Sertac Karaman: Robots That Fly and Robots That Drive
Guests: Sertac Karaman
Full transcript · Read the full conversation in order, with its original context.
§01Transcript · 00:00–08:05
The following is a conversation with Sertac Karaman,
a professor at MIT,
co-founder of the autonomous vehicle company Optimus Ride,
and is one of the top roboticists in the world,
including robots that drive and robots that fly.
To me, personally, he has been a mentor,
a colleague, and a friend.
He's one of the smartest, most generous people I know.
So it was a pleasure and honor to finally sit down with him
for this recorded conversation.
This is the Artificial Intelligence Podcast.
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And now, here's my conversation with Sertac Karaman.
Since you have worked extensively on both,
what is the more difficult task,
autonomous flying or autonomous driving?
- That's a good question.
I think that autonomous flying,
just kind of doing it for consumer drones and so on,
the kinds of applications that we're looking at right now
is probably easier.
And so I think that that's maybe one of the reasons
why it took off, like literally,
a little earlier than the autonomous cars.
But I think if you look ahead,
I would think that the real benefits of autonomous flying,
unleashing them in like transportation, logistics,
and so on, I think it's a lot harder than autonomous driving.
So I think my guess is that we've seen
a few kind of machines fly here and there,
but we really haven't yet seen any kind of,
machine, like at massive scale,
large scale being deployed and flown and so on.
And I think that's gonna be after we kind of resolve
some of the large scale deployments of autonomous driving.
- So what's the hard part?
What's your intuition behind why at scale,
when consumer facing drones are tough?
- So I think in general, at scale is tough.
Like for example, when you think about it,
we have actually deployed a lot of robots
in the, let's say the past 50 years.
- We as academics or we business entrepreneur?
- I think we as humanity.
- Humanity?
- A lot of people working on it.
(laughing)
So we humans deployed a lot of robots.
And I think that, but when you think about it,
robots, they're autonomous, they work,
they work on their own,
but they are either like in isolated environments
or they are in sort of,
they may be at scale,
but they're really confined to a certain environment
that they don't interact so much with humans.
And so, they work in, I don't know,
factory floors, warehouses, they work on Mars,
they are fully autonomous over there.
But I think that the real challenge of our time
is to take these vehicles
and put them into places where humans are present.
So now I know that there's a lot of like human robot
interaction type of things that need to be done.
And so, that's one thing,
but even just from the fundamental algorithms and systems
and the business cases, or maybe the business models,
even like architecture, planning, societal issues,
legal issues, there's a whole bunch of pack of things
that are related to us putting robotic vehicles
into human present environments.
And these humans, they will not potentially be
even trained to interact with them.
They may not even be using the services
that are provided by these vehicles.
They may not even know that they're autonomous.
They're just doing their thing,
living in environments that are designed for humans,
not for robots.
And that I think is one of the biggest challenges,
I think, of our time, to put vehicles there.
And to go back to your question,
I think doing that at scale,
meaning you go out in a city
and you have thousands or tens of thousands
of autonomous vehicles that are going around.
It is so dense to the point where if you see one of them,
you look around, you see another one.
It is that dense.
And that density,
we've never done anything like that before.
And I would bet that that kind of density
will first happen with autonomous cars,
because I think we can bend the environment a little bit.
Especially kind of making them safe is a lot easier
when they're on the ground.
When they're in the air,
it's a little bit more complicated.
But I don't see that there's gonna be a big separation.
I think that there will come a time
that we're gonna quickly see these things unfold.
- Do you think there will be a time
where there's tens of thousands of delivery drones
that fill the sky?
- You know, I think it's possible, to be honest.
Delivery drones is one thing,
but you can imagine for transportation,
like an important use case is,
you know, we're in Boston,
you wanna go from Boston to New York.
And you wanna do it from the top of this building
to the top of another building in Manhattan.
And you're gonna do it in one and a half hours.
And that's a big opportunity, I think.
- Personal transport, so like you and me be a friend.
Like almost like Uber. - Yeah, or almost like an Uber.
So like four people, six people, eight people.
In our work in autonomous vehicles, I see that.
So there's kind of like a bit of a need
for one person transport, but also like a few people.
So you and I could take the trip together,
we could have lunch.
You know, I think kind of sounds crazy,
maybe even sounds a bit cheesy,
but I think that those kinds of things
are some of the real opportunities.
And I think, you know, it's not like the typical airplane
and the airport would disappear very quickly.
But I would think that, you know,
many people would feel like they would spend
an extra $100 on doing that,
and cutting that four hour travel
down to one and a half hours.
- So how feasible are flying cars?
It's been the dream.
It's like when people imagine the future for 50 plus years,
they think flying cars.
It's like all technologies, it's cheesy to think about now
because it seems so far away, but overnight it can change.
But just technically speaking, in your view,
how feasible is it to make that happen?
- I'll get to that question.
But just one thing is that I think, you know,
sometimes we think about what's gonna happen
in the next 50 years.
It's just really hard to guess, right?
Next 50 years, I don't know.
I mean, we could get what's gonna happen
in transportation in the next 50 years.
We could get flying saucers.
I could bet on that.
I think there's a 50/50 chance that, you know,
like you can build machines that can ionize the air
around them and push it down with magnets
and they would fly like a flying saucer.
That is possible.
And it might happen in the next 50 years.
So it's a bit hard to guess,
like when you think about 50 years before.
But I would think that, you know,
there's this kind of notion where
there's a certain type of airspace
that we call the agile airspace.
And there's good amount of opportunities in that airspace.
So that would be the space that is kind of
a little bit higher than the place
where you can throw a stone.
Because that's a tough thing when you think about it.
You know, it takes a kid and a stone
to take an aircraft down and then what happens?
But, you know, imagine the airspace that's high enough
so that you cannot throw a stone,
but it is low enough that you're not interacting
§02Transcript · 08:07–16:31
with the very large aircraft
that are, you know, flying several thousand feet above.
And that airspace is underutilized.
Or it's actually kind of not utilized at all.
- Yeah, that's right.
- So there's, you know, there's like recreational people
kind of fly every now and then, but it's very few.
Like if you look up in the sky,
you may not see any of them at any given time.
Every now and then you'll see one airplane
kind of utilizing that space and you'll be surprised.
And the moment you're outside of an airport a little bit,
like it just kind of flies off and then it goes out.
And I think utilizing that airspace,
the technical challenges there is, you know,
building an autonomy and ensuring
that that kind of autonomy is safe.
Ultimately, I think it is going to be building
in complex software or complicated
so that it's maybe a few orders of magnitude
more complicated than what we have on aircraft today.
And at the same time, ensuring just like we ensure
on aircraft, ensuring that it's safe.
And so that becomes like building that kind
of complicated hardware and software becomes a challenge.
Especially when, you know, you build that hardware,
I mean, you build that software with data.
And so, you know, it's, of course,
there's some rule-based software in there
that kind of do a certain set of things,
but then, you know, there's a lot of training there.
- Do you think machine learning will be key
to delivering safe vehicles in the future,
especially flight?
- Not maybe the safe part,
but I think the intelligent part.
I mean, there are certain things
that we do it with machine learning
and it's just, there's like right now no other way.
And I don't know how else they could be done.
And, you know, there's always this conundrum.
I mean, we could maybe gather billions of programmers,
humans who program perception algorithms
that detect things in the sky and whatever,
or, you know, we, I don't know,
we maybe even have robots like learning
a simulation environment and transfer.
And they might be learning a lot better
in a simulation environment than a billion humans
put their brains together and try to program.
Humans, pretty limited.
- So what's the role of simulations with drones?
You've done quite a bit of work there.
How promising, just the very thing you said just now,
how promising is the possibility of training
and developing a safe flying robot in simulation
and deploying it and having that work pretty well
in the real world?
- I think that, you know, a lot of people,
when they hear simulation,
they will focus on training immediately.
But I think one thing that you said,
which was interesting, it's developing.
I think simulation environments actually could be key
and great for development.
And that's not new.
Like for example, you know, there's people
in the automotive industry have been using
dynamic simulation for like decades now.
And it's pretty standard that, you know,
you would build and you would simulate.
If you want to build an embedded controller,
you plug that kind of embedded computer
into another computer,
that other computer would simulate and so on.
And I think, you know, fast forward these things,
you can create pretty crazy simulation environments.
Like for instance, one of the things
that has happened recently and that, you know,
we can do now is that we can simulate cameras
a lot better than we used to simulate them.
We were able to simulate them before.
And that's, I think we just hit the elbow
on that kind of improvement.
I would imagine that with improvements in hardware,
especially, and with improvements in machine learning,
I think that we would get to a point
where we can simulate cameras very, very well.
- Simulate cameras means simulate how a real camera
would see the real world.
Therefore you can explore the limitations of that.
You can train perception algorithms on that in simulation,
all that kind of stuff.
- Exactly.
So, you know, it has been easier to simulate
what we would call interceptive sensors,
like internal sensors.
So for example, inertial sensing has been easy to simulate.
It has also been easy to simulate dynamics,
like physics that are governed
by ordinary differential equations.
I mean, like how a car goes around,
maybe how it rolls on the road,
how it interacts with the road,
or even an aircraft flying around,
like the dynamic physics of that.
What has been really hard
has been to simulate extraceptive sensors,
sensors that kind of like look out from the vehicle.
And that's a new thing that's coming,
like laser range finders that are a little bit easier.
Cameras, radars are a little bit tougher.
I think once we nail that down,
the next challenge I think in simulation
will be to simulate human behavior.
That's also extremely hard.
Even when you imagine like how a human driven car
would act around, even that is hard.
But imagine trying to simulate, you know,
a model of a human just doing a bunch of gestures
and so on, and you know, it's actually simulated.
It's not captured like with motion capture,
but it is simulated.
That's very hard.
In fact, today I get involved a lot
with like sort of this kind of
very high end rendering projects.
And I have like this test that I pass it to my friends
or my mom, you know, I send like two photos,
two kind of pictures and I say,
rendered, which one is rendered, which one is real?
And it's pretty hard to distinguish,
except I realized, except when we put humans in there.
It's possible that our brains are trained in a way
that we recognize humans extremely well.
But we don't so much recognize the built environments
because built environments sort of came after per se,
we evolved into sort of being humans,
but humans were always there.
Same thing happens, for example,
you look at like monkeys and you can't distinguish
one from another, but they sort of do.
And it's very possible that they look at humans,
it's kind of pretty hard to distinguish one from another,
but we do.
And so our eyes are pretty well trained to look at humans
and understand if something is off, we will get it.
We may not be able to pinpoint it.
So in my typical friend test or mom test,
what would happen is that we'd put like a human walking
in a thing and they say, this is not right.
Something is off in this video.
I don't know what, but I can tell you it's the human.
I can take the human and I can show you like inside
of a building or like an apartment,
and it will look like if we had time to render it,
it will look great.
And this should be no surprise,
a lot of movies that people are watching,
it's all computer generated.
Nowadays, even you watch a drama movie
and like there's nothing going on action wise,
but it turns out it's kind of like cheaper,
I guess to render the background.
And so they would.
- But how do we get there?
How do we get a human that's would pass the mom/friend test,
a simulation of a human walking?
So do you think that's something we can creep up to
by just doing kind of a comparison learning
where you have humans annotate what's more realistic
and not just by watching?
Like what's the path?
'Cause it seems totally mysterious
how we simulate human behavior.
- It's hard because a lot of the other things
that I mentioned to you, including simulating cameras,
it is, the thing there is that we know the physics,
we know how it works like in the real world,
and we can write some rules and we can do that.
Like for example, simulating cameras,
there's this thing called ray tracing.
I mean, you literally just kind of imagine,
it's very similar to, it's not exactly the same,
but it's very similar to tracing photon by photon.
They're going around, bouncing on things
and coming to your eye.
But human behavior, developing a dynamic,
like a model of that, that is mathematical
so that you can put it into a processor
that would go through that, that's gonna be hard.
And so what else do you got?
You can collect data, right?
And you can try to match the data.
Or another thing that you can do is that,
you can show the front test, you can say this or that
and this or that, and that will be labeling.
Anything that requires human labeling,
ultimately we're limited by the number of humans
that we have available at our disposal
§03Transcript · 16:33–25:31
and the things that they can do.
They have to do a lot of other things
than also labeling this data.
So that modeling human behavior part is, I think,
we're gonna realize it's very tough.
And I think that also affects
our development of autonomous vehicles.
I see that in self-driving as well.
Like you wanna use, so you're building self-driving,
at the first time, like right after Urban Challenge,
I think everybody focused on localization,
mapping and localization.
Slam algorithms came in, Google was just doing that.
And so building these HD maps,
basically that's about knowing where you are.
And then five years later in 2012, 2013,
came the kind of coding code AI revolution
and that started telling us where everybody else is.
But we're still missing
what everybody else is gonna do next.
And so you wanna know where you are,
you wanna know what everybody else is,
hopefully you know what you're gonna do next
and then you wanna predict what other people
are going to do and that last bit
has been a real challenge.
- What do you think is the role, your own,
of your, the ego vehicle, the robot,
the you, the robotic you in controlling
and having some control of how the future unrolls,
of what's gonna happen in the future?
That seems to be a little bit ignored
in trying to predict the future
is how you yourself can affect that future
by being either aggressive or less aggressive
or signaling in some kind of way,
sort of this kind of game theoretic dance.
Seems to be ignored for the moment.
- It's, yeah, it's totally ignored.
I mean, it's quite interesting actually,
like how we interact with things
versus we interact with humans.
Like so if you see a vehicle that's completely empty
and it's trying to do something,
all of a sudden it becomes a thing.
So interact it with, like you interact with this table
and so you can throw your backpack
or you can kick it, put your feet on it
and things like that.
But when it's a human,
there's all kinds of ways of interacting with a human.
So if, like you and I are face to face, we're very civil,
we talk and we understand each other for the most part.
We'll see, you just, that's done.
You never know what's gonna happen.
But the thing is that, like for example,
you and I might interact through YouTube comments
and the conversation may go at a totally different angle.
And so I think people kind of abusing these autonomous
vehicles is a real issue in some sense.
And so when you're an ego vehicle,
you're trying to coordinate your way, make your way,
it's actually kind of harder than being a human.
It's like, you not only need to be as smart
as kind of humans are, but you also, you're a thing.
So they're gonna abuse you a little bit.
So you need to make sure that you can get around
and do something.
So I, in general, believe in that sort of
game theoretic aspects.
I've actually personally have done quite a few papers,
both on that kind of game theory and also like this kind of
understanding people's social value orientation,
for example.
Some people are aggressive, some people not so much.
And a robot could understand that by just looking
at how people drive.
And as they kind of come and approach,
you can actually understand, like if someone is gonna
be aggressive or not as a robot,
and you can make certain decisions.
- Well, in terms of predicting what they're going to do,
the hard question is you as a robot,
should you be aggressive or not?
When faced with an aggressive robot.
Right now, it seems like aggressive is a very dangerous
thing to do because it's costly from a societal perspective,
how you're perceived.
People are not very accepting of aggressive robots
in modern society.
- I think that's accurate.
So, it really is.
And so I'm not entirely sure how to go about,
but I know for a fact that how these robots interact
with other people in there is going to be,
and that interaction is always gonna be there.
I mean, you could be interacting with other vehicles
or other just people kind of like walking around.
And like I said, the moment there's nobody in the seat,
it's like an empty thing just rolling off the street.
It becomes like no different than any other thing
that's not human.
And so people, and maybe abuse is the wrong word,
but people, maybe rightfully even, they feel like,
this is a human present environment,
it's designed for humans to be,
and they kind of, they want to own it.
And then the robots, they would need to understand it
and they would need to respond in a certain way.
And I think that this actually opens up
quite a few interesting societal questions for us
as we deploy, like we talk, robots at large scale.
So what would happen when we try to deploy robots
at large scale, I think is that we can design systems
in a way that they're very efficient,
or we can design them that they're very sustainable.
But ultimately the sustainability efficiency trade-offs,
like they're gonna be right in there,
and we're gonna have to make some choices.
Like we're not gonna be able to just kind of put it aside.
So for example, we can be very aggressive
and we can reduce transportation delays,
increase capacity of transportation,
or we can be a lot nicer and allow other people
to kind of quote unquote, own the environment
and live in a nice place, and then efficiency will drop.
So when you think about it,
I think sustainability gets attached to energy consumption
or environmental impact immediately.
And those are there, but like livability
is another sustainability impact.
So you create an environment that people wanna live in.
And if robots are going around being aggressive,
you don't wanna live in that environment, maybe.
However, you should note that if you're not being aggressive
then you're probably taking up some delays
in transportation and this and that.
So you're always balancing that.
And I think this choice has always been there
in transportation, but I think the more autonomy comes in,
the more explicit the choice becomes.
- Yeah, and when it becomes explicit,
then we can start to optimize it.
And then we'll get to ask the very difficult societal
questions of what do we value more, efficiency
or sustainability?
It's kind of interesting.
- I think that will happen.
I think we're gonna have to like,
I think that the interesting thing about like
the whole autonomous vehicles question,
I think is also kind of, I think a lot of times,
you know, we have focused on technology development,
like hundreds of years and, you know,
the products somehow followed and then, you know,
we got to make these choices and things like that.
But this is a good time that, you know,
we even think about, you know, autonomous taxi type
of deployments and the systems that would evolve from there.
And you realize the business models are different,
the impact on architecture is different, urban planning,
you get into like regulations,
and then you get into like these issues that you didn't
think about before, but like sustainability and ethics
is like right in the middle of it.
I mean, even testing autonomous vehicles,
like think about it, you're testing autonomous vehicles
in human present environments.
I mean, the risk may be very small, but still, you know,
it's a strictly greater than zero risk
that you're putting people into.
And so then you have that innovation, you know,
risk trade off that you're in that somewhere.
And we understand that pretty well now is that
if we don't test, at least the development will be slower.
I mean, it doesn't mean that we're not gonna be able
to develop, I think it's gonna be pretty hard actually,
maybe we can, I don't know, but the thing is that
those kinds of trade offs we already are making.
And as these systems become more ubiquitous,
I think those trade offs will just really hit.
- So you are one of the founders of Optimus Ride,
an autonomous vehicle company, we'll talk about it.
But let me, on that point, ask maybe good examples,
keeping Optimus Ride out of this question,
sort of exemplars of different strategies
on the spectrum of innovation and safety or caution.
So like Waymo, Google self-driving car,
Waymo represents maybe a more cautious approach.
And then you have Tesla on the other side,
headed by Elon Musk that represents a more,
however, which adjective you wanna use,
aggressive, innovative, I don't know.
But what do you think about the difference
between the two strategies in your view?
What's more likely, what's needed
and is more likely to succeed in the short term
and the long term?
- Definitely some sort of a balance
is kind of the right way to go.
§04Transcript · 25:33–33:43
But I do think that the thing that is the most important
is actually like an informed public.
So I don't mind, I personally,
like if I were in some place, I wouldn't mind so much,
like taking a certain amount of risk.
Some other people might.
And so I think the key is for people to be informed
and so that they can, ideally, they can make a choice.
In some cases, that kind of choice,
making that unanimously is of course very hard.
But I don't think it's actually that hard to inform people.
So I think in one case, like for example,
even the Tesla approach, I don't know,
it's hard to judge how informed it is,
but it is somewhat informed.
I mean, things kind of come out,
I think people know what they're taking
and things like that and so on.
But I think the underlying,
I do think that these two companies
are a little bit kind of representing like,
of course, one of them seems a bit safer,
the other one or whatever the objective for that is
and the other one seems more aggressive
or whatever the objective for that is.
But I think when you turn the tables,
there are actually two other orthogonal dimensions
that these two are focusing on.
On the one hand, for Waymo, I can see that they're,
I mean, I think they a little bit see it as research as well.
So they kind of, I'm not sure if they're like
really interested in like an immediate product.
They talk about it.
Sometimes there's some pressure to talk about it.
So they kind of go for it.
But I think that they're thinking,
maybe in the back of their minds,
maybe they don't put it this way,
but I think they realize
that we're building like a new engine.
It's kind of like call it the AI engine
or whatever that is.
And autonomous vehicles is a very interesting embodiment
of that engine that allows you to understand
where the ego vehicle is, the ego thing is,
where everything else is, what everything else is gonna do
and how do you react?
How do you actually interact with humans the right way?
How do you build these systems?
And I think they wanna know that.
They wanna understand that.
And so they keep going and doing that.
And so on the other dimension,
Tesla is doing something interesting.
I mean, I think that they have a good product.
People use it.
I think that, you know, like it's not for me,
but I can totally see people like it.
And people, I think they have a good product
outside of automation,
but I was just referring to the automation itself.
I mean, you know, like it kind of drives itself.
You still have to be kind of,
you still have to pay attention to it, right?
But you know, people seem to use it.
So it works for something.
And so people, I think people are willing to pay for it.
People are willing to buy it.
I think it's one of the other reasons
why people buy a Tesla car.
Maybe one of those reasons is Elon Musk is the CEO.
And you know, he seems like a visionary person.
That's what people think.
He seems like a visionary person.
And so that adds like 5K to the value of the car.
And then maybe another 5K is the autopilot.
And you know, it's useful.
I mean, it's useful in the sense
that like people are using it.
And so I can see Tesla, sure,
of course they want to be visionary.
They want to kind of put out a certain approach
and they may actually get there.
But I think that there's also a primary benefit
of doing all these updates and rolling it out
because people pay for it.
And it's basic, you know, demand, supply, market
and people like it.
They're happy to pay another 5K, 10K for that novelty
or whatever that is.
And they use it.
It's not like they get it and they try it a couple of times.
It's a novelty, but they use it a lot of the time.
And so I think that's what Tesla is doing.
It's actually pretty different.
Like they are on pretty orthogonal dimensions
of what kind of things that they're building.
They are using the same AI engine.
So it's very possible that, you know,
they're both going to be sort of one day
kind of using a similar, almost like an internal
combustion engine.
It's a very bad metaphor,
but similar internal combustion engine.
And maybe one of them is building like a car.
The other one is building a truck or something.
So ultimately the use case is very different.
- So you, like I said, are one of the founders
of Optimus Ride.
Let's take a step back.
It's one of the success stories
in the autonomous vehicle space.
It's a great autonomous vehicle company.
Let's go from the very beginning.
What does it take to start an autonomous vehicle company?
How do you go from idea to deploying vehicles
like you are in a bunch of places, including New York?
- I would say that, I think that, you know,
what happened to us is it was the following.
I think we've realized a lot of kind of talk
in the autonomous vehicle industry back in like 2014, even,
when we wanted to kind of get started.
And I don't know, like I kind of,
I would hear things like fully autonomous vehicles
two years from now, three years from now.
I kind of never bought it.
You know, I was a part of MIT's Urban Challenge Entry.
It kind of like, it has an interesting history.
So I did in college and in high school,
sort of a lot of mathematically oriented work.
And I think I kind of, you know, at some point
it kind of hit me.
I wanted to build something.
And so I came to MIT's mechanical engineering program.
And I now realize, I think my advisor hired me
because I could do like really good math.
But I told him that, no, no, no,
I want to work on that urban challenge car.
I want to build the autonomous car.
And I think that was kind of like a process
where we really learned, I mean, what the challenges are
and what kind of limitations are we up against?
You know, like having the limitations of computers
or understanding human behavior.
There's so many of these things.
And I think it just kind of didn't.
And so we said, hey, you know, like,
why don't we take a more like a market-based approach?
So we focus on a certain kind of market
and we build a system for that.
What we're building is not so much
of like an autonomous vehicle only, I would say.
So we build full autonomy into the vehicles.
But you know, the way we kind of see it is that
we think that the approach should actually involve humans
operating them, not just not sitting in the vehicle.
And I think today, what we have is today,
we have one person operate one vehicle,
no matter what that vehicle.
It could be a forklift, it could be a truck,
it could be a car, whatever that is.
And we want to go from that
to 10 people operate 50 vehicles.
How do we do that?
- You're referring to a world of maybe perhaps
teleoperation.
So can you just say what it means for 10?
Might be confusing for people listening.
What does it mean for 10 people to control 50 vehicles?
- That's a good point.
So I think it's, I very deliberately
didn't call it teleoperation.
'Cause what people think then is that people think
away from the vehicle sits a person,
sees like maybe puts on goggles or something,
VR and drives the car.
So that's not at all what we mean.
But we mean the kind of intelligence whereby
humans are in control, except in certain places,
the vehicles can execute on their own.
And so imagine like a room where people can see
what the other vehicles are doing and everything.
And there will be some people who are more like
air traffic controllers, call them like AV controllers.
And so these AV controllers would actually see
kind of like a whole map.
And they would understand why vehicles are really confident
and where they kind of need a little bit more help.
And the help shouldn't be for safety.
Help should be for efficiency.
Vehicles should be safe no matter what.
If you had zero people, they could be very safe,
but they'd be going five miles an hour.
And so if you want them to go around 25 miles an hour,
then you need people to come in.
And for example, the vehicle come to an intersection
and the vehicle can say, I can wait,
I can inch forward a little bit,
show my intent or I can turn left.
And right now it's clear, I can turn, I know that,
§05Transcript · 33:47–41:40
but before you give me the go, I won't.
And so that's one example.
This doesn't mean necessarily we're doing that actually.
I think if you go down all that much detail
that every intersection,
you're kind of expecting a person to press a button,
then I don't think you'll get
the efficiency benefits you want.
You need to be able to kind of go around
and be able to do these things.
But I think you need people to be able to set
high level behavior to vehicles.
That's the other thing with autonomous vehicles.
I think a lot of people kind of think about it as follows.
I mean, this happens with technology a lot.
You think, all right, so I know about cars
and I heard robots.
So I think how this is gonna work out is that
I'm gonna buy a car, press a button,
and it's gonna drive itself.
And when is that gonna happen?
And people kind of tend to think about it that way.
But when you think about what really happens
is that something comes in
in a way that you didn't even expect.
If asked, you might have said, I don't think I need that,
or I don't think it should be that and so on.
And then that becomes the next big thing, coding code.
And so I think that this kind of different ways
of humans operating vehicles could be really powerful.
I think that sooner than later,
we might open our eyes up to a world in which
you go around walking a mall
and there's a bunch of security robots
that are exactly operated in this way.
You go into a factory or a warehouse,
there's a whole bunch of robots
that are operated exactly in this way.
You go to the Brooklyn Navy Yard,
you see a whole bunch of autonomous vehicles, Optimus ride,
and they're operated maybe in this way.
But I think people kind of don't see that.
I sincerely think that there's a possibility
that we may almost see like a whole mushrooming
of this technology in all kinds of places
that we didn't expect before.
And that may be the real surprise.
And then one day when your car actually drives itself,
it may not be all that much of a surprise at all
because you see it all the time, you interact with them,
you take the Optimus ride, hopefully that's your choice.
And then you hear a bunch of things,
you go around, you interact with them.
I don't know, like you have a little delivery vehicle
that goes around the sidewalks and delivers you things
and then you take it, it says, "Thank you."
And then you get used to that.
And one day your car actually drives itself
and the regulation goes by
and you can hit the button and sleep.
And it wouldn't be a surprise at all.
I think that may be the real reality.
- So there's gonna be a bunch of applications
that pop up around autonomous vehicles.
Some of which, maybe many of which we don't expect at all.
So if we look at Optimus ride, what do you think,
the viral application,
the one that like really works for people in mobility,
what do you think Optimus ride will connect with
in the near future first?
- I think that the first places that I like to target,
honestly, is like these places where transportation
is required within an environment,
like people typically call it geofence.
So you can imagine like roughly two mile by two mile,
could be bigger, could be smaller type of an environment.
And there's a lot of these kinds of environments
that are typically transportation deprived.
The Brooklyn Navy Yard that we're in today,
we're in a few different places,
but that was the one that was last publicized.
And that's a good example.
So there's not a lot of transportation there.
And you wouldn't expect, like, I don't know,
I think maybe operating an Uber there
ends up being sort of a little too expensive.
Or when you compare it with operating Uber elsewhere,
that becomes the elsewhere becomes the priority.
And these people,
those places become totally transportation deprived.
And then what happens is that,
people drive into these places
and to go from point A to point B inside this place,
within that day, they use their cars.
And so we end up building more parking
for them to, for example,
take their cars and go to a lunch place.
And I think that one of the things that can be done
is that you can put in efficient, safe,
sustainable transportation systems
into these types of places first.
And I think that you could deliver mobility
in an affordable way, affordable, accessible,
sustainable way.
But I think what also enables is that this kind of effort,
money, area, land that we spend on parking,
we could reclaim some of that.
And that is on the order of like,
even for a small environment, like two mile by two mile,
it doesn't have to be smack in the middle of New York.
I mean, anywhere else,
you're talking tens of millions of dollars.
If you're smack in the middle of New York,
you're looking at billions of dollars of savings
just by doing that.
And that's the economic part of it.
And there's a societal part, right?
I mean, just look around.
I mean, the places that we live are like built for cars.
It didn't look like this just like a hundred years ago.
Like today, no one walks in the middle of the street.
It's for cars.
No one tells you that growing up,
but you grow into that reality.
And so sometimes they close the road, it happens here.
You know, like the celebration, they close the road,
still people don't walk in the middle of the road,
like just walk in the middle and people don't.
But I think it has so much impact,
the car in the space that we have.
And I think we talked about sustainability, livability.
I mean, ultimately these kinds of places
that parking spots at the very least
could change into something more useful
or maybe just like park areas, recreational.
And so I think that's the first thing that we're targeting.
And I think that we're getting like a really good response,
both from an economic societal point of view,
especially places that are a little bit forward looking.
And like, for example, Brooklyn Navy Yard,
they have tenants, there's distinct,
they're called like New Lab.
It's kind of like an innovation center.
There's a bunch of startups there.
And so, you know, you get those kinds of people
and you know, they're really interested in
sort of making that environment more livable.
And these kinds of solutions that Optimist Ride provides
almost kind of comes in and becomes that.
And many of these places that are transportation deprived,
you know, they actually rent shuttles.
And so, you know, you can ask anybody,
the shuttle experience is like terrible.
People hate shuttles.
And I can tell you why.
It's because, you know, like the driver is very expensive
in a shuttle business.
So what makes sense is to attach 20, 30 seats to a driver.
And a lot of people have this misconception.
They think that shuttles should be big.
Sometimes we get that at Optimist Ride.
We tell them we're going to give you like four-seaters,
six-seaters, and we get asked like,
"How about like 20-seaters?"
I'm like, you know, you don't need 20-seaters.
You want to split up those seats
so that they can travel faster
and the transportation delays would go down.
That's what you want.
If you make it big,
not only you will get delays in transportation,
but you won't have an agile vehicle.
It will take a long time to speed up, slow down, and so on.
You need to climb up to the thing.
So it's kind of like really hard to interact with.
- And scheduling too,
perhaps when you have more smaller vehicles,
it becomes closer to Uber
where you can actually get a personal,
I mean, just the logistics of getting the vehicle to you
becomes easier when you have a giant shuttle.
There's fewer of them,
and it probably goes on a route,
a specific route that it's supposed to hit.
- And when you go on a specific route
and all seats travel together
versus, you know, you have a whole bunch of them,
you can imagine the route you can still have,
but you can imagine you split up the seats
and instead of, you know, them traveling,
like, I don't know, a mile apart,
they could be like, you know, half a mile apart
if you split them into two.
That basically would mean that your delays,
when you go out, you won't wait for them for a long time.
And that's one of the main reasons,
or you don't have to climb up.
The other thing is that I think
if you split them up in a nice way,
and if you can actually know
§06Transcript · 41:42–50:06
where people are going to be somehow,
you don't even need the app.
A lot of people ask us the app.
We say, "Why don't you just walk into the vehicle?
How about you just walk into the vehicle,
it recognizes who you are,
and it gives you a bunch of options of places that you go,
and you just kind of go there."
I mean, people kind of also internalize the apps.
Everybody needs an app.
It's like, you don't need an app,
you just walk into the thing.
- You just walk up.
- But I think one of the things that, you know,
we really try to do is to take that shuttle experience
that no one likes and tilt it into something
that everybody loves.
And so I think that's another important thing.
I would like to say that carefully,
just like teleoperation, we don't do shuttles.
You know, we're really kind of thinking of this as a system
or a network that we're designing.
But ultimately, we go to places
that would normally rent a shuttle service
that people wouldn't like as much,
and we want to tilt it into something that people love.
- So you've mentioned this actually earlier,
but how many Optimus ride vehicles
do you think would be needed
for any person in Boston or New York?
If they step outside, there will be,
this is like a mathematical question,
there'll be two Optimus ride vehicles within line of sight.
Is that the right number?
Two, well, at least one.
- Yeah, like for example, that's the density.
So meaning that if you see one vehicle,
you look around, you see another one too.
Imagine like, you know, Tesla will tell you
they collect a lot of data.
Do you see that with Tesla?
Like you just walk around and you look around,
you see Tesla?
Probably not.
- Very specific areas of California, maybe.
- Maybe.
You're right.
Like there's a couple zip codes that, you know.
But I think that's kind of important
because you know, like maybe the couple zip codes.
The one thing that we kind of depend on,
and I'll get to your question in a second,
but now like we're taking a lot of tangents today.
- Hell yes.
- And so I think that this is actually important.
People call this data density or data velocity.
So it's very good to collect data in a way that,
you know, you see the same place so many times.
Like you can drive 10,000 miles around the country,
or you drive 10,000 miles in a confined environment.
You'll see the same intersection hundreds of times.
And when it comes to predicting what people are gonna do
in that specific intersection, you become really good at it.
Versus if you draw on like 10,000 miles around the country,
you've seen that only once.
And so trying to predict what people do becomes hard.
And I think that, you know, you said what is needed.
It's tens of thousands of vehicles.
You know, you really need to be
like a specific fraction of vehicle.
Like for example, in good times in Singapore,
you can go and you can just grab a cab.
And they are like, you know, 10%, 20% of traffic,
those taxis.
Ultimately, that's where you need to get to.
So that, you know, you get to a certain place
where you really, the benefits really kick off
in like orders of magnitude type of a point.
But once you get there, you actually get the benefits.
And you can certainly carry people.
I think that's one of the things.
People really don't like to wait for themselves.
But for example, they can wait a lot more for the goods
if they order something.
Like if you're sitting at home
and you wanna wait half an hour, that sounds great.
People will say, it's great.
You're gonna take a cab, you're waiting half an hour.
Like that's crazy.
You don't wanna wait that much.
But I think, you know, you can, I think,
really get to a point where the system,
at peak times, really focuses on
kind of transporting humans around.
And then it's really, it's a good fraction of traffic
to the point where, you know, you go, you look around
and there's something there
and you just kind of basically get in there.
And it's already waiting for you or something like that.
And then you take it.
If you do it at that scale, like today, for instance, Uber,
if you talk to a driver, right?
I mean, Uber takes a certain cut.
It's a small cut.
Or drivers would argue that it's a large cut.
But, you know, it's, when you look at
the grand scheme of things,
most of that money that you pay Uber
kind of goes to the driver.
And if you talk to the driver,
the driver will claim that most of it is their time.
You know, it's not spent on gas, they think.
It's not spent on the car per se as much.
It's like their time.
And if you didn't have a person driving,
or if you're in a scenario where, you know,
like 0.1 person is driving the car,
a fraction of a person is kind of operating the car,
because, you know, one operates several.
If you're in that situation,
you realize that the internal combustion engine
type of cars are very inefficient.
You know, we build them to go on highways,
they pass crash tests, they're like really heavy.
They really don't need to be like 25 times
the weight of its passengers, or, you know,
like area-wise and so on.
But if you get through those inefficiencies,
and if you really build like urban cars
and things like that,
I think the economics really starts to check out,
like to the point where, I mean, I don't know,
you may be able to get into a car,
and it may be less than a dollar to go from A to B.
As long as you don't change your destination,
you just pay 99 cents and go there.
If you share it, if you take another stop somewhere,
it becomes a lot better.
You know, these kinds of things, at least for models,
at least for mathematics and theory,
they start to really check out.
- So I think it's really exciting
what Optimus Ride is doing in terms of,
it feels the most reachable,
like it'll actually be here and have an impact.
- Yeah, that is the idea.
- And if we contrast that, again,
we'll go back to our old friends, Waymo and Tesla.
So Waymo seems to have sort of technically similar approaches
as Optimus Ride, but a different,
they're not as interested as having impact today.
They have a longer-term sort of investment.
It's almost more of a research project still,
meaning they're trying to solve, as far as I understand,
maybe you can differentiate,
but they seem to want to do more unrestricted movement,
meaning move from A to B, where A to B is all over the place,
versus Optimus Ride is really nicely geo-fenced
and really sort of establish mobility
in a particular environment before you expand it.
And then Tesla is like the complete opposite,
which is the entirety of the world, actually,
is going to be automated.
Highway driving, urban driving, every kind of driving,
you kind of creep up to it by incrementally improving
the capabilities of the autopilot system.
So when you contrast all of these,
and on top of that, let me throw a question
that nobody likes, but is timeline.
When do you think each of these approaches,
loosely speaking, nobody can predict the future,
will see mass deployment?
So Musk predicts the craziest approach is,
I've heard figures like at the end of this year, right?
So that's probably wildly inaccurate,
but how wildly inaccurate is it?
- I mean, first thing to lay out, like everybody else,
it's really hard to guess.
I mean, I don't know where Tesla can look at,
or Elon Musk can look at and say,
hey, it's the end of this year.
I mean, I don't know what you can look at.
You know, even the data that, I mean,
if you look at the data, even kind of trying
to extrapolate the end state without knowing
what exactly is gonna go, especially for like
a machine learning approach.
I mean, it's just kind of very hard to predict,
but I do think the following does happen.
I think a lot of people, you know what they do
is that there's something that I called a couple times
time dilation in technology prediction happens.
Let me try to describe a little bit.
There's a lot of things that are so far ahead,
people think they're close.
And there's a lot of things that are actually close,
people think it's far ahead.
People try to kind of look at a whole landscape
of technology development.
Admittedly, it's chaos.
§07Transcript · 50:08–57:59
Anything can happen in any order at any time.
And there's a whole bunch of things in there.
People take it, clamp it,
and put it into the next three years.
And so then what happens is that there's some things
that maybe can happen by the end of the year
or next year and so on.
And they push that into like few years ahead
because it's just hard to explain.
And there are things that are like,
we're looking at 20 years more maybe,
hopefully in my lifetime type of things.
And 'cause we don't know.
I mean, we don't know how hard it is even.
Like that's a problem.
We don't know like if some of these problems
are actually AI complete.
Like we have no idea what's going on.
And we take all of that and then we clamp it
and then we say three years from now.
And then some of us are more optimistic.
So they're shooting at the end of the year.
And some of us are more realistic.
They say like five years, but we all,
I think it's just hard to know.
And I think trying to predict like products ahead,
two, three years, it's hard to know in the following sense.
You know, like we typically say,
okay, this is a technology company,
but sometimes really you're trying to build something
where the technology does,
like there's a technology gap.
And Tesla had that with electric vehicles.
You know, like when they first started,
they would look at a chart,
much like a Moore's law type of chart.
And they would just kind of extrapolate that out.
And they'd say, we want to be here.
What's the technology to get that?
We don't know.
It goes like this.
So it's probably just going to keep going.
With AI that goes into the cars, we don't even have that.
Like we can't, I mean, what can you quantify?
Like what kind of chart are you looking at?
You know?
But so I think when there's that technology gap,
it's just kind of really hard to predict.
So now I realize I talked like five minutes
and I avoided your question.
I didn't tell you anything about that.
It was very skillfully done.
- That was very well done.
And I don't think you,
I think you've actually argued that it's not a use,
even any answer you provide now is not that useful.
- It's going to be very hard.
There's one thing that I really believe in
and you know, this is not my idea
and it's been discussed several times,
but this kind of like something like a startup
or a kind of an innovative company,
including definitely Waymo, Tesla,
maybe even some of the other big companies
that are kind of trying things.
This kind of like iterated learning is very important.
The fact that we're over there
and we're trying things and so on,
I think that's important.
We try to understand.
And I think that, you know,
the code in code Silicon Valley has done that
with business models pretty well.
And now I think we're trying to get to do it
where there's a literal technology gap.
I mean, before, like, you know, you're trying to build,
I'm not trying to, you know,
I think these companies are building great technology
to, for example, enable internet search,
to do it so quickly.
And that kind of didn't, wasn't there so much,
but at least like it was a kind of a technology
that you could predict to some degree and so on.
And now we're just kind of trying to build,
you know, things that it's kind of hard to quantify
what kind of a metric are we looking at.
- So psychologically as a sort of,
as a leader of graduate students and at Optimus Ride,
a bunch of brilliant engineers, just curiosity,
psychologically, do you think it's good to think that,
you know, whatever technology gap we're talking about
can be closed by the end of the year?
Or do you, you know, 'cause we don't know.
So the way, do you want to say
that everything is going to improve exponentially
to yourself and to others around you as a leader?
Or do you want to be more sort of maybe not cynical,
but I don't want to use realistic
'cause it's hard to predict, but yeah,
maybe more cynical, pessimistic
about the ability to close that gap?
- Yeah, I think that, you know, going back,
I think that iterated learning is like key,
that, you know, you're out there,
you're running experiments to learn.
And that doesn't mean sort of like, you know,
like you're Optimus Ride, you're kind of doing something,
but like in an environment,
but like what Tesla is doing,
I think is also kind of like this kind of notion.
And, you know, people can go around and say like,
you know, this year, next year, the other year and so on.
But I think that the nice thing about it
is that they're out there,
they're pushing this technology in.
I think what they should do more of,
I think that kind of inform the people
about what kind of technology that they're providing,
you know, the good and the bad,
and not just sort of, you know, if it works very well.
But I think, and I'm not saying
they're not doing bad on informing.
I think they're kind of trying,
they put up certain things,
or at the very least, YouTube videos comes out
on how the summon function works every now and then,
and, you know, people get informed.
And so that kind of cycle continues,
but, you know, I admire it.
I think they're kind of go out there
and they do great things.
They do their own kind of experiment.
I think we do our own.
And I think we're closing some similar technology gaps,
but some also, some are orthogonal as well.
You know, I think like we talked about, you know,
people being remote, like it's something,
or in the kind of environments that we're in,
or, you know, think about a Tesla car,
maybe you can enable it one day,
like there's, you know, low traffic,
like you're kind of, the stop and go motion,
you just hit the button, and you can really,
or maybe there's another, you know, lane
that you can pass into, you go in that.
I think they can enable these kinds of,
I believe it.
And so I think that that part,
that is really important, and that is really key.
And beyond that, I think, you know,
when is it exactly gonna happen and so on?
I mean, it's, like I said, it's very hard to predict.
And I would imagine that it would be good to do
some sort of like a one or two year plan,
when it's a little bit more predictable,
that, you know, the technology gaps you close,
and the kind of sort of product that would ensue.
So I know that from Optimus Ride,
or, you know, other companies that I get involved in,
I mean, at some point, you find yourself in a situation
where you're trying to build a product,
and people are investing in that, you know, building effort.
And those investors, they do wanna know,
as they compare the investments they wanna make,
they do wanna know what happens
in the next one or two years.
And I think that's good to communicate that.
But I think beyond that, it becomes a vision
that we wanna get to someday,
and saying five years, 10 years,
I don't think it means anything.
- But iterative learning is key, though,
to do and learn.
- I think that is key.
- You know, I gotta sort of throw back right at you,
criticism, in terms of, you know, like Tesla,
or somebody communicating, you know,
how someone works and so on.
I got a chance to visit Optimus Ride,
and you guys are doing some awesome stuff,
and yet the internet doesn't know about it.
So you should also communicate more,
showing off, you know, showing off some of the awesome stuff,
the stuff that works and stuff that doesn't work.
I mean, it's just, the stuff I saw
with the tracking of different objects and pedestrians,
so I mean, incredible stuff going on there.
It's just, maybe it's just the nerd in me,
but I think the world would love to see that kind of stuff.
- Yeah, that's well taken.
I think, you know, I should say that it's not like,
you know, we weren't able to,
I think we made a decision at some point.
That decision did involve me quite a bit
on kind of sort of doing this
in kind of coding code stealth mode for a bit.
But I think that, you know,
we'll open it up quite a lot more.
And I think that we are also at Optimus Ride
kind of hitting a new era.
§08Transcript · 58:02–1:06:38
You know, we're big now,
we're doing a lot of interesting things.
And I think, you know, some of the deployments
that we kind of announced were some of the first bits
of information that we kind of put out into the world.
We'll also put out our technology.
A lot of the things that we've been developing
is really amazing.
And then, you know, we're gonna start putting that out.
We're especially interested in sort of like
being able to work with the best people.
And I think it's good to not just kind of show them
when they come to our office for an interview,
but just put it out there in terms of like, you know,
get people excited about what we're doing.
- So on the autonomous vehicle space,
let me ask one last question.
So Elon Musk famously said that LIDAR is a crutch.
So I've talked to a bunch of people about it,
gotta ask you.
You use that crutch quite a bit in the DARPA days.
So, you know, and his idea in general,
sort of, you know, more provocative and fun, I think,
than a technical discussion.
But the idea is that camera-based,
primarily camera-based systems is going to be
what defines the future of autonomous vehicles.
So what do you think of this idea?
LIDAR is a crutch versus primarily camera-based systems?
- First things first.
I think, you know, I'm a big believer
in just camera-based autonomous vehicle systems.
Like I think that, you know, you can put in
a lot of autonomy and you can do great things.
And it's very possible that at the timescales,
like we said, we can't predict 20 years from now,
like you may be able to do things that we're doing today
only with LIDAR and then you may be able
to do them just with cameras.
And I think that, you know, you can just,
I think that I will put my name on it too.
Like, you know, there will be a time
when you can only use cameras and you'll be fine.
At that time though, it's very possible that, you know,
you find the LIDAR system as another robustifier
or it's so affordable that it's stupid not to, you know,
just kind of put it there.
And I think we may be looking at a future like that.
- Do you think we're over-relying on LIDAR right now
because we understand the better, it's more reliable
in many ways in terms from a safety perspective?
- It's easier to build with, that's the other thing.
I think to be very frank with you,
I mean, you know, we've seen a lot of sort of
autonomous vehicles companies come and go
and the approach has been, you know,
you slap a LIDAR on a car and it's kind of easy to build
with and you have a LIDAR, you know,
just kind of coat it up and you hit the button
and you do a demo.
So I think there's admittedly, there's a lot of people
they focus on the LIDAR 'cause it's easier to build with.
That doesn't mean that, you know, without the camera,
just cameras, you cannot do what they're doing,
but it's just kind of a lot harder.
And so you need to have certain kind of expertise
to exploit that.
What we believe in and you know,
you've maybe seen some of it is that
we believe in computer vision.
We certainly work on computer vision and OptumSprite
by a lot, like, and we've been doing that from day one.
And we also believe in sensor fusion.
So, you know, we have a relatively minimal use of LIDARs,
but we do use them.
And I think, you know, in the future,
I really believe that the following sequence
of events may happen.
First things first, number one,
there may be a future in which, you know,
there's like cars with LIDARs and everything
and the cameras, but you know,
this in this 50 year ahead future,
they can just drive with cameras as well,
especially in some isolated environments and cameras,
they go and they do the thing.
In the same future, it's very possible that, you know,
the LIDARs are so cheap and frankly make the software
maybe a little less compute intensive at the very least,
or maybe less complicated so that they can be certified
or ensure their safety and things like that,
that it's kind of stupid not to put the LIDAR.
Like, imagine this, you either pay money for the LIDAR
or you pay money for the compute.
And if you don't put the LIDAR,
it's a more expensive system
because you have to put in a lot of compute.
Like this is another possibility.
I do think that a lot of the sort of initial deployments
of self-driving vehicles, I think they will enroll LIDARs
and especially either low range or short,
either short range or low resolution LIDARs
are actually not that hard to build in solid state.
They're still scanning,
but like MEMS type of scanning LIDARs and things like that,
they're like, they're actually not that hard.
I think they will, maybe kind of playing with the spectrum
and the phase arrays, they're a little bit harder,
but I think like putting a MEMS mirror in there
that kind of scans the environment, it's not hard.
The only thing is that, you know, you,
just like with a lot of the things that we do nowadays
in developing technology,
you hit fundamental limits of the universe.
The speed of light becomes a problem
in when you're trying to scan the environment.
So you don't get either good resolution
or you don't get range,
but you know, it's still,
it's something that you can put in there affordably.
- So let me jump back to drones.
You have a role in the Lockheed Martin
Alpha Pilot Innovation Challenge,
where teams compete in drone racing.
It's super cool, super intense,
interesting application of AI.
So can you tell me about the very basics of the challenge
and where you fit in,
what your thoughts are on this problem?
And it's sort of echoes of the early DARPA challenge
in the, through the desert that we're seeing now,
now with drone racing.
- Yeah, I mean, one interesting thing about it is that,
you know, people, drone racing exists as an e-sport.
And so it's much like you're playing a game,
but there's a real drone going in an environment.
- A human being is controlling it with goggles on.
So there's no, it is a robot, but there's no AI.
- There's no AI, yeah.
Human being is controlling it.
And so that's already there.
And I've been interested in this problem
for quite a while, actually,
from a roboticist point of view.
And that's what's happening in Alphapilot.
- Which problem, of aggressive flight?
- Of aggressive flight, fully autonomous, aggressive flight.
The problem that I'm interested in,
I mean, you asked about Alphapilot,
and I'll get there in a second,
but the problem that I'm interested in,
I'd love to build autonomous vehicles like drones
that can go far faster than any human possibly can.
I think we should recognize that we as humans have,
you know, limitations in how fast
we can process information.
And those are some biological limitations.
Like we think about this AI this way too.
I mean, this has been discussed a lot,
and this is not sort of my idea per se,
but a lot of people kind of think about human level AI.
And they think that, you know, AI is not human level.
One day it'll be human level,
and humans and AIs, they kind of interact.
Versus I think that the situation really is that
humans are at a certain place,
and AI keeps improving,
and at some point just crosses off,
and you know, it gets smarter and smarter and smarter.
And so drone racing, the same issue.
Humans play this game,
and you know, you have to like react in milliseconds.
And there's really, you know,
you see something with your eyes,
and then that information just flows through your brain,
into your hands so that you can command it.
And there's some also delays on, you know,
getting information back and forth.
But suppose those delays didn't exist.
You just, just a delay between your eye and your fingers
is a delay that a robot doesn't have to have.
So we end up building in my research group,
like systems that, you know,
see things at a kilohertz,
like a human eye would barely hit a hundred hertz.
So imagine things that see stuff in slow motion,
like 10X slow motion.
It will be very useful.
Like we talked a lot about autonomous cars,
so, you know, we don't get to see it,
but a hundred lives are lost every day,
just in the United States on traffic accidents.
And many of them are like known cases, you know,
like the, you're coming through like a ramp,
going into a highway, you hit somebody and you're off,
or, you know, like you kind of get confused.
You try to like swerve into the next lane,
you go off the road and you crash, whatever.
And I think if you had enough compute in a car
and a very fast camera, right at the time of an accident,
§09Transcript · 1:06:43–1:14:13
you could use all compute you have,
like you could shut down the infotainment system
and use that kind of computing resources,
instead of rendering,
you use it for the kind of artificial intelligence
that goes in there, the autonomy.
And you can either take control of the car
and bring it to a full stop,
but even if you can't do that,
you can deliver what the human is trying to do.
Human is trying to change the lane,
but goes off the road,
not being able to do that with motor skills and the eyes,
and you know, you can get in there.
And I was, there's so many other things
that you can enable
with what I would call high throughput computing.
You know, data is coming in extremely fast,
and in real time, you have to process it.
And the current CPUs,
however fast you clock it, are typically not enough.
You need to build those computers from the ground up
so that they can ingest all that data.
That I'm really interested in.
- Just on that point, just really quick,
is the, currently what's the bottom?
Like you mentioned the delays in humans.
Is it the hardware?
So you work a lot with Nvidia hardware.
Is it the hardware or is it the software?
- I think it's both.
I think it's both.
In fact, they need to be co-developed,
I think, in the future.
I mean, that's a little bit what Nvidia does.
Sort of like they almost like build the hardware,
and then they build the neural networks,
and then they build the hardware back,
and the neural networks back,
and it goes back and forth, but it's that co-design.
And I think that, you know, like,
we tried to, way back,
we tried to build a fast drone
that could use a camera image to like track what's moving
in order to find where it is in the world.
This typical sort of, you know,
visual inertial state estimation problems
that we would solve.
And, you know, we just kind of realized
that we're at the limit sometimes of, you know,
doing simple tasks.
We're at the limit of the camera frame rate.
Because, you know, if you really want to track things,
you want the camera image to be 90% kind of like,
or somewhat the same from one frame to the next.
- That's right.
- And why are we at the limit of the camera frame rate?
It's because camera captures data.
It puts it into some serial connection.
It could be USB,
or like there's something called camera serial interface
that we use a lot.
It puts into some serial connection,
and copper wires can only transmit so much data.
And you hit the Shannon limit on copper wires.
And, you know, you hit yet another kind of universal limit
that you can transfer the data.
So you have to be much more intelligent
on how you capture those pixels.
You can take compute and put it right next to the pixels.
People are building those-
- How hard is it to do?
How hard is it to get past the bottleneck
of the copper wire?
- Yeah, you need to do a lot of parallel processing,
as you can imagine.
The same thing happens in the GPUs.
You know, like the data is transferred in parallel somehow.
It gets into some parallel processing.
I think that, you know, like,
now we're really kind of diverted off
into so many different dimensions, but-
- Great, so it's aggressive flight.
How do we make drones see many more frames a second,
you know, to enable aggressive flight?
That's a super interesting problem.
- That's an interesting problem.
So, but like, think about it.
You have CPUs.
You clock them at, you know, several gigahertz.
We don't clock them faster,
largely because, you know,
we run into some heating issues and things like that.
But another thing is that three gigahertz clock,
light travels kind of like on the order of a few inches
or an inch.
That's the size of a chip.
And so you pass a clock cycle,
and as the clock signal is going around in the chip,
you pass another one.
And so trying to coordinate that,
the design of the complexity of the chip becomes so hard.
I mean, we have hit the fundamental limits of the universe
in so many things that we're designing.
I don't know if people realize that.
It's great, but like, we can't make transistors smaller
because like quantum effects,
electrons start to tunnel around.
We can't clock it faster.
One of the reasons why is because like,
information doesn't travel faster in the universe.
And we're limited by that.
Same thing with the laser scanner.
But so then it becomes clear that, you know,
the way you organize the chip into a CPU or even a GPU,
you now need to look at how to redesign that
if you're gonna stick with silicon.
You could go do other things too.
I mean, there's that too,
but you really almost need to take those transistors,
put them in a different way
so that the information travels on those transistors
in a different way,
in a much more way that is specific
to the high speed cameras coming in.
And so that's one of the things
that we talk about quite a bit.
- So drone racing kind of really makes that-
- Embodies that.
- It embodies that.
And that's why it's really exciting.
- And it's exciting.
It's exciting for people, you know, students like it.
It embodies all those problems.
But going back, we're building,
code and code another engine.
And that engine, I hope one day will be
just like how impactful seatbelts were in driving.
I hope so.
Or it could enable, you know,
next generation autonomous air taxis and things like that.
I mean, it sounds crazy,
but one day we may need to purge these things.
If you really wanna go from Boston to New York
in one and a half hours,
you may wanna fix big aircraft.
Most of these companies that are kind of doing
code and code flying cars, they're focusing on that.
But then how do you land it on top of a building?
You may need to pull off like kind of
fast maneuvers for a robot,
like perch land, it's just gonna go perch into a building.
If you wanna do that, like you need these kinds of systems.
And so drone racing, you know,
it's being able to go way faster
than any human can comprehend.
Take an aircraft, forget the quadcopter,
you take a fixed wing.
While you're at it,
you might as well put some like rocket engines in the back
and just light it.
You go through the gate and a human looks at it
and just said, "What just happened?"
And they would say, "It's impossible for me to do that."
And that's closing the same technology gap
that would, you know, one day steer cars out of accidents.
- So, but then let's get back to the practical,
which is sort of just getting the thing
to work in a race environment,
which is kind of what the,
it's another kind of exciting thing,
which the DARPA Challenge for the desert did.
You know, theoretically we had autonomous vehicles,
but making them successfully finish a race,
first of all, which nobody finished the first year.
And then the second year just to get, you know,
to finish and go at a reasonable time
is really difficult engineering,
practically speaking challenge.
So that, let me ask about the Alpha Pilot Challenge.
There's a, I guess, a big prize
potentially associated with it.
But let me ask, reminiscent of the DARPA days,
predictions, you think anybody will finish?
(laughing)
- Well, not soon.
I think that depends on how you set up the race course.
And so if the race course is a slalom course,
I think people will kind of do it.
But can you set up some course, like literally some course,
you get to design it, as the algorithm developer,
can you set up some course
so that you can beat the best human?
When is that gonna happen?
Like, that's not very easy.
Even just setting up some course.
If you let the human that you're competing with
set up the course, it becomes a lot harder.
- So how many in the space of all possible courses
are, would humans win and would machines win?
§10Transcript · 1:14:18–1:22:44
- Great question.
Let's get to that.
I wanna answer your other question,
which is like, the DARPA challenge days, right?
What was really hard?
I think we understand, we understood what we wanted to build
but still building things, that experimentation,
that iterated learning,
that takes up a lot of time actually.
And so in my group, for example,
in order for us to be able to develop fast,
we build like VR environments.
We'll take an aircraft,
we'll put it in a motion capture room,
big, huge motion capture room,
and we'll fly it in real time,
we'll render other images and beam it back to the drone.
That sounds kind of notionally simple,
but it's actually hard
because now you're trying to fit all that data
through the air into the drone.
And so you need to do a few crazy things to make that happen
but once you do that, then at least you can try things.
If you crash into something, you didn't actually crash.
So it's like the whole drone is in VR.
We can do augmented reality and so on.
And so I think at some point,
testing becomes very important.
One of the nice things about AlphaPilot
is that they built the drone
and they build a lot of drones and it's okay to crash.
In fact, I think maybe the viewers
may kind of like to see things that crash.
- That potentially could be the most exciting part.
- It could be the exciting part.
And I think as an engineer,
it's a very different situation to be in.
Like in academia, a lot of my colleagues
who are actually in this race
and they're really great researchers,
but I've seen them trying to do similar things
whereby they built this one drone
and somebody with like a face mask
and a glows are going right behind the drone,
trying to hold it if it falls down.
Imagine you don't have to do that.
I think that's one of the nice things
about AlphaPilot Challenge where we have these drones
and we're going to design the courses
in a way that will keep pushing people
up until the crashes start to happen.
And we'll hopefully sort of,
I don't think you want to tell people crashing is okay.
Like we want to be careful here,
but because we don't want people to crash a lot,
but certainly we want them to push it
so that everybody crashes once or twice
and they're really pushing it to their limits.
- That's where iterated learning comes in.
'Cause every crash is a lesson.
- Is a lesson, exactly.
- So in terms of the space of possible courses,
how do you think about it?
In the war of human versus machines,
where do machines win?
- We look at that quite a bit.
I mean, I think that you will see quickly
that you can design a course
and in certain courses, like in the middle somewhere,
if you kind of run through the course once,
the machine gets beaten pretty much consistently by slightly.
But if you go through the course like 10 times,
humans get beaten very slightly, but consistently.
So humans, at some point, you get confused,
you get tired and things like that versus this machine
is just executing the same line of code,
tirelessly just going back to the beginning
and doing the same thing exactly.
I think that kind of thing happens.
And I realized sort of as humans,
there's the classical things that everybody has realized.
If you put in some sort of like strategic thinking,
that's a little bit harder for machines
that I think sort of comprehend.
Precision is easy to do, so that's what they excel in.
And also sort of repeatability is easy to do,
that's what they excel in.
You can build machines that excel in strategy as well
and beat humans that way too,
but that's a lot harder to build.
- I have a million more questions,
but in the interest of time, last question.
- Yeah.
- What is the most beautiful idea
you've come across in robotics?
Whether a simple equation, experiment,
a demo, simulation, piece of software,
what just gives you pause?
- That's an interesting question.
I have done a lot of work myself in decision-making,
so I've been interested in that area.
So in robotics, you have,
somehow the field has split into,
like there's people who would work on like perception,
how robots perceive the environment,
then how do you actually make like decisions?
And there's people also like how do you interact,
people interact with robots.
There's a whole bunch of different fields.
And I have admittedly worked a lot on the more control
and decision-making than the others.
And I think that the one equation
that has always kind of baffled me is Bellman's equation.
And so it's this person who have realized like way back,
more than half a century ago,
on like how do you actually sit down
and if you have several variables
that you're kind of jointly trying to determine,
how do you determine that?
And there's one beautiful equation that,
like today people do reinforcement learning,
we still use it.
And it's baffling to me because it both kind of
tells you the simplicity,
'cause it's a single equation that anyone can write down.
You can teach it in the first course on decision-making.
At the same time, it tells you how computation,
we have hard the problem is.
I feel like a lot of the things that I've done at MIT
for research has been kind of just this fight
against computational efficiency things.
Like how can we get it faster to the point
where we now got to like, let's just redesign this chip.
Like maybe that's the way.
But I think it talks about how computationally hard
certain problems can be by nowadays
what people call curse of dimensionality.
And so as the number of variables kind of grow,
the number of decisions you can make grows rapidly.
Like if you have 100 variables,
each one of them take 10 values,
all possible assignments is more than the number of atoms
in the universe, it's just crazy.
And that kind of thinking is just embodied
in that one equation that I really like.
- And the beautiful balance between it being
theoretically optimal and somehow, practically speaking,
given the curse of dimensionality,
nevertheless in practice works,
despite all those challenges, which is quite incredible.
- Which is quite incredible.
So I would say that it's kind of like quite baffling,
actually, in a lot of fields that we think about
how little we know.
And so I think here too, we know that in the worst case,
things are pretty hard,
but in practice, generally things work.
So it's just kind of baffling in decision-making
how little we know.
Just like how little we know about the beginning of time,
how little we know about our own future.
Like if you actually go into like from Balman's equation
all the way down, I mean, there's also how little we know
about like mathematics.
I mean, we don't even know if the axioms are like consistent
it's just crazy.
- Yeah, I think a good lesson there
just like as you said, we tend to focus on the worst case
or the boundaries of everything we're studying.
And then the average case seems to somehow work out.
If you think about life in general,
we mess it up a bunch,
we freak out about a bunch of the traumatic stuff,
but in the end it seems to work out okay.
- Yeah, it seems like a good metaphor.
(laughs)
- So Tasha, thank you so much for being a friend,
a colleague, a mentor.
I really appreciate it.
It's an honor to talk to you.
- Likewise, thank you, Lex.
- Thanks for listening to this conversation
with Sertash Karaman
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which is all I think that any conscious entity
can ever hope to do."
Thank you for listening and hope to see you next time.
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