saying, I'm gonna show you other stuff
with similar features.
And that's it, and I wash my hands from it,
and I say, that's all that's going on.
- People are so harsh on AI systems.
So one, the bar of performance is extremely high,
and yet we also ask them to, in the case of social media,
to help find the better angels of our nature
and help make a better society.
So what do you think about the role of AI?
- So that, I agree with you.
That's the interesting dichotomy, right?
Because on one hand, we're sitting there
and we're sort of doing the easy part,
which is finding the patterns.
We're not building, the system's not building a theory
that is consumable and understandable by other humans
that can be explained and justified.
And so on one hand to say, oh, AI is doing this.
Why isn't it doing this other thing?
Well, this other thing's a lot harder.
And it's interesting to think about why it's harder.
And because you're interpreting the data
in the context of prior models.
In other words, understandings of what's important
in the world, what's not important.
What are all the other abstract features
that drive our decision-making?
What's sensible, what's not sensible, what's good,
what's bad, what's moral, what's valuable, what isn't?
Where is that stuff?
No one's applying the interpretation.
So when I see you clicking on a bunch of stuff
and I look at these simple features, the raw features,
the features that are there in the data,
like what words are being used,
or how long the material is,
or other very superficial features,
what colors are being used in the material.
Like I don't know why you're clicking
on the stuff you're looking.
Or if it's products, what the price is,
or what the category is, and stuff like that.
And I just feed you more of the same stuff.
That's very different than kind of getting in there
and saying, what does this mean?
The stuff you're reading, like why are you reading it?
What assumptions are you bringing to the table?
Are those assumptions sensible?
Does the material make any sense?
Does it lead you to thoughtful, good conclusions?
Again, there's interpretation and judgment involved
in that process that isn't really happening in the AI today.
That's harder because you have to start getting
at the meaning of the stuff, of the content.
You have to get at how humans interpret the content
relative to their value system
§04Transcript · 27:58–36:46
and deeper thought processes.
- So that's what meaning means,
is not just some kind of deep, timeless, semantic thing
that the statement represents,
but also how a large number of people
are likely to interpret.
So it's again, even meaning is a social construct.
So you have to try to predict how most people
would understand this kind of statement.
- Yeah, meaning is often relative,
but meaning implies that the connections
go beneath the surface of the artifacts.
If I show you a painting, it's a bunch of colors on a canvas,
what does it mean to you?
And it may mean different things to different people
because of their different experiences.
It may mean something even different
to the artist who painted it.
As we try to get more rigorous with our communication,
we try to really nail down that meaning.
So we go from abstract art to precise mathematics,
precise engineering drawings and things like that.
We're really trying to say,
I wanna narrow that space of possible interpretations
because the precision of the communication
ends up becoming more and more important.
And so that means that I have to specify,
and I think that's why this becomes really hard,
because if I'm just showing you an artifact
and you're looking at it superficially,
whether it's a bunch of words on a page
or whether it's brushstrokes on a canvas
or pixels in a photograph,
you can sit there and you can interpret
lots of different ways at many, many different levels.
But when I wanna align our understanding of that,
I have to specify a lot more stuff
that's actually not directly in the artifact.
Now I have to say, well, how are you interpreting
this image and that image?
And what about the colors and what do they mean to you?
What perspective are you bringing to the table?
What are your prior experiences with those artifacts?
What are your fundamental assumptions and values?
What is your ability to kind of reason
to chain together logical implication
as you're sitting there and saying,
well, if this is the case, then I would conclude this.
If that's the case, then I would conclude that.
So your reasoning processes and how they work,
your prior models and what they are,
your values and your assumptions,
all those things now come together into the interpretation.
Getting in sync of that is hard.
- And yet humans are able to intuit some of that
without any pre--
- Because they have the shared experience.
- And we're not talking about shared,
two people having a shared experience.
I mean, as a society-- - That's correct.
- We have the shared experience and we have similar brains.
So we tend to, in other words,
part of our shared experience
is our shared local experience.
Like we may live in the same culture,
we may live in the same society,
and therefore we have similar educations.
We have similar, what we like to call prior models
about the prior experiences.
And we use that as a,
think of it as a wide collection of interrelated variables
and they're all bound to similar things.
And so we take that as our background
and we start interpreting things similarly.
But as humans, we have a lot of shared experience.
We do have similar brains, similar goals,
similar emotions under similar circumstances
because we're both humans.
So now one of the early questions you asked,
how is biological and computer information systems
fundamentally different?
Well, one is humans come with a lot of pre-programmed stuff,
a ton of program stuff,
and they're able to communicate
because they have a lot of,
because they share that stuff.
- Do you think that shared knowledge,
if we can maybe escape the hardware question,
how much is encoded in the hardware,
just the shared knowledge in the software,
the history, the many centuries of wars and so on
that came to today, that shared knowledge,
how hard is it to encode?
Do you have a hope?
Can you speak to how hard is it to encode that knowledge
systematically in a way that could be used by a computer?
- So I think it is possible to learn for a machine,
to program a machine to acquire that knowledge
with a similar foundation.
In other words, a similar interpretive foundation
for processing that knowledge.
- What do you mean by that?
- So in other words,
we view the world in a particular way.
And so in other words, we have, if you will,
as humans, we have a framework
for interpreting the world around us.
So we have multiple frameworks
for interpreting the world around us.
But if you're interpreting, for example,
sociopolitical interactions,
you're thinking about where there's people,
there's collections and groups of people,
they have goals, goals are largely built around survival
and quality of life.
There are fundamental economics
around scarcity of resources.
And when humans come and start interpreting
a situation like that,
because you brought up like historical events,
they start interpreting situations like that.
They apply a lot of this fundamental framework
for interpreting that.
Well, who are the people?
What were their goals?
What resources did they have?
How much power or influence did they have over the other?
Like this fundamental substrate, if you will,
for interpreting and reasoning about that.
So I think it is possible to imbue a computer
with that stuff that humans like take for granted
when they go and sit down and try to interpret things.
And then with that foundation, they acquire,
they start acquiring the details,
the specifics in a given situation,
are then able to interpret it with regard to that framework.
And then given that interpretation, they can do what?
They can predict.
But not only can they predict,
they can predict now with an explanation
that can be given in those terms,
in the terms of that underlying framework
that most humans share.
Now you can find humans that come and interpret events
very differently than other humans,
because they're like using a different framework.
You know, the movie "Matrix" comes to mind,
where they decided humans were really just batteries,
and that's how they interpreted the value of humans
as a source of electrical energy.
So, but I think that for the most part,
we have a way of interpreting the events,
or the social events around us,
because we have this shared framework.
It comes from, again, the fact that we're similar beings
that have similar goals, similar emotions,
and we can make sense out of these.
These frameworks make sense to us.
- So how much knowledge is there, do you think?
So you said it's possible.
- Well, there's a tremendous amount
of detailed knowledge in the world.
You can imagine effectively infinite number
of unique situations and unique configurations
of these things.
But the knowledge that you need,
what I refer to as like the frameworks,
for you need for interpreting them, I don't think.
I think those are finite.
- You think the frameworks are more important
than the bulk of the knowledge?
So like framing--
- Yeah, because what the frameworks do
is they give you now the ability to interpret and reason,
and to interpret and reason over the specifics
in ways that other humans would understand.
- What about the specifics?
- Well, you acquire the specifics by reading
and by talking to other people.
- So I'll mostly, actually, just even,
if we can focus on even the beginning,
the common sense stuff,
the stuff that doesn't even require reading,
or it almost requires playing around
with the world or something.
Just being able to sort of manipulate objects,
drink water and so on, all of that.
Every time we try to do that kind of thing
in robotics or AI, it seems to be like an onion.
You seem to realize how much knowledge
is really required to perform
even some of these basic tasks.
Do you have that sense as well?
And if so, how do we get all those details?
Are they written down somewhere?
Do they have to be learned through experience?
- So I think when, like if you're talking about
sort of the physics, the basic physics around us,
for example, acquiring information about,
acquiring how that works.
§05Transcript · 36:48–45:59
Yeah, I mean, I think there's a combination of things going,
I think there's a combination of things going on.
I think there is like fundamental pattern matching,
like what we were talking about before,
where you see enough examples,
enough data about something,
you just start assuming that,
and with similar input, I'm gonna predict similar outputs.
You can't necessarily explain it at all.
You may learn very quickly that when you let something go,
it falls to the ground.
- That's such a--
- But you can't necessarily explain that.
- But that's such a deep idea,
that if you let something go,
like the idea of gravity.
- I mean, people are letting things go
and counting on them falling
well before they understood gravity.
- But that seems to be, that's exactly what I mean,
is before you take a physics class
or study anything about Newton,
just the idea that stuff falls to the ground
and then you be able to generalize
that all kinds of stuff falls to the ground,
it just seems like a non,
without encoding it, like hard coding it in,
it seems like a difficult thing to pick up.
It seems like you have to have a lot of different knowledge
to be able to integrate that into the framework,
sort of into everything else.
So both know that stuff falls to the ground
and start to reason about sociopolitical discourse.
So both, like the very basic
and the high level reasoning decision-making.
I guess my question is how hard is this problem?
Sorry to linger on it because again,
and we'll get to it for sure,
as what Watson with Jeopardy did,
is take on a problem that's much more constrained
but has the same hugeness of scale,
at least from the outsider's perspective.
So I'm asking the general life question
of to be able to be an intelligent being
and reasoning in the world
about both gravity and politics,
how hard is that problem?
- So I think it's solvable.
- Okay, now beautiful.
So what about time travel?
Okay, on that topic, I'm just,
not the same answer? - Not as convinced.
- Not as convinced yet, okay.
- No, I think it is, I think it is solvable.
I mean, I think that it's,
first of all, it's about getting machines to learn.
Learning is fundamental.
And I think we're already in a place that we understand,
for example, how machines can learn in various ways.
Right now, our learning stuff is sort of primitive
in that we haven't sort of taught machines
to learn the frameworks.
We don't communicate our frameworks
because of how shared, in some cases we do,
but we don't annotate, if you will,
all the data in the world with the frameworks
that are inherent or underlying our understanding.
Instead, we just operate with the data.
So if we wanna be able to reason over the data
in similar terms in the common frameworks,
we need to be able to teach the computer,
or at least we need to program the computer to acquire,
to have access to and acquire, learn the frameworks as well
and connect the frameworks to the data.
I think this can be done.
I think we can start, I think machine learning,
for example, with enough examples,
can start to learn these basic dynamics.
Will they relate them necessarily to gravity?
Not unless they can also acquire those theories as well
and put the experiential knowledge
and connect it back to the theoretical knowledge.
I think if we think in terms of these class of architectures
that are designed to both learn the specifics,
find the patterns, but also acquire the frameworks
and connect the data to the frameworks,
if we think in terms of robust architectures like this,
I think there is a path toward getting there.
- In terms of encoding architectures like that,
do you think systems that are able to do this
will look like neural networks
or representing, if you look back to the '80s and '90s,
with the expert systems,
so more like graphs, systems that are based in logic,
able to contain a large amount of knowledge
where the challenge was the automated acquisition
of that knowledge.
I guess the question is,
when you collect both the frameworks
and the knowledge from the data,
what do you think that thing will look like?
- Yeah, so I mean, I think asking the question
do they look like neural networks is a bit of a red herring.
I mean, I think that they will certainly do inductive
or pattern-matched-based reasoning.
And I've already experimented with architectures
that combine both, that use machine learning
and neural networks to learn certain classes of knowledge,
in other words, to find repeated patterns
in order for it to make good inductive guesses,
but then ultimately to try to take those learnings
and marry them, in other words, connect them to frameworks
so that it can then reason over that
in terms other humans understand.
So for example, at Elemental Cognition, we do both.
We have architectures that do both, but both those things,
but also have a learning method
for acquiring the frameworks themselves and saying,
"Look, ultimately I need to take this data.
"I need to interpret it in the form of these frameworks
"so they can reason over it."
So there is a fundamental knowledge representation,
like what you're saying, like these graphs of logic,
if you will.
There are also neural networks
that acquire a certain class of information.
Then they align them with these frameworks,
but there's also a mechanism
to acquire the frameworks themselves.
- Yeah, so it seems like the idea of frameworks
requires some kind of collaboration with humans.
- Absolutely.
- So do you think of that collaboration as direct?
- Well, and let's be clear.
Only for the express purpose
that you're designing an intelligence
that can ultimately communicate with humans
in the terms of frameworks that help them understand things.
So to be really clear, you can independently create
a machine learning system, an intelligence
that I might call an alien intelligence
that does a better job than you with some things,
but can't explain the framework to you.
That doesn't mean it might be better than you at the thing.
It might be that you cannot comprehend the framework
that it may have created for itself
that is inexplicable to you.
That's a reality.
- But you're more interested in a case where you can.
- I am, yeah.
My sort of approach to AI
is because I've set the goal for myself.
I want machines to be able to ultimately communicate
understanding with humans.
I want them to be able to acquire and communicate,
acquire knowledge from humans
and communicate knowledge to humans.
They should be using what, you know,
inductive machine learning techniques are good at,
which is to observe patterns of data,
whether it be in language or whether it be in images
or videos or whatever, to acquire these patterns,
to induce the generalizations from those patterns,
but then ultimately work with humans
to connect them to frameworks, interpretations, if you will,
that ultimately make sense to humans.
Of course, the machine is gonna have the strength
that it has, the richer, longer memory,
but that, you know, it has the more rigorous
reasoning abilities, the deeper reasoning abilities.
So it'll be an interesting, you know,
complementary relationship between the human and the machine.
- Do you think that ultimately needs explainability
like a machine?
So if we look, we study, for example,
Tesla autopilot a lot, where humans,
I don't know if you've driven the vehicle,
are aware of what, so you're basically,
the human and machine are working together there,
and the human is responsible for their own life
to monitor the system, and you know,
the system fails every few miles.
And so there's hundreds, there's millions
of those failures a day.
And so that's like a moment of interaction.
Do you see--
- Yeah, no, that's exactly right.
That's a moment of interaction where, you know,
the machine has learned some stuff,
it has a failure, somehow the failure's communicated,
the human is now filling in the mistake, if you will,
or maybe correcting or doing something
that is more successful in that case,
the computer takes that learning.
So I believe that the collaboration
between human and machine, I mean,
that's sort of a primitive example
and sort of a more, another example
is where the machine's literally talking to you
and saying, "Look, I'm reading this thing.
§06Transcript · 46:02–54:10
"I know that like the next word might be this or that,
"but I don't really understand why.
"I have my guess, can you help me understand
"the framework that supports this?"
And then can kind of acquire that,
take that and reason about it and reuse it
the next time it's reading to try to understand something.
Not unlike a human student might do.
I mean, I remember when my daughter was in first grade
and she had a reading assignment about electricity
and somewhere in the text it says,
"And electricity is produced by water flowing over turbines,"
or something like that.
And then there's a question that says,
"Well, how is electricity created?"
And so my daughter comes to me and says,
"I mean, I could, you know, created and produced
"are kind of synonyms in this case,
"so I can go back to the text and I can copy
"by water flowing over turbines."
But I have no idea what that means.
Like, I don't know how to interpret
water flowing over turbines and what electricity even is.
I mean, I can get the answer right by matching the text,
but I don't have any framework for understanding
what this means at all.
- And framework really is, I mean, it's a set of,
not to be mathematical, but axioms of ideas
that you bring to the table and interpreting stuff
and then you build those up somehow.
You build them up with the expectation
that there's a shared understanding of what they are.
- Sure, yeah, it's the social, us humans.
Do you have a sense that humans on Earth in general
share a set of, like how many frameworks are there?
- I mean, it depends on how you bound them, right?
So in other words, how big or small
like their individual scope.
But there's lots and there are new ones.
I think the way I think about it is kind of in a layer.
I think of the architectures as being layered in that.
There's a small set of primitives
that allow you the foundation to build frameworks.
And then there may be many frameworks,
but you have the ability to acquire them.
And then you have the ability to reuse them.
I mean, one of the most compelling ways of thinking
about this is a reasoning by analogy where I can say,
oh, wow, I've learned something very similar.
I never heard of this game, soccer,
but if it's like basketball in the sense
that the goal's like the hoop
and I have to get the ball in the hoop
and I have guards and I have this and I have that,
like where are the similarities and where are the differences
and I have a foundation now
for interpreting this new information.
- And then the different groups,
like the millennials will have a framework.
- Yeah, well, like that--
- The Democrats and Republicans.
Millennials, nobody wants that framework.
- Well, I mean, I think--
- Nobody understands it.
- Right, I mean, they're talking about political
and social ways of interpreting the world around them.
And I think these frameworks
are still largely, largely similar.
I think they differ in maybe what some fundamental
assumptions and values are.
Now, from a reasoning perspective,
like the ability to process the framework
might not be that different.
The implications of different fundamental values
or fundamental assumptions in those frameworks
may reach very different conclusions.
So from a social perspective,
the conclusions may be very different.
From an intelligence perspective,
I just followed where my assumptions took me.
- Yeah, the process itself will look similar,
but that's a fascinating idea
that frameworks really help carve
how a statement will be interpreted.
I mean, having a Democrat and a Republican framework
and then read the exact same statement
and the conclusions that you derive
will be totally different from an AI perspective
is fascinating.
- What we would want out of the AI
is to be able to tell you that this perspective,
one perspective, one set of assumptions
is gonna lead you here,
another set of assumptions is gonna lead you there.
And in fact, to help people reason and say,
oh, I see where our differences lie.
You know, I have this fundamental belief about that,
I have this fundamental belief about that.
- Yeah, that's quite brilliant.
- From my perspective, NLP,
there's this idea that there's one way
to really understand a statement,
but there probably isn't.
There's probably an infinite number of ways
to understand a statement, depending on the framework.
- There's lots of different interpretations
and the broader the content, the richer it is.
And so, you and I can have very different experiences
with the same text, obviously.
And if we're committed to understanding each other,
we start, and that's the other important point,
if we're committed to understanding each other,
we start decomposing and breaking down our interpretation
to its more and more primitive components
until we get to that point where we say,
oh, I see why we disagree.
And we try to understand how fundamental
that disagreement really is.
But that requires a commitment
to breaking down that interpretation
in terms of that framework in a logical way.
Otherwise, and this is why I think of AI
as really complementing and helping human intelligence
to overcome some of its biases and its predisposition
to be persuaded by more shallow reasoning,
in the sense that we get over this idea,
well, I'm right because I'm Republican,
or I'm right because I'm Democratic,
and someone labeled this as a Democratic point of view,
or it has the following keywords in it.
And if the machine can help us break that argument down
and say, wait a second,
what do you really think about this?
So essentially holding us accountable
to doing more critical thinking.
- We're gonna have to sit and think about that.
I love that.
I think that's really empowering use of AI
for the public discourse
that's completely disintegrating currently
as we learn how to do it on social media.
- That's right.
- So one of the greatest accomplishments
in the history of AI is Watson
competing in the game of Jeopardy against humans.
And you were a lead in that, a critical part of that.
Let's start at the very basics.
What is the game of Jeopardy?
The game for us humans, human versus human.
- Right, so it's to take a question
and answer it.
The game of Jeopardy.
- It's just the opposite.
- Actually, well, no, but it's not, right?
It's really not.
It's really to get a question and answer,
but it's what we call a factoid question.
So this notion of like,
it really relates to some fact
that few people would argue
whether the facts are true or not.
In fact, most people wouldn't.
Jeopardy kind of counts on the idea
that these statements have factual answers.
And the idea is to, first of all,
determine whether or not you know the answer,
which is sort of an interesting twist.
- So first of all, understand the question.
- You have to understand the question.
What is it asking?
And that's a good point
because the questions are not asked directly, right?
- They're all like, the way the questions are asked
is nonlinear.
It's like, it's a little bit witty.
It's a little bit playful sometimes.
It's a little bit tricky.
- Yeah, they're asked in, exactly,
in numerous, witty, tricky ways.
Exactly what they're asking is not obvious.
It takes inexperienced humans a while to go,
what is it even asking?
And that's sort of an interesting realization
that you have when somebody says,
oh, Jeopardy is a question answering show.
And then it's like, oh, I know a lot.
And then you read it,
and you're still trying to process the question,
and the champions have answered and moved on.
There are three questions ahead
by the time you've figured out
what the question even meant.
So there's definitely an ability there
to just parse out what the question even is.
So that was certainly challenging.
It's interesting, historically, though,
if you look back at the Jeopardy games much earlier.
- Like '60s, '70s, that kind of thing?
- The questions were much more direct.
They weren't quite like that.
§07Transcript · 54:11–1:02:14
They got sort of more and more interesting.
The way they asked them,
that sort of got more and more interesting,
and subtle, and nuanced, and humorous, and witty over time,
which really required the human
to kind of make the right connections
in figuring out what the question was even asking.
So yeah, you have to figure out the questions even asking.
Then you have to determine whether or not
you think you know the answer.
And because you have to buzz in really quickly,
you sort of have to make that determination
as quickly as you possibly can.
Otherwise, you lose the opportunity to buzz in.
- Even before you really know if you know the answer.
- I think a lot of humans will assume
they'll look at it, process it very superficially.
In other words, what's the topic, what are some keywords,
and just say, do I know this area or not
before they actually know the answer?
Then they'll buzz in and think about it.
So it's interesting what humans do.
Now, some people who know all things,
like Ken Jennings or something,
or the more recent Big Jeopardy player,
I mean, they'll just buzz in.
They'll just assume they know all about Jeopardy,
and they'll just buzz in.
Watson, interestingly, didn't even come close
to knowing all of Jeopardy, right?
Watson really-- - Even at the peak,
even at its best. - Yeah, so for example,
I mean, we had this thing called recall,
which is like how many of all the Jeopardy questions,
how many could we even find the right answer for,
like anywhere?
Like, could we come up with, if we had a big body of knowledge
of some of the order of several terabytes,
I mean, from a web scale, it was actually very small.
But from a book scale,
I was talking about millions of books, right?
So they're calling millions of books,
encyclopedias, dictionaries, books,
so it's still a ton of information.
And for, I think it was only 85%
was the answer anywhere to be found.
So you're already down at that level
just to get started, right?
So, and so it was important to get a very quick sense of,
do you think you know the right answer to this question?
So we had to compute that confidence
as quickly as we possibly could.
So in effect, we had to answer it
and at least spend some time essentially answering it
and then judging the confidence
that our answer was right
and then deciding whether or not
we were confident enough to buzz in.
And that would depend on what else was going on in the game
because there was a risk.
So like, if you're really in a situation
where I have to take a guess, I have very little to lose,
then you'll buzz in with less confidence.
- So that was accounted for the financial standings
of the different competitors.
- Correct.
How much of the game was laughed,
how much time was laughed,
where you were in the standing and things like that.
- How many hundreds of milliseconds
that we're talking about here?
Do you have a sense of what is,
like if it's, what's the target?
- So, I mean, we targeted answering in under three seconds
and--
- Buzzing in, so the decision to buzz in
and then the actual answering,
are those two different stages?
- Yeah, they were two different things.
In fact, we had multiple stages,
whereas like we would say, let's estimate our confidence,
which was sort of a shallow answering process.
And then ultimately decide to buzz in,
and then we may take another second or something
to kind of go in there and do that.
But by and large, we're saying like, we can't play the game.
We can't even compete if we can't, on average,
answer these questions in around three seconds or less.
- So you stepped in,
so there's these three humans playing a game,
and you stepped in with the idea that IBM Watson
would be one of, replace one of the humans
and compete against two.
Can you tell the story of Watson taking on this game?
- Sure.
- It seems exceptionally difficult.
- Yeah, so the story was that it was coming up,
I think, to the 10-year anniversary of Big Blue.
Not Big Blue, Deep Blue.
IBM wanted to do sort of another kind of really,
fun challenge, public challenge
that can bring attention to IBM research
and the kind of the cool stuff that we were doing.
I had been working in AI at IBM for some time.
I had a team doing what's called
open domain factoid question answering,
which is, we're not gonna tell you what the questions are,
we're not even gonna tell you what they're about.
Can you go off and get accurate answers to these questions?
And it was an area of AI research that I was involved in.
And so it was a very specific passion of mine.
Language understanding had always been a passion of mine.
One sort of narrow slice on whether or not
you could do anything with language
was this notion of open domain,
meaning I could ask anything about anything.
Factoid, meaning it essentially had an answer,
and being able to do that accurately and quickly.
So that was a research area that my team had already been in.
And so completely independently,
several IBM executives were like, what are we gonna do?
What's the next cool thing to do?
And Ken Jennings was on his winning streak.
This was like, whatever it was, 2004, I think,
was on his winning streak.
And someone thought, hey, that would be really cool
if the computer can play Jeopardy.
And so this was like in 2004,
they were shopping this thing around.
And everyone was telling the research execs, no way.
Like, this is crazy.
And we had some pretty senior people in the field
and they're saying, no, this is crazy.
And it would come across my desk and I was like,
but that's kind of what I'm really interested in doing.
But there was such this prevailing sense of,
this is nuts, we're not gonna risk IBM's reputation on this,
we're just not doing it.
And this happened in 2004, it happened in 2005.
At the end of 2006, it was coming around again.
And I was coming off of a,
I was doing the open domain question answering stuff,
but I was coming off a couple other projects.
I had a lot more time to put into this.
And I argued that it could be done.
And I argued it would be crazy not to do this.
- Can I, you can be honest at this point.
So even though you argued for it,
what's the confidence that you had yourself, privately,
that this could be done?
Was, we just told the story,
how you tell stories to convince others.
How confident were you?
What was your estimation of the problem at that time?
- So I thought it was possible.
And a lot of people thought it was impossible.
I thought it was possible.
A reason why I thought it was possible
was because I did some brief experimentation.
I knew a lot about how we were approaching
open domain factoid question answering.
We've been doing it for some years.
I looked at the Jeopardy stuff.
I said, this is gonna be hard
for a lot of the points that we mentioned earlier.
Hard to interpret the question,
hard to do it quickly enough,
hard to compute an accurate confidence.
None of this stuff had been done well enough before.
But a lot of the technologies we're building
were the kinds of technologies that should work.
But more to the point, what was driving me was,
I was in IBM Research.
I was a senior leader in IBM Research.
And this is the kind of stuff we were supposed to do.
In other words, we were basically supposed to--
- This is the moonshot.
This is the--
- I mean, we were supposed to take things and say,
this is an active research area.
It's our obligation to kind of,
if we have the opportunity, to push it to the limits.
And if it doesn't work, to understand more deeply
why we can't do it.
And so I was very committed to that notion,
saying, folks, this is what we do.
It's crazy not to do this.
This is an active research area.
We've been in this for years.
Why wouldn't we take this grand challenge
and push it as hard as we can?
At the very least, we'd be able to come out and say,
here's why this problem is way hard.
Here's what we've tried and here's how we failed.
So I was very driven as a scientist from that perspective.
And then I also argued,
based on what we did a feasibility study,
of why I thought it was hard but possible.
And I showed examples of where it succeeded,
where it failed, why it failed,
§08Transcript · 1:02:16–1:09:56
and sort of a high-level architectural approach
for why we should do it.
But for the most part, at that point,
the execs really were just looking
for someone crazy enough to say yes,
because for several years at that point,
everyone had said no.
I'm not willing to risk my reputation
and my career on this thing.
- Clearly, you did not have such fears.
- I did not.
- So you dived right in, and yet,
from what I understand,
it was performing very poorly in the beginning.
So what were the initial approaches and why did they fail?
- Well, there were lots of hard aspects to it.
I mean, one of the reasons why prior approaches
that we had worked on in the past failed
was because the questions were difficult to interpret.
Like, what are you even asking for?
Very often, if the question was very direct,
like what city, or what, even then it could be tricky,
but what city or what person,
often when it would name it very clearly,
you would know that.
And if there were just a small set of them,
in other words, we're gonna ask about these five types.
Like, it's gonna be an answer,
and the answer will be a city in this state,
or a city in this country.
The answer will be a person of this type, right?
Like an actor or whatever it is.
But it turns out that in "Jeopardy!"
there were like tens of thousands of these things,
and it was a very, very long tale,
meaning it just went on and on.
And so even if you focused on trying to encode the types
at the very top, like there's five that were the most,
let's say five of the most frequent,
you still cover a very small percentage of the data.
So you couldn't take that approach of saying,
I'm just going to try to collect facts
about these five or 10 types,
or 20 types, or 50 types, or whatever.
So that was like one of the first things,
like what do you do about that?
And so we came up with an approach toward that.
And the approach looked promising.
And we continued to improve our ability
to handle that problem throughout the project.
The other issue was that right from the outset,
I said, we're not going to,
I committed to doing this in three to five years.
So we did it in four.
So I got lucky.
But one of the things that that,
putting that stake in the ground,
was I, and I knew how hard
the language understanding problem was.
I said, we're not going to actually understand language
to solve this problem.
We are not going to interpret the question
and the domain of knowledge that the question refers to
in reason over that to answer these questions.
Obviously, we're not going to be doing that.
At the same time, simple search wasn't good enough
to confidently answer with a single correct answer.
- First of all, that's like brilliant.
That's such a great mix of innovation
and practical engineering, three, four, eight.
So you're not trying to solve the general NLU problem.
You're saying, let's solve this in any way possible.
- Oh, yeah, no, I was committed to saying,
look, we're just solving
the open domain question answering problem.
We're using Jeopardy as a driver for that.
- Big benchmark.
- Hard enough, big benchmark, exactly.
And now we're--
- How do we do it?
- We could just like, whatever,
like just figure out what works,
because I want to be able to go back
to the academic science community and say,
here's what we tried.
Here's what worked.
Here's what didn't work.
I don't want to go in and say,
oh, I only have one technology.
I have a hammer.
I'm only going to use this.
I'm going to do whatever it takes.
I'm like, I'm going to think out of the box
and do whatever it takes.
And I also, there was another thing I believed.
I believed that the fundamental NLP technologies
and machine learning technologies would be adequate.
And this was an issue of how do we enhance them?
How do we integrate them?
How do we advance them?
So I had one researcher who came to me
who had been working on question answering
with me for a very long time,
who had said, we're going to need Maxwell's equations
for question answering.
And I said, if we need some fundamental formula
that breaks new ground in how we understand language,
we're screwed.
We're not going to get there from here.
Like, I am not counting.
My assumption is I'm not counting
on some brand new invention.
What I'm counting on is the ability to take everything
that has done before to figure out an architecture
on how to integrate it well, and then see where it breaks
and make the necessary advances we need to make
until this thing works.
- Yeah, push it hard to see where it breaks
and then patch it up.
I mean, that's how people change the world.
I mean, that's the Elon Musk approach with rockets,
SpaceX, that's the Henry Ford and so on.
I love it.
- And I happen to be, in this case, I happen to be right,
but we didn't know.
But you kind of have to put a stake in it
as to how you're going to run the project.
- So, yeah, and backtracking to search.
So, if you were to do, what's the brute force solution?
What would you search over?
So you have a question, how would you search
the possible space of answers?
- Look, web search has come a long way, even since then.
But at the time, first of all, I mean,
there are a couple other constraints around the problem,
which is interesting.
So you couldn't go out to the web,
you couldn't search the internet.
In other words, the AI experiment was,
we want a self-contained device.
The device, if the device is as big as a room,
fine, it's as big as a room,
but we want a self-contained device.
You're not going out to the internet,
you don't have a lifeline to anything.
So it had to kind of fit in a shoebox, if you will,
or at least the size of a few refrigerators,
whatever it might be.
So, but also you couldn't just get out there.
You couldn't go off network, right, to kind of go.
So there was that limitation.
But then we did, but the basic thing was,
go do a web search.
The problem was, even when we went and did a web search,
I don't remember exactly the numbers,
but somewhere in the order of 65% of the time,
the answer would be somewhere,
you know, in the top 10 or 20 documents.
So first of all, that's not even good enough
to play Jeopardy.
You know, in other words, even if you could pull the,
even if you could perfectly pull the answer
out of the top 20 documents,
top 10 documents, whatever it was,
which we didn't know how to do,
but even if you could do that,
you'd be, and you knew it was right,
unless you had enough confidence in it, right?
So you'd have to pull out the right answer.
You'd have to have confidence it was the right answer.
And then you'd have to do that fast enough
to now go buzz in,
and you'd still only get 65% of them right,
which doesn't even put you in the winner's circle.
Winner's circle, you have to be up over 70,
and you have to do it really quick,
and you have to do it really quickly.
But now the problem is, well,
even if I had somewhere in the top 10 documents,
how do I figure out where in the top 10 documents
that answer is, and how do I compute a confidence
of all the possible candidates,
so it's not like I go in knowing the right answer
and have to pick it.
I don't know the right answer.
I have a bunch of documents,
somewhere in there's the right answer.
How do I, as a machine, go out and figure out
which one's right, and then how do I score it?
So, and now how do I deal with the fact
that I can't actually go out to the web?
- First of all, if you pause on that, just think about it.
If you could go to the web,
do you think that problem is solvable,
if you just pause on it?
Just thinking even beyond Jeopardy,
do you think the problem of reading text
to find where the answer is?
- Well, we solved that in some definition of solved,
given the Jeopardy challenge.
§09Transcript · 1:09:57–1:17:34
- How did you do it for Jeopardy?
So how do you take a body of work on a particular topic
and extract the key pieces of information?
- So, now forgetting about the huge volumes
that are on the web, right?
So now we have to figure out,
we did a lot of source research.
In other words, what body of knowledge
is gonna be small enough, but broad enough,
to answer Jeopardy?
And we ultimately did find the body of knowledge
that did that, I mean, it included Wikipedia
and a bunch of other stuff.
- So like encyclopedia type of stuff,
I don't know if you can speak to--
- Encyclopedia, dictionaries,
different types of semantic resources,
like WordNet and other types of semantic resources,
like that, as well as like some web crawls.
In other words, where we went out and took that content
and then expanded it based on producing statistical,
you know, statistically producing seeds,
using those seeds for other searches,
and then expanding that.
So using these like expansion techniques,
we went out and found enough content
and we're like, okay, this is good.
And even up until the end, you know,
we had a thread of research,
it was always trying to figure out
what content could we efficiently include.
- I mean, there's a lot of popular,
like what is the church lady?
Well, I think was one of the, like what,
where do you, I guess that's probably an encyclopedia, so.
- So that's an encyclopedia, but then we would take
that stuff and we would go out and we would expand.
In other words, we go find other content
that wasn't in the core resources and expand it.
You know, the amount of content,
we grew it by an order of magnitude,
but still, again, from a web scale perspective,
this is very small amount of content.
- It's very select.
- And then we then took all that content
and we pre-analyzed the crap out of it,
meaning we parsed it, you know,
broke it down into all those individual words
and then we did semantic,
static and semantic parses on it,
you know, had computer algorithms that annotated it
and we indexed that in a very rich and very fast index.
So we have a relatively huge amount of,
let's say the equivalent of, for the sake of argument,
two to five million bucks.
We've now analyzed all that, blowing up its size even more
because now we have all this metadata
and then we richly indexed all of that
and by way in a giant in-memory cache.
So Watson did not go to disk.
- So the infrastructure component there,
if you could just speak to it, how tough it,
I mean, I know 2000, maybe this is 2008, nine,
you know, that's kind of a long time ago.
- Right.
- How hard is it to use multiple machines?
Like how hard is the infrastructure component,
the hardware component?
- So we used IBM hardware.
We had something like, I forget exactly,
but close to 3000 cores completely connected.
So you had a switch where, you know,
every CPU was connected to every other CPU.
- And they were sharing memory in some kind of way.
- Large shared memory, right?
And all this data was pre-analyzed
and put into a very fast indexing structure
that was all in memory.
And then we took that question,
we would analyze the question.
So all the content was now pre-analyzed.
So if I went and tried to find a piece of content,
it would come back with all the metadata
that we had pre-computed.
- How do you shove that question?
How do you connect the big stuff,
the big knowledge base of the metadata
that's indexed to the simple little witty,
confusing question?
- Right.
So therein lies, you know, the Watson architecture.
So we would take the question,
we would analyze the question.
So which means that we would parse it
and interpret it a bunch of different ways.
We'd try to figure out what is it asking about?
So we would come, we had multiple strategies
to kind of determine what was it asking for.
That might be represented as a simple string,
a character string,
or something we would connect back
to different semantic types
that were from existing resources.
So anyway, the bottom line is we would do
a bunch of analysis in the question.
And question analysis had to finish,
and it had to finish fast.
So we do the question analysis
because then from the question analysis,
we would now produce searches.
So we would, and we had built,
using open source search engines, we modified them.
We had a number of different search engines we would use
that had different characteristics.
We went in there and engineered
and modified those search engines,
ultimately to now take our question analysis,
produce multiple queries based on different interpretations
of the question,
and fire out a whole bunch of searches in parallel.
And they would come back with passages.
So these are passive search algorithms.
They would come back with passages.
And so now let's say you had a thousand passages.
Now for each passage, you parallelize again.
So you went out and you parallelized the search.
Each search would now come back
with a whole bunch of passages.
Maybe you had a total of a thousand
or 5,000, whatever passages.
For each passage now, you'd go and figure out
whether or not there was a candidate,
we'd call a candidate answer in there.
So you had a whole bunch of other algorithms
that would find candidate answers,
possible answers to the question.
And so you had candidate answers,
called candidate answers generators,
a whole bunch of those.
So for every one of these components,
the team was constantly doing research,
coming up better ways to generate search queries
from the questions,
better ways to analyze the question,
better ways to generate candidates.
- And speed, so better is accuracy and speed.
- Correct, so right, speed and accuracy,
for the most part, were separated.
We handled that sort of in separate ways.
Like I focus purely on accuracy and inaccuracy,
are we ultimately getting more questions
and producing more accurate confidences?
And then a whole nother team
that was constantly analyzing the workflow
to find the bottlenecks,
and then figuring out how to both parallelize
and drive the algorithm speed.
But anyway, so now think of it like,
you have this big fan out now, right?
Because you had multiple queries,
now you have thousands of candidate answers.
For each candidate answer, you're gonna score it.
So you're gonna use all the data that built up.
You're gonna use the question analysis.
You're gonna use how the query was generated.
You're gonna use the passage itself.
And you're gonna use the candidate answer
that was generated.
And you're gonna score that.
So now we have a group of researchers
coming up with scorers.
There are hundreds of different scorers.
So now you're getting a fan out of it again,
from however many candidate answers you have,
to all the different scorers.
So if you have 200 different scorers
and you have a thousand candidates,
now you have 200,000 scores.
And so now you gotta figure out,
how do I now rank these answers
based on the scores that came back?
And I wanna rank them based on the likelihood
that they're a correct answer to the question.
So every scorer was its own research project.
- What do you mean by scorer?
So is that the annotation process
of basically a human being saying
that this answer has a quality of--
- Think of it, if you wanna think of it,
what you're doing, if you wanna think about
what a human would be doing,
a human would be looking at a possible answer.
They'd be reading the, Emily Dickinson,
they'd be reading the passage in which that occurred.
They'd be looking at the question
and they'd be making a decision of how likely it is
that Emily Dickinson, given this evidence in this passage,
is the right answer to that question.
- Got it.
So that's the annotation task.
§10Transcript · 1:17:36–1:26:40
That's the annotation process.
- That's the scoring task.
- But scoring implies zero to one kind of continuous--
- That's right, you give it a zero to one score.
- So it's not a binary--
- No, you give it a score.
Give it a zero, yeah, exactly, a zero to one score.
- So but humans give different scores,
so you have to somehow normalize and all that kind of stuff
that deal with all that complexity.
- Depends on what your strategy is.
We both, we--
- Could be relative, too.
It could be--
- We actually looked at the raw scores as well,
standardized scores, because humans are not involved in this.
Humans are not involved.
- Sorry, so I'm misunderstanding the process here.
There's passages.
Where is the ground truth coming from?
- Ground truth is only the answers to the questions.
- So it's end to end.
- It's end to end.
So I was always driving end to end performance.
It was a very interesting engineering approach,
and ultimately scientific and research approach,
always driving end to end.
Now, that's not to say we wouldn't make hypotheses
that individual component performance
was related in some way to end to end performance.
Of course we would, because people would have to
build individual components.
But ultimately, to get your component
integrated into the system,
you had to show impact on end to end performance,
question answering performance.
- There's many very smart people working on this,
and they're basically trying to sell their ideas
as a component that should be part of the system.
- That's right.
And they would do research on their component,
and they would say things like,
I'm gonna improve this as a candidate generator,
or I'm gonna improve this as a question score,
or as a passage score, I'm gonna improve this,
or as a parser, and I can improve it by 2%
on its component metric, like a better parse,
or a better candidate, or a better type estimation,
or whatever it is.
And then I would say, I need to understand
how the improvement on that component metric
is gonna affect the end to end performance.
If you can't estimate that, and can't do experiments
to demonstrate that, it doesn't get in.
- That's like the best run AI project I've ever heard.
That's awesome, okay.
What breakthrough would you say,
like I'm sure there's a lot of day to day breakthroughs,
but was there like a breakthrough
that really helped improve performance?
Like where people began to believe?
Or is it just a gradual process?
- Well, I think it was a gradual process,
but one of the things that I think gave people confidence
that we can get there was that as we follow this procedure
of different ideas, build different components,
plug them into the architecture, run the system,
see how we do, do the error analysis,
start off new research projects to improve things,
and the very important idea that the individual component
work did not have to deeply understand everything
that was going on with every other component.
And this is where we leverage machine learning
in a very important way.
So while individual components could be statistically driven
machine learning components, some of them were heuristic,
some of them were machine learning components,
the system has a whole combined all the scores
using machine learning.
This was critical because that way you can divide
and conquer.
So you can say, okay, you work on your candidate generator,
or you work on this approach to answer scoring,
you work on this approach to type scoring,
you work on this approach to passage search
or to passage selection and so forth.
But when we just plug it in,
and we had enough training data to say,
now we can train and figure out how do we weigh
all the scores relative to each other
based on predicting the outcome,
which is right or wrong on Jeopardy.
And we had enough training data to do that.
So this enabled people to work independently
and to let the machine learning do the integration.
- Beautiful, so yeah, the machine learning
is doing the fusion, and then it's a human orchestrated
ensemble of different approaches.
That's great.
Still impressive that you're able to get it done
in a few years.
That's not obvious to me that it's doable
if I just put myself in that mindset.
But when you look back at the Jeopardy challenge,
again, when you're looking up at the stars,
what are you most proud of?
Just looking back at those days.
- I'm most proud of my...
(mouse clicking)
My commitment and my team's commitment
to be true to the science,
to not be afraid to fail.
- That's beautiful because there's so much pressure
because it is a public event, it is a public show,
that you were dedicated to the idea.
- That's right.
- Do you think it was a success?
In the eyes of the world, it was a success.
By your, I'm sure, exceptionally high standards,
is there something you regret you would do differently?
- It was a success.
It was a success for our goal.
Our goal was to build the most advanced
open domain question answering system.
We went back to the old problems that we used to try
to solve and we did dramatically better on all of them,
as well as we beat Jeopardy.
So we won at Jeopardy.
So it was a success.
I worry that the world would not understand it as a success
because it came down to only one game.
And I knew statistically speaking,
this can be a huge technical success
and we could still lose that one game.
And that's a whole nother theme of the journey.
But it was a success.
It was not a success in natural language understanding,
but that was not the goal.
- Yeah, that was, but I would argue,
I understand what you're saying in terms of the science,
but I would argue that the inspiration of it, right,
not a success in terms of solving
natural language understanding,
but it was a success of being an inspiration
to future challenges.
- Absolutely.
- That drive future efforts.
What's the difference between how human being
compete in Jeopardy and how Watson does it?
That's important in terms of intelligence.
- Yeah, so that actually came up very early on
in the project also.
In fact, I had people who wanted to be on the project
who were early on, who sort of approached me
once I committed to do it,
that wanted to think about how humans do it.
And they were, you know, from a cognition perspective,
like human cognition and how that should play.
And I would not take them on the project
because another assumption or another stake
I put in the ground was I don't really care
how humans do this.
- At least in the context of this project.
- I need to build, in the context of this project,
in NLU and in building an AI that understands
how it needs to ultimately communicate with humans,
I very much care.
So it wasn't that I didn't care in general.
In fact, as an AI scientist, I care a lot about that,
but I'm also a practical engineer
and I committed to getting this thing done
and I wasn't gonna get distracted.
I had to kind of say, like, if I'm gonna get this done,
I'm gonna chart this path and this path says,
we're gonna engineer a machine
that's gonna get this thing done.
And we know what search and NLP can do.
We have to build on that foundation.
If I come in and take a different approach
and start wondering about how the human mind
might or might not do this,
I'm not gonna get there from here in the time frame.
- I think that's a great way to lead the team.
But now that it's done and then one,
when you look back, analyze what's the difference actually.
- Right, so I was a little bit surprised actually
to discover over time, as this would come up
from time to time and we'd reflect on it,
and talking to Ken Jennings a little bit
and hearing Ken Jennings talk about
how he answered questions,
that it might have been closer to the way humans
answer questions than I might have imagined previously.
- 'Cause humans are probably in the game of Jeopardy
at the level of Ken Jennings,
probably also cheating their way to winning, right?
- Well, they're doing shallow analysis.
- Shallow, the fastest possible.
- They're doing shallow analysis.
So they are very quickly analyzing the question
§11Transcript · 1:26:44–1:35:55
and coming up with some key vectors or cues, if you will.
And they're taking those cues
and they're very quickly going through
their library of stuff,
not deeply reasoning about what's going on.
And then sort of like a lots of different,
like what we call these scores,
would kind of score that in a very shallow way
and then say, oh, boom, that's what it is.
And so it's interesting as we reflected on that,
so we may be doing something that's not too far off
from the way humans do it,
but we certainly didn't approach it by saying,
how would a human do this?
Now in elemental cognition,
like the project I'm leading now,
we ask those questions all the time,
because ultimately we're trying to do something
that is to make the intelligence of the machine
and the intelligence of the human very compatible.
Well, compatible in the sense they can communicate
with one another and they can reason
with this shared understanding.
So how they think about things and how they build answers,
how they build explanations
becomes a very important question to consider.
- So what's the difference between this open domain,
but cold constructed question answering of Jeopardy
and more something that requires understanding
for shared communication with humans and machines?
- Yeah, well, this goes back to the interpretation
of what we were talking about before.
- Framework.
- Jeopardy, the system's not trying to interpret
the question and it's not interpreting the content
that's reusing with regard to any particular framework.
I mean, it is parsing it and parsing the content
and using grammatical cues and stuff like that.
So if you think of grammar as a human framework,
in some sense it has that.
But when you get into the richer semantic frameworks,
what are people, how do they think, what motivates them?
What are the events that are occurring
and why are they occurring and what causes
what else to happen and where are things in time and space?
And like when you start thinking about how humans formulate
and structure the knowledge that they acquire in their head,
it wasn't doing any of that.
- What do you think are the essential challenges
of free-flowing communication, free-flowing dialogue
versus question answering even with a framework
of the interpretation?
Dialogue.
- Yep.
- Do you see free-flowing dialogue
as fundamentally more difficult than question answering
even with shared interpretation?
- So dialogue is important in a number of different ways.
I mean, it's a challenge.
So first of all, when I think about the machine that,
when I think about a machine that understands language
and ultimately can reason in an objective way
that can take the information that it perceives
through language or other means
and connect it back to these frameworks,
reason and explain itself,
that system ultimately needs to be able to talk to humans
or it needs to be able to interact with humans.
So in some sense it needs to dialogue.
That doesn't mean that it,
sometimes people talk about dialogue and they think,
you know, how do humans talk to each other
in a casual conversation?
And you can mimic casual conversations.
We're not trying to mimic casual conversations.
We're really trying to produce a machine
whose goal is to help you think
and help you reason about your answers
and explain why.
So instead of like talking to your friend down the street
about having a small talk conversation
with your friend down the street,
this is more about like you would be communicating
to the computer on Star Trek where,
like what do you wanna think about?
Like what do you wanna reason about?
I'm gonna tell you the information I have.
I'm gonna have to summarize it.
I'm gonna ask you questions.
You're gonna answer those questions.
I'm gonna go back and forth with you.
I'm gonna figure out what your mental model is.
I'm gonna now relate that to the information I have
and present it to you in a way that you can understand it
and then we could ask follow-up questions.
So it's that type of dialogue that you wanna construct.
It's more structured.
It's more goal-oriented, but it needs to be fluid.
In other words, it can't, it can't,
it has to be engaging and fluid.
It has to be productive and not distracting.
So there has to be a model of,
in other words, the machine has to have a model
of how humans think through things and discuss them.
- So basically a productive, rich conversation,
unlike this podcast.
- I'd like to think it's more similar to this podcast.
- I was just joking.
I'll ask you about humor as well, actually.
But what's the hardest part of that?
Because it seems we're quite far away
as a community from that still to be able to,
so one is having a shared understanding.
That's, I think, a lot of the stuff you said
with frameworks is quite brilliant.
But just creating a smooth discourse.
Yeah, it feels clunky right now.
Which aspects of this whole problem that you specified
of having a productive conversation is the hardest?
Or maybe any aspect of it you can comment on
'cause it's so shrouded in mystery.
- So I think to do this, you kind of have to be creative
in the following sense.
If I were to do this as purely a machine learning approach
and someone said, "Learn how to have a good,
"fluent, structured knowledge acquisition conversation,"
I'd go out and say, "Okay, I have to collect a bunch
"of data of people doing that, people reasoning well
"having a good, structured conversation
"that both acquires knowledge efficiently
"as well as produces answers and explanations
"as part of the process."
And you struggle.
I don't know-- - To collect the data.
- To collect the data because I don't know
how much data is like that.
- Okay, okay, this one, there's a humorous commenter
on the lack of rational discourse.
But also, even if it's out there, say it was out there,
how do you actually-- - Yeah, how--
- Like how do you collect successful examples?
- Right, so I think any problem like this
where you don't have enough data to represent
the phenomenon you wanna learn, in other words,
if you have enough data, you could potentially
learn the pattern.
In an example like this, it's hard to do.
It's sort of a human sort of thing to do.
What recently came out at IBM was the debater project,
sort of interesting, right, because now you do have
these structured dialogues, these debate things,
where they did use machine learning techniques
to generate these debates.
Dialogues are a little bit tougher, in my opinion,
than generating a structured argument
where you have lots of other structured arguments like this.
You could potentially annotate that data
and you could say this is a good response,
this is a bad response in a particular domain.
Here, I have to be responsive and I have to be opportunistic
with regard to what is the human saying.
So I'm goal-oriented in saying I wanna solve the problem,
I wanna acquire the knowledge necessary,
but I also have to be opportunistic and responsive
to what the human is saying.
So I think that it's not clear that we could just train
on the body of data to do this, but we could bootstrap it.
In other words, we can be creative
and we could say, what do we think?
What do we think the structure of a good dialogue is
that does this well?
And we can start to create that.
If we can create that more programmatically,
at least to get this process started,
and I can create a tool that now engages humans effectively,
I could start both, I could start generating data,
I could start with the human learning process
and I can update my machine,
but I could also start
the automatic learning process as well.
But I have to understand what features to even learn over.
So I have to bootstrap the process a little bit first.
And that's a creative design task
that I could then use as input
into a more automatic learning task.
- So some creativity and yeah, and bootstrapping.
What elements of a conversation do you think
you would like to see?
So one of the benchmarks for me is humor, right?
That seems to be one of the hardest.
And to me, the biggest contrast is Watson.
So one of the greatest comedy sketches of all time, right,
is the SNL celebrity Jeopardy!
With Alex Trebek and Sean Connery
and Burt Reynolds and so on.
With Sean Connery commentating on Alex Trebek's mother
a lot.
And I think all of them are in the negative points wise.
So they're clearly all losing
in terms of the game of Jeopardy!,
§12Transcript · 1:35:56–1:44:21
but they're winning in terms of comedy.
So what do you think about humor in this whole interaction
in the dialogue that's productive?
Or even just whatever, what humor represents to me is
the same idea that you're saying about framework,
'cause humor only exists
within a particular human framework.
So what do you think about humor?
What do you think about things like humor
that connect to the kind of creativity
you mentioned that's needed?
- I think there's a couple of things going on there.
So I sort of feel like,
and I might be too optimistic this way,
but I think that there are,
we did a little bit about with puns in Jeopardy!.
We literally sat down and said,
how do puns work?
And it's like wordplay,
and you could formalize these things.
So I think there's a lot aspects of humor
that you could formalize.
You could also learn humor.
You could just say, what do people laugh at?
And if you have enough, again,
if you have enough data to represent that phenomenon,
you might be able to weigh the features
and figure out what humans find funny
and what they don't find funny.
The machine might not be able to explain
why the human find it funny,
unless we sit back and think about that more formally.
I think, again, I think you do a combination of both.
And I'm always a big proponent of that.
I think robust architectures and approaches
are always a little bit combination of us reflecting
and being creative about how things are structured,
how to formalize them,
and then taking advantage of large data
and doing learning
and figuring out how to combine these two approaches.
I think there's another aspect to humor though,
which goes to the idea that I feel like I can relate
to the person telling the story.
And I think that's an interesting theme
in the whole AI theme, which is,
do I feel differently when I know it's a robot?
And when I know, when I imagine
that the robot is not conscious the way I'm conscious,
when I imagine the robot does not actually
have the experiences that I experience,
do I find it funny?
Or do, because it's not as related,
I don't imagine that the person's relating it to it
the way I relate to it.
I think this also, you see this in the arts
and in entertainment where,
like sometimes you have savants
who are remarkable at a thing,
whether it's sculpture, it's music or whatever,
but the people who get the most attention
are the people who can evoke a similar emotional response
who can get you to emote, right, about the way they are.
In other words, who can basically make the connection
from the artifact, from the music or the painting
or the sculpture to the emotion
and get you to share that emotion with them.
And then, and that's when it becomes compelling.
So they're communicating at a whole different level.
They're just not communicating the artifact.
They're communicating their emotional response
to the artifact.
And then you feel like, oh, wow,
I can relate to that person.
I can connect to that person.
So I think humor has that aspect as well.
- So the idea that you can connect to that person,
person being the critical thing,
but we're also able to anthropomorphize objects pretty,
robots and AI systems pretty well.
So we're almost looking to make them human.
Then maybe from your experience with Watson,
maybe you can comment on,
did you consider that as part,
well, obviously the problem of Jeopardy
doesn't require anthropomorphization, but nevertheless--
- Well, there was some interest in doing that.
And that's another thing I didn't wanna do
'cause I didn't wanna distract
from the actual scientific task.
But you're absolutely right.
I mean, humans do anthropomorphize
and without necessarily a lot of work.
I mean, you just put some eyes
and a couple of eyebrow movements
and you're getting humans to react emotionally.
And I think you can do that.
So I didn't mean to suggest that,
that that connection cannot be mimicked.
I think that connection can be mimicked
and can get you to,
can produce that emotional response.
I just wonder though,
if you're told what's really going on,
if you know that the machine is not conscious,
not having the same richness of emotional reactions
and understanding that it doesn't really share
the understanding, but it's essentially
just moving its eyebrow or drooping its eyes
or making them bigger, whatever it's doing,
just getting the emotional response,
will you still feel it?
Interesting, I think you probably would for a while.
And then when it becomes more important
that there's a deeper shared understanding,
it may run flat, but I don't know.
- No, I'm pretty confident that majority of the world,
even if you tell them how it works--
- Won't matter.
- Well, it will not matter,
especially if the machine herself says
that she is conscious.
- That's very possible.
- So you, the scientist that made the machine,
is saying that this is how the algorithm works.
Everybody will just assume you're lying
and that there's a conscious being there.
- So you're deep into the science fiction genre now,
but yeah, I know--
- I don't think it's, it's actually psychology.
I think it's not science fiction.
I think it's reality.
I think it's a really powerful one
that we'll have to be exploring in the next few decades.
- I agree.
- It's a very interesting element of intelligence.
So what do you think,
we've talked about social constructs of intelligence
and frameworks and the way humans
kind of interpret information.
What do you think is a good test of intelligence
in your view?
So there's the Alan Turing with the Turing test.
Watson accomplished something very impressive with Jeopardy.
What do you think is a test
that would impress the heck out of you,
that you saw that a computer could do?
They would say, this is crossing a kind of threshold
that gives me pause in a good way.
- My expectations for AI are generally high.
- What does high look like, by the way?
So not the threshold, test is a threshold.
What do you think is the destination?
What do you think is the ceiling?
- I think machines will, in many measures,
will be better than us, will become more effective.
In other words, better predictors about a lot of things
than ultimately we can do.
I think where they're gonna struggle
is what we've talked about before,
which is relating to, communicating with,
and understanding humans in deeper ways.
And so I think that's a key point.
Like, we can create the super parrot.
What I mean by the super parrot is,
given enough data, a machine can mimic
your emotional response, can even generate language
that will sound smart, and what someone else might say
under similar circumstances.
Like, I would just pause on that.
Like, that's the super parrot, right?
So, given similar circumstances,
moves its faces in similar ways,
changes its tone of voice in similar ways,
produces strings of language that, you know,
would similar that a human might say,
not necessarily being able to produce
a logical interpretation or understanding
that would ultimately satisfy a critical interrogation
or a critical understanding.
- I think you just described me in a nutshell.
So I think philosophically speaking,
you could argue that that's all we're doing
as human beings, too.
We're super parrots. - So I was gonna say,
it's very possible, you know,
humans do behave that way, too.
And so upon deeper probing and deeper interrogation,
you may find out that there isn't a shared understanding,
because I think humans do both.
Like, humans are statistical language model machines,
and they are capable reasoners.
You know, they're both.
And you don't know which is going on, right?
So, and I think it's an interesting problem,
we talked earlier about, like,
where we are in our social and political landscape.
Can you distinguish someone who can string words together
and sound like they know what they're talking about
from someone who actually does?
§13Transcript · 1:44:24–1:52:53
Can you do that without dialogue,
without interrogative or probing dialogue?
So it's interesting, because humans are really good
at, in their own mind, justifying or explaining
what they hear, because they project
their understanding onto yours.
So you could say, you could put together a string of words,
and someone will sit there and interpret it
in a way that's extremely biased
to the way they want to interpret it.
They want to assume that you're an idiot,
and they'll interpret it one way.
They will assume you're a genius,
and they'll interpret it another way that suits their needs.
So this is tricky business.
So I think to answer your question,
as AI gets better and better at better and better mimic,
we create the super parrots,
we're challenged, just as we are with,
we're challenged with humans.
Do you really know what you're talking about?
Do you have a meaningful interpretation,
a powerful framework that you could reason over
and justify your answers, justify your predictions
and your beliefs, why you think they make sense?
Can you convince me what the implications are?
You know, can you, so can you reason intelligently
and make me believe that those,
the implications of your prediction and so forth?
So what happens is it becomes reflective.
My standard for judging your intelligence
depends a lot on mine.
- But you're saying that there should be
a large group of people with a certain standard
of intelligence that would be convinced
by this particular AI system,
then it would pass.
- There should be, but I think one of the,
depending on the content,
one of the problems we have there is that
if that large community of people are not judging it
with regard to a rigorous standard
of objective logic and reason, you still have a problem.
Like masses of people can be persuaded.
- The millennials, yeah.
- To turn their brains off.
- Right, okay.
- Sorry.
- By the way, I have nothing against the one.
- No, I don't know, I'm just,
so you're a part of one of the great benchmarks,
challenges of AI history.
What do you think about AlphaZero, OpenAI5,
AlphaStar accomplishments on video games recently,
which are also, I think, at least in the case of Go,
with AlphaGo and AlphaZero playing Go
was a monumental accomplishment as well.
What are your thoughts about that challenge?
- I think it was a giant landmark for AI.
I think it was phenomenal.
I mean, it was one of those other things
nobody thought like solving Go was gonna be easy,
particularly 'cause it's hard for,
particularly hard for humans,
hard for humans to learn, hard for humans to excel at.
And so it was another measure of intelligence.
It's very cool.
I mean, it's very interesting what they did.
I mean, and I loved how they solved the data problem,
which is, again, they bootstrapped it
and got the machine to play itself
to generate enough data to learn from.
I think that was brilliant.
I think that was great.
And of course the result speaks for itself.
I think it makes us think about, again,
okay, what's intelligence?
What aspects of intelligence are important?
Can the Go machine help me make me a better Go player?
Is it an alien intelligence?
Am I even capable of, like, again,
if we put in very simple terms, it found the function.
It found the Go function.
Can I even comprehend the Go function?
Can I talk about the Go function?
Can I conceptualize the Go function,
like whatever it might be?
- So one of the interesting ideas of that system
is that it plays against itself, right?
But there's no human in the loop there.
So like you're saying, it could have, by itself,
created an alien intelligence.
- Toward a goal, like, imagine you're sentencing,
you're a judge and you're sentencing people,
or you're setting policy,
or you're making medical decisions,
and you can't explain.
You can't get anybody to understand
what you're doing or why.
So it's an interesting dilemma for the applications of AI.
Do we hold AI to this accountability that says,
you know, humans have to be able to take responsibility
for the decision.
In other words, can you explain why you would do the thing?
Will you get up and speak to other humans
and convince them that this was a smart decision?
Is the AI enabling you to do that?
Can you get behind the logic that was made there?
- Do you think, sorry to linger on this point,
'cause it's a fascinating one.
It's a great goal for AI.
Do you think it's achievable in many cases?
Or, okay, there's two possible worlds
that we have in the future.
One is where AI systems do like medical diagnosis
or things like that, or drive a car,
without ever explaining to you why it fails when it does.
That's one possible world, and we're okay with it.
Or the other, where we are not okay with it,
and we really hold back the technology
from getting too good before it gets able to explain.
Which of those worlds are more likely, do you think,
and which are concerning to you or not?
- I think the reality is it's gonna be a mix.
I'm not sure I have a problem with that.
I mean, I think there are tasks that I'm perfectly fine with
machines show a certain level of performance,
and that level of performance is already better than humans.
So, for example, I don't know that I,
take driverless cars.
If driverless cars learn how to be more effective drivers
than humans, but can't explain what they're doing,
but bottom line, statistically speaking,
they're 10 times safer than humans,
I don't know that I care.
I think when we have these edge cases,
when something bad happens and we wanna decide
who's liable for that thing, and who made that mistake,
and what do we do about that?
And I think those edge cases are interesting cases.
And now do we go to designers of the AI,
and the AI says, I don't know,
if that's what it learned to do?
And it says, well, you didn't train it properly.
You know, you were negligent in the training data
that you gave that machine.
Like, how do we drive down the reliability?
So I think those are interesting questions.
- So the optimization problem there, sorry,
is to create an AI system that's able
to explain the lawyers away.
- Yeah, there you go.
I think that, I think it's gonna be interesting.
I mean, I think this is where technology
and social discourse are gonna get deeply intertwined
in how we start thinking about problems,
decisions, and problems like that.
I think in other cases, it becomes more obvious
where, you know, it's like,
like, why did you decide to give that person,
you know, a longer sentence, or deny them parole?
Again, policy decisions, or why did you pick that treatment?
Like, that treatment ended up killing that guy.
Like, why was that a reasonable choice to make?
So, and people are gonna demand explanations.
Now, there's a reality, though, here.
And the reality is that it's not,
I'm not sure humans are making reasonable choices
when they do these things.
They are using statistical hunches, biases,
or even systematically using statistical averages
to make calls.
I mean, this is what happened to my dad,
and if you saw the talk I gave about that.
But, you know, I mean, they decided
that my father was brain dead.
He had went into cardiac arrest,
and it took a long time for the ambulance to get there,
and he was not resuscitated right away, and so forth.
And they came, they told me he was brain dead.
And why was he brain dead?
Because essentially, they gave me
a purely statistical argument.
Under these conditions, with these four features,
98% chance he's brain dead.
And I said, but can you just tell me,
not inductively, but deductively,
go there and tell me his brain's not functioning
as the way for you to do that?
And the protocol and response was,
no, this is how we make this decision.
I said, this is inadequate for me.
I understand the statistics,
and I don't know how, you know,
there's a 2% chance he's still alive.
Like, I just don't know the specifics.
I need the specifics of this case,
and I want the deductive, logical argument
about why you actually know he's brain dead.
So I wouldn't sign the do not resuscitate.
§14Transcript · 1:52:55–2:02:54
And, I don't know, it was like,
they went through lots of procedures.
It was a big, long story.
But the bottom, a fascinating story, by the way,
about how I reasoned, and how the doctors reasoned
through this whole process.
But I don't know, somewhere around 24 hours later
or something, he was sitting up in bed
with zero brain damage.
- I mean, what lessons do you draw from that story,
that experience?
- That the data that's being used
to make statistical inferences
doesn't adequately reflect the phenomenon.
So in other words, you're getting shit wrong, sorry.
You're getting stuff wrong
because your model's not robust enough,
and you might be better off
not using statistical inference
and statistical averages in certain cases
when you know the model's insufficient,
and that you should be reasoning it about the specific case
more logically and more deductively,
and hold yourself responsible,
hold yourself accountable to doing that.
- And perhaps AI has a role to say
the exact thing we just said,
which is, perhaps this is a case
you should think for yourself.
You should reason deductively.
- Well, so it's hard because it's hard to know that.
You'd have to go back
and you'd have to have enough data to essentially say,
and this goes back to the case of how do we decide
whether AI is good enough to do a particular task?
And regardless of whether or not
it produces an explanation.
So, and what standards do we hold, right, for that?
So, if you look more broadly, for example,
as my father, as a medical case,
the medical system ultimately helped him
a lot throughout his life.
Without it, he probably would have died much sooner.
So overall, it's sort of worked for him
in sort of a net-net kind of way.
Actually, I don't know that that's fair,
but maybe not in that particular case, but overall.
The medical system overall does more good than bad.
- Yeah, the medical system overall
was doing more good than bad.
Now, there's another argument that suggests
that that wasn't the case,
but for the sake of argument,
let's say that's a net positive.
And I think you have to sit there
and take that into consideration.
Now you look at a particular use case,
like for example, making this decision.
Have you done enough studies to know
how good that prediction really is?
And have you done enough studies to compare it?
To say, well, what if we dug in in a more direct,
let's get the evidence, let's do the deductive thing
and not use statistics here.
How often would that have done better?
So you have to do the studies
to know how good the AI actually is.
And it's complicated,
because it depends how fast you have to make decision.
So if you have to make decisions super fast,
you have no choice.
If you have more time,
but if you're ready to pull the plug,
and this is a lot of the argument that I had with a doctor,
I said, what's he gonna do if you do it,
what's gonna happen to him in that room
if you do it my way?
Well, he's gonna die anyway,
so let's do it my way then.
- I mean, it raises questions for our society
to struggle with, as is the case with your father,
but also when things like race and gender
start coming into play,
when judgments are made based on things
that are complicated in our society,
at least in the discourse.
And it starts, I think I'm safe to say
that most of the violent crime is committed by males.
So if you discriminate based,
it's a male versus female saying that
if it's a male, more likely to commit the crime.
- So this is one of my very positive and optimistic views
of why the study of artificial intelligence,
the process of thinking and reasoning,
logically and statistically, and how to combine them
is so important for the discourse today,
because it's causing a, regardless of what state AI devices
are or not, it's causing this dialogue to happen.
This is one of the most important dialogues
that, in my view, the human species can have right now,
which is how to think well, how to reason well,
how to understand our own cognitive biases
and what to do about them.
That has got to be one of the most important things
we as a species can be doing, honestly.
We've created an incredibly complex society.
We've created amazing abilities to amplify noise
faster than we can amplify signal.
We are challenged.
We are deeply, deeply challenged.
We have big segments of the population
getting hit with enormous amounts of information.
Do they know how to do critical thinking?
Do they know how to objectively reason?
Do they understand what they are doing,
nevermind what their AI is doing?
This is such an important dialogue to be having.
And we are fundamentally, our thinking can be
and easily becomes fundamentally biased.
And there are statistics, and we shouldn't blind ourselves,
we shouldn't discard statistical inference,
but we should understand the nature of statistical inference.
As a society, we decide to reject statistical inference,
to favor understanding and deciding on the individual.
We consciously make that choice.
So even if the statistics said,
even if the statistics said males are more likely
to be violent criminals,
we still take each person as an individual,
and we treat them based on the logic
and the knowledge of that situation.
We purposefully and intentionally
reject the statistical inference.
We do that out of respect for the individual.
- For the individual, yeah,
and that requires reasoning and thinking.
Looking forward, what grand challenges
would you like to see in the future?
Because the Jeopardy challenge captivated the world,
AlphaGo, AlphaZero captivated the world,
Deep Blue certainly beating Kasparov,
Gary's bitterness aside, captivated the world.
What do you think, do you have ideas
for next grand challenges for future challenges of that?
- Look, I mean, I think there are lots
of really great ideas for grand challenges.
I'm particularly focused on one right now,
which is can you demonstrate that they understand,
that they could read and understand,
that they can acquire these frameworks
and reason and communicate with humans?
So it is kind of like the Turing test,
but it's a little bit more demanding than the Turing test.
It's not enough to convince me that you might be human
because you can parrot a conversation.
I think the standard is a little bit higher.
For example, can you, the standard is higher,
and I think one of the challenges
of devising this grand challenge
is that we're not sure what intelligence is.
We're not sure how to determine
whether or not two people actually understand each other
and in what depth they understand it,
to what depth they understand each other.
So the challenge becomes something along the lines
of can you satisfy me that we have a shared purpose
we have a shared understanding?
So if I were to probe and probe and you probe me,
can machines really act like thought partners
where they can satisfy me that we have a shared,
our understanding is shared enough
that we can collaborate and produce answers together
and that they can help me explain and justify those answers.
- So maybe here's an idea.
So we'll have AI system run for president and convince--
- That's too easy.
I'm sorry, go ahead.
- You have to convince the voters that they should vote.
So I guess what does winning look like?
- Again, that's why I think this is such a challenge
because we go back to the emotional persuasion.
We go back to, now we're checking off an aspect
of human cognition that is in many ways weak or flawed.
We're so easily manipulated.
Our minds are drawn for often the wrong reasons.
Not the reasons that ultimately matter to us,
but the reasons that can easily persuade us.
I think we can be persuaded to believe one thing or another
for reasons that ultimately don't serve us well
in the long term.
And a good benchmark should not play with those elements
of emotional manipulation.
- I don't think so.
And I think that's where we have to set the higher standard
for ourselves of what does it mean.
This goes back to rationality
and it goes back to objective thinking.
Can you produce, can you acquire information
and produce reasoned arguments?
And to those reasoned arguments,
§15Transcript · 2:02:56–2:11:24
pass a certain amount of muster.
And can you acquire new knowledge?
Can you, for example, can you reason,
I have acquired new knowledge,
can you identify where it's consistent or contradictory
with other things you've learned?
And can you explain that to me
and get me to understand that?
So I think another way to think about it perhaps
is can a machine teach you?
Can it help you-- - Oh, that's a really nice,
nice and then nice way to put it.
Can it help you understand--
- Can it help you understand something
that you didn't really understand before?
- Oh, that's a beautiful way to put it.
- Where it's taking you, so you're not,
again, it's almost like, can it teach you?
Can it help you learn?
And in an arbitrary space,
so it can open those domain space.
So can you tell the machine,
and again, this borrows from some science fictions,
but can you go off and learn about this topic
that I'd like to understand better
and then work with me to help me understand it?
- That's quite brilliant.
Well, a machine that passes that kind of test,
do you think it would need to have self-awareness
or even consciousness?
What do you think about consciousness
and the importance of it,
maybe in relation to having a body,
having a presence, an entity?
Do you think that's important?
- People used to ask me if Watson was conscious,
and I used to say, conscious of what exactly?
I mean, I think-- - Of self.
- It depends what it is that you're conscious of.
I mean, so did it,
it's certainly easy for it to answer questions about,
it would be trivial to program it
so that it'd answer questions about
whether or not it was playing Jeopardy.
I mean, it could certainly answer questions
that would imply that it was aware of things.
Exactly, what does it mean to be aware
and what does it mean to consciousness?
It's sort of interesting.
I mean, I think that we differ from one another
based on what we're conscious of.
- But wait, wait, yes, for sure.
There's degrees of consciousness in there, so--
- Well, and there's just areas.
Like, it's not just degrees.
What are you aware of?
Like, what are you not aware of?
- But nevertheless, there's a very subjective element
to our experience.
Let me even not talk about consciousness.
Let me talk about another, to me,
really interesting topic of mortality,
fear of mortality.
Watson, as far as I could tell,
did not have a fear of death.
- Certainly not.
- Most humans do.
- Wasn't conscious of death.
- He wasn't, yeah, so there's an element of finiteness
to our existence that I think, like you mentioned,
survival, that adds to the whole thing.
I mean, consciousness is tied up with that,
that we are a thing.
It's a subjective thing that ends,
and that seems to add a color and flavor
to our motivations in a way that seems
to be fundamentally important for intelligence,
or at least the kind of human intelligence.
- Well, I think for generating goals.
Again, I think you could have an intelligence capability
and a capability to learn, a capability to predict,
but I think without, I mean, again, you get a fear,
but essentially without the goal to survive.
- So you think you can just encode that
without having to really--
- I think you can encode that.
I mean, you can create a robot now,
and you could say, plug it in,
and say, protect your power source,
and give it some capabilities,
and it'll sit there and operate
to try to protect its power source and survive.
I mean, so I don't know that that's philosophically
a hard thing to demonstrate.
It sounds like a fairly easy thing to demonstrate
that you can give it that goal.
Well, it'll come up with that goal by itself,
and I think you have to program that goal in.
- But there's something, because I think,
as we touched on, intelligence is kind of
like a social construct.
The fact that a robot will be protecting its power source
would add depth and grounding to its intelligence
in terms of us being able to respect it.
I mean, ultimately, it boils down to us
acknowledging that it's intelligent,
and the fact that it can die,
I think, is an important part of that.
- The interesting thing to reflect on
is how trivial that would be,
and I don't think if you knew how trivial that was,
you would associate that with being intelligence.
I mean, I literally put in a statement of code
that says you have the following actions you can take.
You give it a bunch of actions,
like maybe you mount a laser gun on it,
or you have the ability to scream or screech or whatever,
and you say, if you see your power source threatened,
then you could program that in,
and you're gonna take these actions to protect it.
You could train it on a bunch of things.
So, and now you're gonna look at that,
and you're gonna say, well, that's intelligence,
because it's protecting its power source.
Maybe, but that's, again, this human bias that says,
the thing I, I identify my intelligence and my conscience
so fundamentally with the desire,
or at least the behaviors associated
with the desire to survive,
that if I see another thing doing that,
I'm going to assume it's intelligence.
- What timeline year will society have a,
something that would, that you would be comfortable calling
an artificial general intelligence system?
What's your intuition?
Nobody can predict the future,
certainly not the next few months or 20 years away,
but what's your intuition?
How far away are we?
- I don't know.
It's hard to make these predictions.
I mean, I would be, you know, I would be guessing,
and there's so many different variables,
including just how much we want to invest in it,
and how important it, you know,
and how important we think it is,
what kind of investment we're willing to make in it,
what kind of talent we end up bringing to the table,
all, you know, the incentive structure,
all these things.
So I think it is possible to do this sort of thing.
I think it's, I think trying to sort of ignore many
of the variables and things like that,
is it a 10-year thing?
Is it a 20-year?
It's probably closer to a 20-year thing, I guess.
- But not several hundred years.
- No, I don't think it's several hundred years.
I don't think it's several hundred years,
but again, so much depends on how committed we are
to investing and incentivizing this type of work.
And it's sort of interesting,
like I don't think it's obvious how incentivized we are.
I think from a task perspective, you know,
if we see business opportunities to take this technique
or that technique to solve that problem,
I think that's the main driver for many of these things.
From a general intelligence,
it's kind of an interesting question.
Are we really motivated to do that?
And like we just struggled ourselves right now
to even define what it is.
So it's hard to incentivize when we don't even know
what it is we're incentivized to create.
And if you said mimic a human intelligence,
I just think there are so many challenges
with the significance and meaning of that,
that there's not a clear directive.
There's no clear directive to do precisely that thing.
- So assistance in a larger and larger number of tasks.
So being able to, a system that's particularly able
to operate my microwave and making a grilled cheese sandwich,
I don't even know how to make one of those.
And then the same system would be doing the vacuum cleaning.
And then the same system would be teaching
my kids that I don't have math.
- I think that when you get into a general intelligence
for learning physical tasks,
and again, I wanna go back to your body question,
'cause I think your body question was interesting,
but you wanna go back to learning the abilities
to do physical tasks.
You might have, we might get,
I imagine in that timeframe,
we will get better and better
at learning these kinds of tasks,
whether it's mowing your lawn or driving a car
or whatever it is.
I think we will get better and better at that
where it's learning how to make predictions
§16Transcript · 2:11:25–2:20:30
over large bodies of data.
I think we're gonna continue
to get better and better at that.
And machines will outpace humans
in a variety of those things.
The underlying mechanisms for doing that may be the same,
meaning that maybe these are deep nets,
there's infrastructure to train them,
reusable components to get them
to do different classes of tasks,
and we get better and better
at building these kinds of machines.
You could still argue
that the general learning infrastructure in there
is a form of a general type of intelligence.
I think what starts getting harder
is this notion of,
can we effectively communicate and understand
and build that shared understanding
because of the layers of interpretation
that are required to do that,
and the need for the machine
to be engaged with humans at that level
in a continuous basis.
So how do you get in there?
How do you get the machine in the game?
How do you get the machine in the intellectual game?
- Yeah, and to solve AGI,
you probably have to solve that problem.
- You have to get the machine.
So it's a little bit of a bootstrapping thing.
Can we get the machine engaged in the intellectual,
I'm calling it a game,
but in the intellectual dialogue with the humans?
Are the humans sufficiently
in intellectual dialogue with each other
to generate enough data in this context?
And how do you bootstrap that?
Because every one of those conversations,
every one of those conversations,
those intelligent interactions
require so much prior knowledge
that it's a challenge to bootstrap it.
So the question is, and how committed?
So I think that's possible,
but when I go back to, are we incentivized to do that?
I know we're incentivized to do the former.
Are we incentivized to do the latter significantly enough?
Do people understand
what the latter really is well enough?
Part of the elemental cognition mission
is to try to articulate that better and better
through demonstrations
and through trying to craft these grand challenges
and get people to say,
look, this is a class of intelligence.
This is a class of AI.
Do we want this?
What is the potential of this?
What's the business potential?
What's the societal potential to that?
And to build up that incentive system around that.
- Yeah, I think if people don't understand yet,
I think they will.
I think there's a huge business potential here.
So it's exciting that you're working on it.
We kind of skipped over,
but I'm a huge fan of physical presence of things.
Do you think, you know, Watson had a body.
Do you think having a body adds to the interactive element
between the AI system and a human,
or just in general to intelligence?
- So I think going back to that shared understanding bit,
humans are very connected to their bodies.
I mean, one of the reasons,
one of the challenges in getting an AI
to kind of be a compatible human intelligence
is that our physical bodies are generating a lot of features
that make up the input.
So in other words, where our bodies are,
are the tool we use to affect output,
but they also generate a lot of input for our brains.
So we generate emotion, we generate all these feelings,
we generate all these signals that machines don't have.
So the machines that have this is the input data,
and they don't have the feedback that says,
okay, I've gotten this, I've gotten this emotion,
or I've gotten this idea, I now wanna process it,
and then I can, it then affects me as a physical being,
and then I can play that out.
In other words, I could realize the implications of that,
'cause the implications, again, on my mind-body complex,
I then process that, and the implications, again,
are internal features are generated, I learn from them,
they have an effect on my mind-body complex.
So it's interesting when we think,
do we want a human intelligence?
Well, if we want a human-compatible intelligence,
probably the best thing to do
is to embed it in a human body.
- Just to clarify, and both concepts are beautiful,
is humanoid robots, so robots that look like humans is one,
or did you mean actually sort of what Elon Musk
is working with Neuralink,
really embedding intelligence systems
to ride along human bodies?
- No, I mean, riding along is different.
I meant like if you wanna create an intelligence
that is human-compatible,
meaning that it can learn and develop
a shared understanding of the world around it,
you have to give it a lot of the same substrate.
Part of that substrate is the idea
that it generates these kinds of internal features,
the sort of emotional stuff, it has similar senses,
it has to do a lot of the same things
with those same senses, right?
So I think if you want that,
again, I don't know that you want that.
Like, that's not my specific goal.
I think that's a fascinating scientific goal.
I think it has all kinds of other implications.
That's sort of not the goal.
Like, I wanna create, I think of it
as I create intellectual thought partners for humans,
so that kind of intelligence.
I know there are other companies
that are creating physical thought partners.
Physical partners for humans.
But that's kind of not where I'm at.
But the important point is that a big part
of what we process is that physical experience
of the world around us.
- On the point of thought partners,
what role does an emotional connection,
or forgive me, love, have to play
in that thought partnership?
Is that something you're interested in,
put another way, sort of having a deep connection
beyond intellectual?
- With the AI?
- Yeah, with the AI, between human and AI.
Is that something that gets in the way
of the rational discourse?
Is that something that's useful?
- I worry about biases, you know, obviously.
So in other words, if you develop an emotional relationship
with a machine, all of a sudden you start,
are more likely to believe what it's saying,
even if it doesn't make any sense.
So I worry about that.
But at the same time, I think the opportunity
to use machines to provide human companionship
is actually not crazy.
Intellectual and social companionship is not a crazy idea.
- Do you have concerns, as a few people do,
Elon Musk, Sam Harris, about long-term existential threats
of AI and perhaps short-term threats of AI?
We talked about bias, we talked about different misuses,
but do you have concerns about thought partners,
systems that are able to help us make decisions
together with humans, somehow having a significant
negative impact on society in the long-term?
- I think there are things to worry about.
I think giving machines too much leverage is a problem.
And what I mean by leverage is too much control
over things that can hurt us, whether it's socially,
psychologically, intellectually, or physically.
And if you give the machines too much control,
I think that's a concern.
You forget about the AI, just when you give them
too much control, human bad actors can hack them
and produce havoc.
So that's a problem.
And you can imagine hackers taking over
the driverless car network and creating all kinds of havoc.
But you could also imagine, given the ease at which
humans could be persuaded one way or the other,
and now we have algorithms that can easily take control
over that and amplify noise and move people
one direction or another.
I mean, humans do that to other humans all the time.
And we have marketing campaigns, we have political campaigns
that take advantage of our emotions or our fears.
And this is done all the time.
But with machines, machines are like giant megaphones, right?
We can amplify this in orders of magnitude
and fine tune its control so we can tailor the message.
We can now very rapidly and efficiently tailor the message
to the audience, taking advantage of their biases
and amplifying them and using them to persuade them
in one direction or another in ways that are not fair,
not logical, not objective, not meaningful.
And machines empower that.
So that's what I mean by leverage.
Like, it's not new, but wow, it's powerful
because machines can do it more effectively,
more quickly, and we see that already going on
in social media and other places.
That's scary.
§17Transcript · 2:20:33–2:24:24
And that's why I go back to saying,
one of the most important public dialogues
we could be having is about the nature of intelligence
and the nature of inference and logic
and reason and rationality,
and us understanding our own biases,
us understanding our own cognitive biases
and how they work and then how machines work
and how do we use them to complement it basically
so that in the end we have a stronger overall system.
That's just incredibly important.
I don't think most people understand that.
So like telling your kids or telling your students,
this goes back to the cognition.
Here's how your brain works.
Here's how easy it is to trick your brain, right?
There are fundamental cognitive,
you should appreciate the different types of thinking
and how they work and what you're prone to
and what do you prefer and under what conditions
does this make sense versus that make sense?
And then say, here's what AI can do.
Here's how it can make this worse
and here's how it can make this better.
- And that's where the AI has a role
is to reveal that trade-off.
So if you imagine a system that is able to,
beyond any definition of the Turing test or the benchmark,
really an AGI system as a thought partner
that you one day will create,
what question, what topic of discussion
if you get to pick one, would you have with that system?
What would you ask and you get to find out
the truth together?
- So you threw me a little bit
with finding the truth at the end,
(laughing)
'cause the truth is a whole nother topic.
But I think the beauty of it,
I think what excites me is the beauty of it is
if I really have that system, I don't have to pick.
So in other words, I can go to it and say,
this is what I care about today.
And that's what we mean by it,
like this general capability.
Go out, read this stuff in the next three milliseconds.
And I wanna talk to you about it.
I wanna draw analogies.
I wanna understand how this affects
this decision or that decision.
What if this were true?
What if that were true?
What knowledge should I be aware of
that could impact my decision?
Here's what I'm thinking is the main implication.
Can you find, can you prove that out?
Can you give me the evidence that supports that?
Can you give me evidence that supports this other thing?
Boy, would that be incredible.
Would that be just incredible.
- Just a long discourse.
- Just to be part of, whether it's a medical diagnosis
or whether it's the various treatment options
or whether it's a legal case
or whether it's a social problem that people are discussing,
be part of the dialogue, one that holds itself and us
accountable to reasons and objective dialogue.
I get goosebumps talking about it.
It's like, this is what I want.
- So when you create it, please come back on the podcast
so we can have a discussion together
and make it even longer.
This is a record for the longest conversation ever.
It was an honor.
It was a pleasure, David.
Thank you so much for talking to me.
- Thanks so much, a lot of fun.
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