The following is a conversation with Jitendra Malik, a professor at Berkeley and one of the

seminal figures in the field of computer vision, the kind before the deep learning revolution

and the kind after. He has been cited over 180,000 times and has mentored many world

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And now here's my conversation with Jitendra Malik. In 1966, Seymour Papert at MIT wrote up a

proposal called the summer vision project to be given as far as we know to 10 students to work on

and solve that summer. So that proposal outlined many of the computer vision tasks we still work on

today. Why do you think we underestimate and perhaps we did underestimate and perhaps still

underestimate how hard computer vision is? Because most of what we do in vision, we do unconsciously

or subconsciously in human vision. So that effortlessness gives us the sense that, oh,

this must be very easy to implement on a computer. Now, this is why the early researchers in AI got

it so wrong. However, if you go into neuroscience or psychology of human vision, then the complexity

becomes very clear. The fact is that a very large part of the the cerebral cortex is devoted to

visual processing. I mean, and this is true in other primates as well. So once we looked at it

from a neuroscience or psychology perspective, it becomes quite clear that the problem is very

challenging and it will take some time. You said the high level parts are the harder parts?

I think vision appears to to be easy because most of what visual processing is subconscious or

unconscious. So we underestimate the difficulty. Whereas when you are like proving a mathematical

theorem or playing chess, the difficulty is much more evident. So because it is your conscious

brain, which is processing various aspects of the problem solving behavior. Whereas in vision,

all this is happening, but it's not in your awareness. It's in your it's operating below that.

But it still seems strange. Yes, that's true. But it seems strange that as computer vision

researchers, for example, the community broadly is time and time again makes the mistake of

thinking the problem is easier than it is. Or maybe it's not a mistake. We'll talk a little bit

about autonomous driving, for example, how hard of a vision task that is. Do you think, I mean,

is it just human nature or is there something fundamental to the vision problem that we

underestimate? We're still not able to be cognizant of how hard the problem is.

Yeah, I think in the early days, it could have been excused because in the early days,

all aspects of AI were regarded as too easy. But I think today it is much less excusable.

And I think why people fall for this is because of what I call the fallacy of the successful

first step. There are many problems in vision where getting 50% of the solution you can get in one

minute, getting to 90% can take you a day, getting to 99% may take you five years and

99.99% may be not in your lifetime. I wonder if that's a unique division.

It seems that language people are not so confident about, so natural language processing,

people are a little bit more cautious about our ability to solve that problem. I think for

language people intuit that we have to be able to do natural language understanding. For vision,

it seems that we're not cognizant or we don't think about how much understanding is required.

It's probably still an open problem. But in your sense, how much understanding is required to solve

vision? Put another way, how much something called common sense reasoning is required to

really be able to interpret even static scenes? Yeah, so vision operates at all levels. And there

are parts which can be solved with what we could call maybe peripheral processing. So in the human

vision literature, there used to be these terms sensation, perception, and cognition, which

roughly speaking referred to the front end of processing, middle stages of processing, and

higher level of processing. And I think they made a big deal out of this and they wanted

to study only perception and then dismiss certain problems as being, quote, cognitive.

But really, I think these are artificial divides. The problem is continuous at all levels,

and there are challenges at all levels. The techniques that we have today, they work better

at the lower and mid levels of the problem. I think the higher levels of the problem, quote,

the cognitive levels of the problem are there. And we, in many real applications, we have to

confront them. Now, how much that is necessary will depend on the application. For some problems,

it doesn't matter. For some problems, it matters a lot. So I am, for example, a pessimist on

fully autonomous driving in the near future. And the reason is because I think there will be

that 0.01% of the cases where quite sophisticated cognitive reasoning is called for. However,

there are tasks where you can, first of all, they are much more, they are robust. So in the sense

that error rates, error is not so much of a problem. For example, let's say you're doing

image search. You're trying to get images based on some description, some visual description.

We are very tolerant of errors there, right? I mean, when Google image search gives you some

images back and a few of them are wrong, it's okay. It doesn't hurt anybody. There's no,

there's not a matter of life and death. But making mistakes when you are driving at 60 miles per hour

and you could potentially kill somebody is much more important. So just for the,

for the fun of it, since you mentioned, let's go there briefly about autonomous vehicles.

So one of the companies in the space, Tesla, is with Andre Capati and Elon Musk are working on

a system called autopilot, which is primarily a vision based system with eight cameras and

basically a single neural network, a multitask neural network. They call it hydranet multiple heads.

So it does multiple tasks, but is forming the same representation at the core.

Do you think driving can be converted in this way to a purely a vision problem and then solved

with learning? Or even more specifically in the current approach, what do you think about

what Tesla autopilot team is doing? So the way I think about it is that there are certainly

subsets of the visual based driving problem, which are quite solvable. So for example,

driving in freeway conditions is quite a solvable problem. I think there were demonstrations of that

going back to the 1980s by someone called Ernst Tickman's in Munich. In the 90s, there were

approaches from Carnegie Mellon. There were approaches from our team at Berkeley. In the 2000s,

there were approaches from Stanford and so on. So autonomous driving in certain settings is

very doable. The challenge is to have an autopilot work under all kinds of driving conditions. At

that point, it's not just a question of vision or perception, but really also of control and

dealing with all the edge cases. So where do you think most of the difficult cases, to me,

even the highway driving is an open problem because it applies the same 50, 90, 95, 99 rule

or the first step, the fallacy of the first step. I forget how you put it. We fall victim too.

I think even highway driving has a lot of elements because to solve autonomous driving,

you have to completely relinquish the fat help of a human being. You're always in control. So

you're really going to feel the edge cases. So I think even highway driving is really difficult.

But in terms of the general driving task, do you think vision is the fundamental problem?

Or is it also your action, the interaction with the environment,

the ability to... And then the middle ground, I don't know if you put that under vision,

which is trying to predict the behavior of others, which is a little bit in the world of

understanding the scene, but it's also trying to form a model of the actors in the scene

and predict their behavior. Yeah, I include that in vision because to me, perception blends into

cognition and building predictive models of other agents in the world, which could be other agents,

could be people, other agents, could be other cars. That is part of the task of perception

because perception always has to not tell us what is now, but what will happen because what's

now is boring. It's done. It's over with. We care about the future because we act in the future.

And we care about the past in as much as it informs what's going to happen in the future.

So I think we have to build predictive models of behaviors of people and those can get quite

complicated. So I mean, I've seen examples of this in actually, I mean, I own a Tesla and

it has various safety features built in. And what I see are these examples where

let's say there is some skateboarder. I mean, and I don't want to be too critical because

obviously these systems are always being improved and any specific criticism I have,

maybe the system six months from now will not have that particular failure mode.

So it had the wrong response and it's because it couldn't predict what this skateboarder was going

to do. And because it really required that higher level cognitive understanding of what

skateboarders typically do as opposed to a normal pedestrian. So what might have been

the correct behavior for a pedestrian, a typical behavior for pedestrian was not the

typical behavior for a skateboarder. And so therefore to do a good job there,

you need to have enough data where you have pedestrians, you also have skateboarders,

you've seen enough skateboarders to see what kinds of patterns or behavior they have.

So it is in principle with enough data that problem could be solved. But I think our current

systems, computer vision systems, they need far, far more data than humans do for learning

those same capabilities. So say that there is going to be a system that solves autonomous

driving. Do you think it will look similar to what we have today, but have a lot more data,

perhaps more compute, but the fundamental architecture is involved? Well, in the case

of Tesla autopilot is neural networks. Do you think it will look similar? In that regard,

and we'll just have more data. That's a scientific hypothesis as to which way is it going to go. I

will tell you what I would bet on. And this is my general philosophical position on how these

learning systems have been. What we have found currently very effective in computer vision

in the deep learning paradigm is sort of tabular ASA learning and tabular ASA learning in a

supervised way with lots and lots of... What's tabular ASA learning? Tabular ASA in the sense

that blank slate. We just have the system which is given a series of experiences in this setting

and then it learns there. Now, if let's think about human driving, it is not tabular ASA learning.

So at the age of 16 in high school, a teenager goes into driver head class. And now at that point,

they learn, but at the age of 16, they are already visual geniuses because from 0 to 16,

they have built a certain repertoire of vision. In fact, most of it has probably been achieved by

age two. In this period of age up to age two, they know that the world is three dimensional.

They know how objects look like from different perspectives. They know about occlusion.

They know about common dynamics of humans and other bodies. They have some notion of intuitive

physics. So they built that up from their observations and interactions in early childhood

and of course, reinforced through their growing up to age 16. So then at age 16, when they go into

driver head, what are they learning? They're not learning afresh the visual world. They have a mastery

of the visual world. What they are learning is control. They are learning how to be smooth

about control, about steering and brakes and so forth. They're learning a sense of typical

traffic situations. Now, that education process can be quite short because they are coming in as

visual geniuses. And of course, in their future, they're going to encounter situations which are

very novel. So during my driver head class, I may not have had to deal with a skateboarder.

I may not have had to deal with a truck driving in front of me where the back opens up and some

junk gets dropped from the truck and I have to deal with it. But I can deal with this as a driver,

even though I did not encounter this in my driver head class. And the reason I can deal with it is

because I have all this general visual knowledge and expertise. And do you think the learning

mechanisms we have today can do that kind of long term accumulation of knowledge? Or do we have to

do some kind of... The work that led up to expert systems with knowledge representation,

the broader field of artificial intelligence worked on this kind of accumulation of knowledge.

Do you think neural networks can do the same? I think I don't see any in principle problem with

neural networks doing it. But I think the learning techniques would need to evolve significantly.

So the current learning techniques that we have are supervised learning. You're giving lots of

examples, X, Y, Y pairs, and you learn the functional mapping between them. I think that

human learning is far richer than that. It includes many different components. There is

a child who explores the world and sees... For example, a child takes an object and manipulates it

in his or her hand and therefore gets to see the object from different points of view. And the child

has commanded the movement. So that's a kind of learning data. But the learning data has been

arranged by the child. And this is a very rich kind of data. The child can do various experiments

with the world. So there are many aspects of human learning. And these have been studied in

child development by psychologists. And what they tell us is that supervised learning is a very

small part of it. There are many different aspects of learning. And what we would need to do is to

develop models of all of these and then train our systems with that kind of protocol.

So new methods of learning, some of which might imitate the human brain. But you also,

in your talks, I've mentioned sort of the compute side of things. In terms of the

difference in the human brain or referencing Hans Maravak. So do you think there's something

interesting, valuable to consider about the difference in the computational power of the human

brain versus the computers of today in terms of instructions per second? Yes. So if we go back...

So this is a point I've been making for 20 years now. And I think once upon a time, the way I used

to argue this was that we just didn't have the computing power of the human brain. Our computers

were not quite there. And I mean, there is a well known tradeoff, which we know that

the neurons are slow compared to transistors. But we have a lot of them and they have a very high

connectivity. Whereas in silicon, you have much faster devices, transistors switch at...

On the order of nanoseconds, but the connectivity is usually smaller. At this point in time,

I mean, we are now talking about 2020, we do have, if you consider the latest GPUs and so on,

amazing computing power. And if we look back at Hans Maravak's type of calculations, which he

did in the 1990s, we may be there today in terms of computing power comparable to the brain. But

it's not in the same style. It's of a very different style. So I mean, for example, the style of

computing that we have in our GPUs is far, far more power hungry than the style of computing that

is there in the human brain or other biological entities. Yeah. And that the efficiency part

is we're going to have to solve that in order to build actual real world systems of large scale.

Let me ask sort of the high level question. Taking a step back, how would you articulate

the general problem of computer vision? Does such a thing exist? So if you look at the computer vision

conferences and the work that's been going on, it's often separated into different little segments,

breaking the problem of vision apart into whether segmentation, 3D reconstruction,

object detection, I don't know, image capturing, whatever, there's benchmarks for each. But if

you were to sort of philosophically say, what is the big problem of computer vision? Does such a

thing exist? Yes, but it's not in isolation. So for all intelligence tasks, I always go back to

sort of biology or humans. And if you think about vision or perception in that setting,

we realize that perception is always to guide action. Perception for a biological system

does not give any benefits unless it is coupled with action. So we can go back and think about

the first multicellular animals which arose in the Cambrian era 500 million years ago.

And these animals could move and they could see in some way. And the two activities helped each

other because how does movement help? Movement helps that because you can get food in different

places. But you need to know where to go. And that's really about perception or seeing. I mean,

vision is perhaps the single most perception sense. But all the others are equally are also

important. So perception and action kind of go together. So earlier it was in these very

simple feedback loops which were about finding food or avoiding becoming food if there's a

predator running, trying to eat you up and so forth. So we must at the fundamental level

connect perception to action. Then as we evolved, perception became more and more sophisticated

because it served many more purposes. And so today we have what seems like a fairly general

purpose capability which can look at the external world and build a model of the external world

inside the head. We do have that capability. That model is not perfect. And psychologists

have great fun in pointing out the ways in which the model in your head is not a perfect model

of the external world. They create various illusions to show the ways in which it is

imperfect. But it's amazing how far it has come from a very simple perception action loop that

you exist in, you know, an animal 500 million years ago. Once we have these very sophisticated

visual systems, we can then impose a structure on them. It's we as scientists who are imposing

that structure where we have chosen to characterize this part of the system as this

quote module of object detection or quote this module of 3D reconstruction. What's going on

is really all of these processes are running simultaneously and they are running simultaneously

because originally their purpose was in fact to help guide action. So as a guiding general

statement of a problem, do you think we can say that the general problem of computer vision,

you said in humans, it was tied to action. Do you think we should also say that ultimately

that the goal, the problem of computer vision is to sense the world in the way that helps you

act in the world? Yes, I think that's the most fundamental, that's the most fundamental purpose.

We have by now hyper evolved. So we have this visual system which can be used for other things,

for example, judging the aesthetic value of a painting. And this is not guiding action,

maybe it's guiding action in terms of how much money you will put in your auction bid, but

that's a bit stretched. But the basics are in fact in terms of action, but we are not

talking about action, but we have evolved really this hyper, we have hyper evolved our visual

system. Actually, just to, sorry to interrupt, but perhaps it is fundamentally about action.

You're kind of jokingly said about spending, but perhaps the capitalistic drive that drives

a lot of the development in this world is about the exchange of money and the fundamental action

is money. If you watch Netflix, if you enjoy watching movies, you're using your perception

system to interpret the movie. Ultimately, your enjoyment of that movie means you'll

subscribe to Netflix. So the action is this extra layer that we've developed in modern society,

perhaps is fundamentally tied to the action of spending money. Well, certainly with respect to

interactions with firms. So in this homo economic role, when you're interacting with firms,

it does become that. That's it. What else is there?

And that was a rhetorical question. Okay. So to link on the division between the static and the

dynamic, so much of the work in computer vision, so many of the breakthroughs that you've been a

part of have been in the static world in looking at static images. And then you've also worked on

starting, but it's a much smaller degree. The community is looking at dynamic at video

at dynamic scenes. And then there is robotic vision, which is dynamic, but also where you're

actually have a robot in the physical world interacting based on that vision.

Which problem is harder? The sort of the trivial first answers of, well, of course,

one image is harder. But if you look at a deeper question there, are we, what's the term, cutting

ourselves at the knees or like making the problem harder by focusing on the images?

That's a fair question. I think sometimes we, we can simplify a problem so much

that we essentially lose part of the juice that could enable us to solve the problem.

And one could reasonably argue that to some extent this happens when we go from video to

single images. Now, historically, you have to consider the limits of imposed by the

computation capabilities we had. So if we, many of the choices made in the computer vision community

through the 70s, 80s, 90s can be understood as choices which were forced upon us by the

fact that we just didn't have access to compute enough compute.

Not enough memory, not enough hard drive.

Exactly. Not enough, not enough compute, not enough storage. So, so think of these choices.

So one of the choices is focusing on single images rather than video. Okay,

clear questions, storage and compute. We had to focus on, we did, we used to detect edges and

throw away the image, right? So you have an image which I say 256 by 256 pixels.

And instead of keeping around the grayscale value, what we did was we detected edges,

find the places where the brightness changes a lot. So now that's, and now, and then throw away

the rest. So this was a major compression device. And the hope was that this makes it,

that you can still work with it. And the logic was humans can interpret a line drawing.

And, and yes, and this will save us a computation. So many of the choices were dictated by that.

I think today we are no longer detecting edges, right? We process images with conlets

because we don't need to, we don't have that those compute restrictions anymore.

Now video is still understudied because video compute is still quite challenging

if you are a university researcher. I think video computing is not so challenging if you are at

Google or Facebook or Amazon. Still super challenging. I just spoke with the

VP of engineer and Google head of the YouTube search and discovery, and they still struggle

doing stuff on video. It's very difficult except doing, except using techniques that are essentially

the techniques used in the 90s, some very basic computer vision techniques.

No, that's when you want to do things at scale. So if you want to operate at the scale of all the

content of YouTube, it's very challenging. And there are similar issues in Facebook.

But as a researcher, you, you have, you have more, you know, opportunities.

You can train large networks with relatively large video data sets. Yeah.

Yes. So I think that this is part of the reason why we have so emphasized static images.

I think that this is changing. And over the next few years, I see a lot more progress happening

in video. So I, I have this generic statement that to me, video recognition feels like 10 years

behind object recognition. And you can quantify that because you can take some of the challenging

video data sets and their performance on action classification is like say 30%, which is kind

of what we used to have around 2009 in object detection, you know, it's like about 10 years

behind. And whether it'll take 10 years to catch up is a different question. Hopefully,

it will take less than that. Let me ask a similar question I've already asked. But once again,

so for dynamic scenes, do you think, do you think some kind of injection of knowledge

basis and reasoning is required to help improve like action recognition? Like if, if, if, if we

solve the general action recognition problem, what do you think the solution would look like?

There's another way. Yeah. So I, I completely agree that knowledge is called for. And that

knowledge can be quite sophisticated. So the way I would say it is, is that you have to

be quite sophisticated. So the way I would say it is that perception blends into cognition.

And cognition brings in issues of memory and this notion of a schema from psychology, which is,

let me use the classic example, which is you go to a restaurant, right? Now there are things

happen in a certain order, you walk in, somebody takes you to a table, waiter comes,

gives you a menu, takes the order, food arrives, eventually bill arrives, etc., etc.

This is a classic example of AI from the 1970s. It was called, there was the term frames and

scripts and schemas. These are all quite similar ideas. Okay. And then in the 70s, the way the AI

of the time dealt with it was by hand coding this. So they hand coded in this notion of a script and

the various stages and the actors and so on and so forth and use that to interpret, for example,

language. I mean, if there's a description of a, of a story involving some people eating at a

restaurant, there are all these inferences you can make because you know what happens typically

at a restaurant. So I think this kind of, this kind of knowledge is absolutely essential.

So I think that when we are going to do long form video understanding,

we are going to need to do this. I think the kinds of technology that we have right now with

3D convolutions over a couple of second of clip or video, it's very much tailored towards short

term video understanding, not that long term understanding, long term understanding requires

a notion of this notion of schemas that I talked about, perhaps some notions of goals,

intentionality, functionality and so on and so forth. Now, how will we bring that in? So we

could either revert back to the 70s and say, okay, I'm going to hand code in a script or

we might try to learn it. So I tend to believe that we have to find learning ways of doing this

because I think learning ways land up being more robust. And there must be a learning version of

the story because children acquire a lot of this knowledge by sort of just observation. So at no

moment in a child's life, it's possible, but I think it's not so typical that somebody that a

mother coaches a child through all the stages of what happens in a restaurant. They just go as a

family, they go to the restaurant, they eat, come back and the child goes through 10 such

experiences and the child has got a schema of what happens when you go to a restaurant.

So we somehow need to, we need to provide that capability to our systems.

You mentioned the following line from the end of the Alan Turing paper,

Computing Machinery and Intelligence that many people, like you said, many people know and

very few have read where he proposes the Turing test. This is how you know because it's towards

the end of the paper. Instead of trying to produce a program to simulate the adult mind,

why not rather try to produce one which simulates the child's? So that's a really interesting point.

If I think about the benchmarks we have before us, the tests of our computer vision systems,

they're often kind of trying to get to the adult. So what kind of benchmarks should we have?

What kind of tests for computer vision do you think we should have that mimic the child's

in computer vision? Yeah, I think we should have those and we don't have those today.

And I think the part of the challenge is that we should really be collecting data

of the type that a child experiences. So that gets into issues of privacy and so on and so

forth. But there are attempts in this direction to sort of try to collect the kind of data that

a child encounters growing up. So what's the child's linguistic environment? What's the child's

visual environment? So if we could collect that kind of data and then develop learning schemes

based on that data, that would be one way to do it. I think that's a very promising direction

myself. There might be people who would argue that we could just short circuit this in some way

and sometimes we have imitated, we have had success by not imitating nature in detail.

So the usual example is airplanes. We don't build flapping wings. So yes, that's one of the points

of debate. In my mind, I would bet on this learning like a child approach.

So one of the fundamental aspects of learning like a child is the interactivity. So the child

gets to play with the data set it's learning from. Yes. It's against the select. I mean,

you can call that active learning in the machine learning world. You can call it a lot of terms.

What are your thoughts about this whole space of being able to play with the data set or select

what you're learning? Yeah. So I think that I believe in that and I think that we could achieve

it in two ways and I think we should use both. So one is actually real robotics. So real physical

embodiments of agents who are interacting with the world and they have a physical body with

a dynamics and mass and moment of inertia and friction and all the rest and you learn your

body, the robot learns its body by doing a series of actions. The second is that simulation

environments. So I think simulation environments are getting much, much better. In my life,

in Facebook, AI research, our group has worked on something called Habitat, which is a simulation

environment, which is a visually photo realistic environment of places like houses or interiors

of various urban spaces and so forth. And as you move, you get a picture, which is a pretty

accurate picture. So now you can imagine that subsequent generations of these simulators

will be accurate, not just visually, but with respect to forces and masses and haptic interactions

and so on. And then we have that environment to play with. I think that, let me state one reason

why I think this being able to act in the world is important. I think that this is one way to break

the correlation versus causation barrier. So this is something which is of a great

deal of interest these days. People like Judea Pearl have talked a lot about that we are

neglecting causality and he describes the entire set of successes of deep learning as just curve

fitting. But I don't quite agree. He's a troublemaker, he is. But causality is important. But

causality is not like a single silver bullet. It's not like one single principle. There are many

different aspects here. And one of the ways in which one of our most reliable ways of establishing

causal links and this is the way, for example, the medical community does this is randomized

control trials. So you pick some situation and now in some situation you perform an action

and for certain others you don't. So you have a controlled experiment. Well, the child is in fact

performing controlled experiments all the time. In a small scale. But that is a way

that the child gets to build and refine its causal models of the world. And my colleague,

Alison Gopnik, has together with a couple of authors, co authors has this book called The

Scientist in the Crib, referring to his children. So I like the part that I like about that is

the scientist wants to do, wants to build causal models and the scientist does control

experiments. And I think the child is doing that. So to enable that, we will need to

have these, these active experiments. And I think this could be done some in the real world

and some in simulation. So you have hope for simulation. I have hope for simulation. That's

an exciting possibility if we can get to not just photo realistic, but what's that called

life realistic simulation. So you don't see any fundamental blocks to why we can't eventually

simulate the principles of what it means to exist in the world.

I don't see any fundamental problems that I mean, and look, the computer graphics community has come

a long way. So the in the early days, back going back to the 80s and 90s, they were,

they were focusing on visual realism, right? And then they could do the easy stuff, but they

couldn't do stuff like hair or fur and so on. Okay, well, they managed to do that. Then they

couldn't do physical actions, right? Like there's a bowl of glass and it falls down and it shatters,

but then they could start to do pretty realistic models of that and so on and so forth. So the

graphics people have shown that they can do this forward direction, not just for optical

interactions, but also for physical interactions. So I think, of course, some of that is very

computer intensive, but I think by and by, we will find ways of making our models ever more

realistic. You break vision apart into, in one of your presentations, early vision,

static scene understanding, dynamic scene understanding, and raise a few interesting

questions. I thought I could just throw some, some at you to see if you want to talk about them.

So early vision, so it's, what is it that you said? Sensation, perception, and cognition. So

is this a sensation? Yes. What can we learn from image statistics that we don't already know?

So at the lowest level, what, what can we make from just the, the, the, the

statistics, the basics or the, the variations in the rock pixels, the textures and so on?

Yeah. So what we seem to have learned is, is that there's a lot of redundancy in these images.

And as a result, we are able to do a lot of compression and, and this compression is very

important in biological settings, right? So you might have 10 to the eight photo receptors and

only 10 to the six fibers in the optic nerve. So you have to do this compression by a factor of

100 is to one. And, and so there are analogs of that which are happening in, in our neural

net, artificial neural network. That's the early layers. So you think there's, there's a lot of

compression that can be done in the beginning. Yeah. Just, just the statistics. Yeah.

How much, how much? Well, I saw, I mean, the, the way to think about it is just how successful is

image compression, right? And we, we, and there are, and that's been done with older technologies,

but it can be done with, there are several companies which are trying to use sort of these

more advanced neural network type techniques for compression, both for static images as well as for,

for video. One of my former students has a company which is trying to do stuff like this.

And I think, I think that they are showing quite interesting results. And I think that

that's all the success of that's really about image statistics and video statistics.

But that's still not doing compression of the kind. When I see a picture of a cat,

all I have to say is it's a cat. That's another semantic kind of compression.

Yeah. So this is, this is at the lower level, right? So we are, we are, as I said, yeah,

that's focusing on low level statistics. So to linger on that for a little bit,

you mentioned how far can bottom up image segmentation go? And in general, what you mentioned

that the central question for scene understanding is the interplay of bottom up and top down

information. Maybe this is a good time to elaborate on that. Maybe define what is,

what is bottom up, what is top down in the context of computer vision?

Right. That's, so today what we have are very interesting systems because they work completely

bottom up. How are they? What does bottom up mean? Sorry. So bottom up means, in this case,

means a feed forward neural network. So starting from the raw pixels.

Yeah. They start from the raw pixels and they, they end up with some, something like cat or

not a cat, right? So our, our systems are running totally feed forward. They're trained in a very

top down way. So they're trained by saying, okay, this is a cat. There's a cat. There's a dog.

There's a zebra, et cetera. And I'm not happy with either of these choices fully. We have gone into,

because we have completely separated these processes, right? So there's a, so I would

like the, the process, the, the, so what do we know compared to biology? So in biology, what we

know is that the processes in at test time at runtime, those processes are not purely feed

forward, but they involve feedback. So, and they involve much shallower neural networks.

So the kinds of neural networks we are using in computer vision, say a ResNet 50 has 50 layers.

Well, in, in the brain, in the visual cortex, going from the retina to IT, maybe we have like seven,

right? So they're far shallower, but we have the possibility of feedback. So there are backward

connections. And this might enable us to, to deal with the more ambiguous stimuli, for example.

So the, the biological solution seems to involve feedback. The solution in, in artificial vision

seems to be just feed forward, but with a much deeper network. And the two are functionally

equivalent, because if you have a feedback network, which just has like three rounds of feedback,

you can just unroll it and make it three times the depth and create it in a totally feed forward

way. So this is something which, I mean, we have written some papers on this theme, but I really

feel that this should, this theme should be pursued further.

Oh, some kind of recurrence mechanism.

Yeah. Okay. The other, so that's, so I, so I want to have a little bit more top down in the

at test time. Okay. Then at training time, we make use of a lot of top down knowledge right now.

So basically to learn to segment an object, we have to have all these examples of this is the

boundary of a cat, and this is the boundary of a chair, and this is the boundary of a horse,

and so on. And this is too much top down knowledge. How do humans do this? We manage to,

we manage with far less supervision, and we do it in a sort of bottom up way, because for example,

we're looking at a video stream and the horse moves, and that enables me to say that all these

pixels are together. So the Gestalt psychologists used to call this the principle of common fate.

So there was a bottom up process by which we were able to segment out these objects,

and we have totally focused on this top down training signal. So in my view, we have currently

solved it in machine vision, this top down bottom up interaction, but I don't find the

solution fully satisfactory. And I would rather have a bit of both in at both stages.

For all computer vision problems, not just segmentation.

And the question that you can ask is, so for me, I'm inspired a lot by human vision,

and I care about that. You could be just a hard boiled engineer, not give a damn.

So to you, I would then argue that you would need far less training data if you could make my

research and, you know, fruitful.

Okay, so then maybe taking a step into segmentation, static scene understanding,

what is the interaction between segmentation and recognition? You mentioned the movement of objects.

So for people who don't know computer vision, segmentation is this weird activity that computer

vision folks have all agreed is very important of drawing outlines around objects versus

a bounding box, and then classifying that object. What's the value of segmentation? What is it

as a problem in computer vision? How is it fundamentally different from

detection recognition and the other problems? Yeah, so I think, so segmentation enables us to say

that some set of pixels are an object without necessarily even being able to name that object

or knowing properties of that object. Oh, so you mean segmentation purely as the act of separating

an object from its background, a blob of that's united in some way from its background. Yeah,

so entityfication, if you will, making an entity out of it. Entityfication, beautifully. So I think

that we have that capability, and that enables us to, as we are growing up, to acquire names of

objects with very little supervision. So suppose the child, let's posit that the child has this

ability to separate out objects in the world. Then when the mother says, pick up your bottle or

the cat's behaving funny today, the word cat suggests some object, and then the child sort

of does the mapping. The mother doesn't have to teach specific object labels by pointing to them.

Weak supervision works in the context that you have the ability to create objects. So I think

that, so to me, that's a very fundamental capability. There are applications where this is very

important. For example, medical diagnosis. So in medical diagnosis, you have some brain scan,

I mean, this is some work that we did in my group where you have CT scans of people who have had

traumatic brain injury, and what the radiologist needs to do is to precisely delineate various

places where there might be bleeds, for example. And there are clear needs like that. So there's

certainly very practical applications of computer vision where segmentation is necessary. But

philosophically, segmentation enables the task of recognition to proceed with much weaker

supervision than we require today. And you think of segmentation as this kind of task

that takes on a visual scene and breaks it apart into interesting entities that might be useful

for whatever the task is. Yeah. And it is not semantics free. So I think, I mean, it blends

into, it involves perception and cognition. It is not, I think the mistake that we used

to make in the early days of computer vision was to treat it as a purely bottom up perceptual task.

It is not just that because we do revise our notion of segmentation with more experience,

right? Because, for example, there are objects which are non rigid, like animals or humans.

And I think understanding that all the pixels of a human are one entity is actually quite a

challenge because the parts of the human, they can move independently. The human wears clothes,

so they might be differently colored. So it's all sort of a challenge.

You mentioned the three hours of computer vision, our recognition reconstruction,

reorganization. Can you describe these three hours and how they interact?

Yeah. So recognition is the easiest one because that's what I think people generally think of

as computer vision achieving these days, which is labels. So is this a cat? Is this a dog?

Is this a chihuahua? I mean, it could be very fine grained, like specific breed of a dog

or a specific species of bird, or it could be very abstract like animal.

But given a part of an image or a whole image, say put a label on it.

Yeah. So that's recognition. Reconstruction is essentially, you can think of it as inverse

graphics. I mean, that's one way to think about it. So graphics is you have some internal computer

representation and you have a computer representation of some objects arranged in a scene.

And what you do is you produce a picture. You produce the pixels corresponding to a rendering

of that scene. So let's do the inverse of this. We are given an image and we say, oh, this image

arises from some objects in a scene looked at with a camera from this viewpoint. And we might

have more information about the objects like their shape, maybe the textures, maybe color,

et cetera, et cetera. So that's the reconstruction problem. In a way, you are in your head creating

a model of the external world. Okay. Reorganization is to do with essentially finding these entities.

So it's organization. The word organization implies structure. So in perception, in psychology,

we use the term perceptual organization, that the world is not just an image is not just seen as

is not internally represented as just a collection of pixels. But we make these entities, we create

these entities, objects, whatever you want to call it. And the relationship between the entities

as well? Or is it purely about the entities? It could be about the relationships, but mainly

we focus on the fact that there are entities. Okay. So I'm trying to pinpoint what the organization

means. So organization is that instead of like a uniform grid, we have this structure of objects.

So segmentation is the small part of that. So segmentation gets us going towards that.

Yeah. And you kind of have this triangle where they all interact together.

Yes. So how do you see that interaction in sort of reorganization is yes,

defining the entities in the world. The recognition is labeling those entities. And then reconstruction

is what filling in the gaps? Well, to, for example, see impute some 3D objects corresponding to each

of these entities, that would be part of adding more information that's not there in the raw data.

Correct. I mean, I started pushing this kind of a view in the around 2010 or something like that,

because at that time in computer vision, the distinction that people were just working on

many different problems, but they treated each of them as a separate isolated problem

with each with its own data set. And then you try to solve that and get good numbers on it.

So I wasn't, I didn't like that approach because I wanted to see the connection between these.

And if people divided up vision into various modules, the way they would do it is as low

level, mid level and high level vision corresponding roughly to the psychologist's

notion of sensation, perception and cognition. And I didn't, that didn't map to tasks that people

cared about. Okay. So therefore, I tried to promote this particular framework as a way

of considering the problems that people in computer vision were actually working on

and trying to be more explicit about the fact that they actually are connected to each other.

And I was at that time just doing this on the basis of information flow. Now it turns out

in the last five years or so, in the post the deep learning revolution that this architecture

has turned out to be very conducive to that because basically in these neural networks,

we are trying to build multiple representations. There can be multiple output heads sharing

common representations. So in a certain sense, today, given the reality of what solutions people

have to this, I do not need to preach this anymore. It is just there. It's part of the solution space.

So speaking of neural networks, how much of this problem of computer vision,

of reorganization, recognition can be reconstruction, how much of it can be learned end to end,

do you think? Sort of set it and forget it, just plug and play, have a giant dataset multiple,

perhaps multimodal, and then just learn the entirety of it. Well, so I think that currently

what that end to end learning means nowadays is end to end supervised learning. And that I would

argue is too narrow a view of the problem. I like this child development view, this lifelong

learning view, one where there are certain capabilities that are built up and then there

are certain capabilities which are built up on top of that. So that's what I believe in. So I think

end to end learning in this supervised setting for a very precise task to me is

kind of a sort of a limited view of the learning process.

Got it. So if we think about beyond purely supervised, look back to children. You mentioned

six lessons that we can learn from children of be multimodal, be incremental, be physical,

explore, be social, use language. Can you speak to these, perhaps picking one that you find most

fundamental to our time today? Yeah. So I mean, I should say to give a due credit, this is from a

paper by Smith and Gasser. And it reflects essentially, I would say, common wisdom among

child development people. It's just that these are, this is not common wisdom among people in

computer vision and AI and machine learning. So I view my role as trying to bridge the two worlds.

So let's take an example of a multimodal. I like that. So multimodal, a canonical example is

a child interacting with an object. So then the child holds a ball and plays with it.

So at that point, it's getting a touch signal. So the touch signal is getting a notion of 3D

shape, but it is sparse. And then the child is also seeing a visual signal. And these two,

so imagine these are two in totally different spaces. So one is the space of receptors on the

skin of the fingers and the thumb and the palm. And then these map onto these neuronal fibers

are getting activated somewhere. These lead to some activation in somatosensory cortex.

I mean, a similar thing will happen if we have a robot hand. And then we have the pixels corresponding

to the visual view, but we know that they correspond to the same object. So that's a very,

very strong cross calibration signal. And it is self supervisory, which is beautiful.

There's nobody assigning a label. The mother doesn't have to come and assign a label.

The child doesn't even have to know that this object is called a ball.

Okay, but the child is learning something about the three dimensional world from this

signal. I think tactile and visual, there is some work on, there is a lot of work currently

on audio and visual. Okay, and audio visual. So there is some event that happens in the world.

And that event has a visual signature, and it has an auditory signature. So there is this

glass bowl on the table, and it falls and breaks. And I hear the smashing sound and I see the pieces

of glass. Okay, I've built that connection between the two, right? We have people, I mean, this

become a hot topic in computer vision in the last couple of years. There are problems like

separating out multiple speakers, right? Which was a classic problem in, in audition,

they call this the problem of source separation or the cocktail party effect and so on.

But just try to do it visually when you also have, it becomes so much easier and so much

more useful. So the multimodal, I mean, there's so much more signal with multimodal and you can use

that for some kind of weak supervision as well. Yes, because they are occurring at the same time

in time. So you have time, which links the two, right? So at a certain moment, T1, you got a certain

signal in the auditory domain and a certain signal in the visual domain, but they must be causally

related. Yeah, that's an exciting area. Not well studied yet. Yeah, I mean, we have a little bit

of work at this, but so much more needs to be done. So this is a good example. Be physical,

that's to do with like something we talked about earlier that there's an embodied world.

To mention language, use language. So No Chomsky believes that language may be at the core of

cognition, at the core of everything in the human mind. What is the connection between language

and vision to you? Like what's more fundamental? Are they neighbors, is one the parent and the child,

the chicken and the egg? Oh, it's very clear. It is vision that is the parent. The parent is

the fundamental ability. Okay. Well, it comes before you think vision is more fundamental

in language. Correct. And you can think of it either in phylogeny or in ontogeny. So phylogeny

means if you look at evolutionary time, right? So we have vision that developed 500 million years

ago. Okay. Then something like when we get to maybe like 5 million years ago, you have the first

bipedal primate. So when we started to walk, then the hand became free. And so then manipulation,

the ability to manipulate objects and build tools and so on and so forth. So you said 500,000 years

ago? No, sorry. The first multicellular animals, which you can say had some intelligence, arose

500 million years ago. Okay. And now let's fast forward to say the last 7 million years,

which is the development of the hominid line, right? Where from the other primates, we have the

branch which leads on to modern humans. Now, there are many of these hominids, but the ones which

people talk about Lucy because that's like a skeleton from 3 million years ago. And we know

that Lucy walked. Okay. So at this stage, you have that the hand is free for manipulating objects.

And then the ability to manipulate objects, build tools and the brain size grew in this era.

So, okay. So now you have manipulation. Now, we don't know exactly when language arose.

But after that. But after that, because no apes have, I mean, so, I mean Chomsky is correct in

that that it is a uniquely human capability. And we primates, other primates don't have that.

But so it developed somewhere in this era. But it developed, I would, I mean,

argue that it probably developed after we had this stage of humans. I mean, the human species

already able to manipulate and hands free, much bigger brain size. And for that, there's a lot of

vision has already had to have developed. So the sensation and the perception may be some of the

cognition. Yeah. So those, so that vision, so the world, so these ancestors of us,

you know, three, four million years ago, they had, they had spatial intelligence.

So they knew that the world consists of objects. They knew that the objects were in

certain relationships to each other. They had observed causal interactions among objects.

They could move in space. So they had space and time and all of that. So language builds on that

substrate. So language has a lot of, I mean, I mean, the, all human languages have constructs

which depend on a notion of space and time. Where did that notion of space and time come from?

It had to come from perception and action in the world we live in.

Yeah. Well, you've referred to the spatial intelligence. Yeah. Yeah. So to linger a little

bit, we mentioned Turing and his mention of we should learn from children. Nevertheless, language

is the fundamental piece of the test of intelligence that Turing proposed. Yes. What do you think is

a good test of intelligence? Are you, what would impress the heck out of you? Is it

fundamentally in natural language or is there something in vision?

I think I wouldn't, I don't think we should have created a single test of intelligence.

So just like I don't believe in IQ as a single number, I think generally there can be many

capabilities which are correlated perhaps. So I think that there will be, there will be

accomplishments which are visual accomplishments, accomplishments which are accomplishments in

manipulation or robotics, and then accomplishments in language. I do believe that language will

be the hardest nut to crack. Really? Yeah. So what's harder to pass the spirit of the Turing

test or like whatever formulation will make it natural language, convincingly a natural language,

like somebody you would want to have a beer with, hang out and have a chat with,

or the general natural scene understanding, you think language is the problem?

I think I'm not a fan of the, I think Turing test, that Turing as he proposed the test in 1950

was trying to solve a certain problem. Yeah, imitation. Yeah. And I think it made a lot of

sense then, where we are today, 70 years later, I think we should not worry about that. I think

the Turing test is no longer the right way to channel research in AI, because that it takes

us down this path of this chatbot which can fool us for five minutes or whatever. I think

I would rather have a list of 10 different tasks. I mean, I think the tasks which they're

tasked in the manipulation domain, tasks in navigation, tasks in visual scene understanding,

tasks in reading a story and answering questions based on that. I mean, so my favorite language

understanding tasks would be reading a novel and being able to answer arbitrary questions from it.

Okay. Right. I think that to me, and this is not an exhaustive list by any means,

so I would, I think that that's what we, where we need to be going to and each of these,

on each of these axes, there's a fair amount of work to be done.

So on the visual understanding side, in this intelligence Olympics that we've set up,

what's a good test for one of many of visual scene understanding? Do you think such benchmarks

exist? Sorry to interrupt. No, there aren't any. I think, I think essentially to me,

a really good aid to the blind. So suppose there was a blind person and I needed to assist the

blind person. So ultimately, like we said, vision that aids in the action in the survival in this

world. Yeah. Maybe in the simulated world. Maybe easier to, to measure performance in a simulated

world. What we are ultimately after is performance in the real world. So David Hilbert in 1900 proposed

23 open problems of mathematics, some of which are still unsolved, most important, famous of

which is probably the Riemann hypothesis you've thought about and presented about the Hilbert

problems of computer vision. So let me ask, what do you today? I don't know when the last year you

presented that 2015, but versions of it, you're kind of the face and the spokesperson for computer

vision. It's your job to state what the problem, the open problems are for the field. So what

today are the Hilbert problems of computer vision? Do you think? Let me pick one to,

which I regard as clearly, clearly unsolved, which is what I would call long form video

understanding. So, so we have a video clip and we want to understand the behavior in there

in terms of agents, their goals, intentionality and make predictions about what might happen.

So that kind of understanding which goes away from atomic visual action. So in the short

range, the question is, are you sitting? Are you standing? Are you catching a ball?

That we can do now. Or even if we can't do it fully accurately, if we can do it at 50%,

maybe next year we'll do it at 65 and so forth. But I think the long range video understanding,

I don't think we can do today. And that means so long. And it blends into cognition. That's

the reason why it's challenging. So you have to track, you have to understand the entities,

you have to understand the entities, you have to track them, and you have to have some kind of model

of their behavior. Correct. And their behavior might be, these are agents. So they're not just

like passive objects, but they're agents. So therefore, they might, they would exhibit goal

directed behavior. Okay. So this is, this is one area. Then I will talk about, say, understanding

the world in 3D. Now, this may seem paradoxical because in a way, we have been able to do 3D

understanding even like 30 years ago, right? But I don't think we currently have the richness of

3D understanding in our computer vision system that we would like. Because, so let me elaborate on

that a bit. So currently, we have two kinds of techniques which are not fully unified. So

there are the kinds of techniques from multi view geometry that you have multiple pictures of a scene

and you do a reconstruction using stereoscopic vision or structure for motion. But these techniques

do not, they totally fail if you just have a single view because they are relying on this,

this multiple view geometry. Okay. Then we have some techniques that we have developed in the

computer vision community, which try to guess 3D from single views. And these techniques are based

on, on a supervised learning and they are based on having a training time, 3D models of objects

available. And this is completely unnatural supervision, right? That's not, CAD models are

not injected into your brain. Okay. So what would I like? What I would like would be a kind of

learning as you move around the world notion of 3D. So we, we have our succession of visual

experiences. And from those, we, so in, as part of that, I might see a chair from different view

points or a table from view point, different view points and so on. Now as part, that enables me

to build some internal representation. And then next time I just see a single photograph.

And it may not even be of that chair, it's of some other chair. And I have a guess of what its

3D shape is like. So you're almost learning the CAD model kind of? Yeah, implicitly. I mean,

implicitly. I mean, the CAD model need not be in the same form as used by computer graphics

programs. It's hidden in the representation. It's hidden in the representation, the ability

to predict new views and what I would see if I went to such and such position.

By the way, on a small tangent on that, are you uncomfortable, are you okay or comfortable with

neural networks that do achieve visual understanding that do, for example, achieve this kind of 3D

understanding? And you don't know how they, you don't know the, you're not able to interest,

but you're not able to visualize or understand or interact with the representation. So the fact

that they're not or may not be explainable. Yeah, I think that's fine. To me, that is, so

let me put some caveats on that. So it depends on the setting. So first of all, I think

humans are not explainable. Yeah, that's a really good point. One human to another human is not

fully explainable. I think there are settings where explainability matters. And these might,

these are, these might be, for example, questions on medical diagnosis. So I'm in a setting where

maybe the doctor, maybe a computer program has made a certain diagnosis.

And then depending on the diagnosis, perhaps I should have treatment A or treatment B,

right? So now is the computer programs diagnosis based on data, which was data collected off

for American males who are in their 30s and 40s, and maybe not so relevant to me,

maybe it is relevant, you know, et cetera, et cetera. And we, I mean, in medical diagnosis,

we have major issues to do with the reference class. So we may have acquired statistics from

one group of people and applying it to a different group of people who may not share all the same

characteristics. The data might have, there might be error bars in the prediction. So that prediction

should really be taken with a huge grain of salt. And, but this has an impact on what treatments

should be picked, right? So, so there are settings where I want to know more than just

this is the answer. But what I acknowledge is that the, so, so, so I, in that sense,

explainability and interpretability may matter. It's about giving error bounds and a better sense

of the quality of the decision. Where, what I, where I'm willing to sacrifice interpretability

is that I believe that there can be systems which can be highly performant, but which are internally

black boxes. And, and that seems to be words headed. Some of the best performing systems

are essentially black boxes, fundamentally by their construction. You and I are black boxes

to each other. Yeah. So the nice thing about the black boxes we are is, so we ourselves are black

boxes, but we're also the, those of us who are charming are able to convince others, like explain

the black, what's going on inside the black box with narratives of stories. So in some sense,

neural networks don't have to actually explain what's going on inside. They just have to come

up with stories real or fake that convince you that they know what's going on. And I'm sure we

can do that. We can create those neural, those stories, neural networks can create those stories.

Yeah. And the transformer will be involved. Do you think we will ever build a system of

human level or super human level intelligence? We've kind of defined what it takes to try to

approach that. But do you think we'll, do you think that's within our reach? The thing that we

thought we could do, what Turing thought actually we could do by year 2000, right? Do you think

we'll ever be able to do? Yeah. So I think there are two answers here. One question, one answer is

in principle, can we do this at some time? And my answer is yes. The second answer is a pragmatic

one. Do you think we will be able to do it in the next 20 years or whatever? And to that my

answer is no. So, and of course that's a wild guess. I think that Donald Rumsfeld is not a

favorite person of mine, but one of his lines was very good, which is about known unknowns,

known unknowns and unknown unknowns. So in the business we are in, there are known unknowns

and we have unknown unknowns. So I think with respect to a lot of what the case in

vision and robotics, I feel like we have known unknowns. So I have a sense of where we need

to go and what the problems that need to be solved are. I feel with respect to natural language,

understanding and high level cognition, it's not just known unknowns, but also unknown unknowns.

So it is very difficult to put any kind of a time frame to that.

Do you think some of the unknown unknowns might be positive in that they'll surprise us and make

the job much easier? So fundamental breakthroughs? I think that is possible because certainly I have

been very positively surprised by how effective these deep learning systems have been because I

certainly would not have believed that in 2010. I think what we knew from the mathematical theory

was that convex optimization works when there's a single global optima than

this gradient descent techniques would work. Now these are nonlinear systems with nonconvex

systems. Huge number of variables. So overparameterized. Overparameterized. And the people who used to

play with them a lot, the ones who were totally immersed in the lore and the black magic, they

knew that they worked well even though they were. Really? I thought like everybody was.

No, the claim that I hear from my friends like Jan Lekoon and so forth is that they feel that

they were comfortable with them. Well, he says that now. But the community as a whole

was certainly not. And I think we were, to me, that was the surprise that they actually

worked robustly for a wide range of problems from a wide range of initializations and so on.

And so that was certainly more rapid progress than we expected. But then there are certainly

lots of times. In fact, most of the history in AI is when we have made less progress at a slower

rate than we expected. So we just keep going. I think what I regard as really unwarranted

are these fears of AGI in 10 years and 20 years and that kind of stuff. Because that's based on

completely unrealistic models of how rapidly we will make progress in this field.

So I agree with you. But I've also gotten a chance to interact with very smart people who

really worry about the existential threats of AI. And as an open minded person, I'm sort of

taking it in. Do you think if AI systems, in some way, the unknown unknowns, not super

intelligent AI, but in ways we don't quite understand the nature of super intelligence,

will have a detrimental effect on society? Do you think this is something we should be

worried about? Or we need to first allow the unknown unknowns to become known unknowns?

I think we need to be worried about AI today. I think that it is not just a worry we need to

have when we get that AGI. I think that AI is being used in many systems today. And there might

be settings, for example, when it causes biases or decisions which could be harmful, I mean,

decisions which could be unfair to some people, or it could be a self driving cars which kills

a pedestrian. So AI systems are being deployed today, right? And they are being deployed in

many different settings, maybe in medical diagnosis, maybe in a self driving car, maybe

in selecting applicants for an interview. So I would argue that when these systems

make mistakes, there are consequences. And we are in a certain sense responsible for those

consequences. So I would argue that this is a continuous effort. And this is something that

in a way is not so surprising. It's about all engineering and scientific progress which

great power comes, great responsibility. So as these systems are deployed, we have to worry

about them. And it's a continuous problem. I don't think of it as something which will

suddenly happen on some day in 2079, for which I need to design some clever trick.

I'm saying that these problems exist today. And we need to be continuously on the lookout for

worrying about safety, biases, risks, right? I mean, the self driving car kills a pedestrian

and they have, right? I mean, there's Uber incident in Arizona, right? It has happened, right?

This is not about AGI. In fact, it's about a very dumb intelligence which is killing people.

The worry people have with AGI is the scale. And I, but I think you're 100% right is

like the thing that worries me about AI today. And it's happening in a huge scale is recommender

system, recommendation systems. So if you look at Twitter or Facebook or YouTube, they're controlling

the ideas that we have access to, the news and so on. And that's a fundamentally machine learning

algorithm behind each of these recommendations. And they, I mean, my life would not be the same

without these sources of information. I'm a totally new human being. And the ideas that I know

are very much because of the internet, because of the algorithm that recommend those ideas.

And so as they get smarter and smarter, I mean, that is the AGI is that's the algorithm that's

recommending the next YouTube video you should watch has control of millions of billions of people

that that algorithm is already super intelligent and has complete control of the population.

Not a complete, but very strong control. For now, we can turn off YouTube, we can just go have a

normal life outside of that. But the more and more that gets into our life, it's that algorithm will

start depending on it and the different companies that are working on the algorithm. So I think

it's, you're right, it's already, it's already there. And YouTube in particular is using computer

vision, doing their hardest to try to understand the content of videos so they could be able to

connect videos with the people who would benefit from those videos the most. And so that development

could go in a bunch of different directions, some of which might be harmful. So yeah, you're

right. The, the, the threats of AI here already, we should be thinking about them. On a philosophical

notion. If you could personal, perhaps, if you could relive a moment in your life outside of family,

because it made you truly happy or it was a profound moment that impacted the direction of your life,

what moment would you go to?

I don't think of single moments, but I look over the long haul. I feel that I've been very lucky

because I feel that I think that in scientific research, a lot of it is about being at the

right place at the right time. And you can, you can work on problems at a time when they're just

too premature, you know, you beat but your head against them and, and nothing happens because

it's the prerequisites for success are not there. And then there are times when you are in a field

which is all pretty mature and you can only solve curricules upon curricules. I've been lucky to

have been in this field, which for 34 years, 34, well, actually 34 years is a professor at Berkeley,

so longer than that, which when I started in it was just like some little crazy, absolutely

useless field, which couldn't really do anything to a time when it's really, really

solving a lot of practical problems has a lot has offered a lot of tools for scientific research,

right, because computer vision is impactful for images in biology or astronomy and so on and

so forth. And we have, so we have made great scientific progress, which has had real practical

impact in the world. And I feel lucky that I, I got in at a time when the field was

very young and at a time when it is, it's now mature, but not fully mature. It's mature, but not

done. I mean, it's really in still in a, in a productive phase. Yeah, I think people 500 years

from now would laugh at you calling this field mature. That is very possible. Yeah. So, but you're

also, lest I forget to mention, you've also mentored some of the biggest names of computer

vision, computer science and AI today. There's so many questions I could ask, but really is

what, what is it? How did you do it? What does it take to be a good mentor? What does it take to be

a good guide? Yeah, I think what I feel I've been lucky to have had very, very smart and hardworking

and creative students. I think some part of the credit just belongs to being at Berkeley. I think

those of us who are at top universities are blessed because we have very, very smart and capable

students coming on, knocking on our door. So, so I have to be humble enough to acknowledge that.

But what have I added? I think I have added something. What I have added is, I think what

I've always tried to teach them is a sense of picking the right problems. So, I think that in

science, in the short run, success is always based on technical competence. You're, you know,

you're quick with math or you are whatever. I mean, there's certain technical capabilities

which make for short range progress. Long range progress is really determined by asking the right

questions and focusing on the right problems. And I feel that what I've been able to bring to the

table in terms of advising these students is some sense of taste of what are good problems.

What are problems that are worth attacking now as opposed to waiting 10 years?

What's a good problem if you could summarize? If is that possible to even summarize? Like what's

your sense of a good problem? I think I think I have a sense of what is a good problem, which is

there is a British scientist. In fact, he won a Nobel Prize, Peter Medover, who has a book on this.

And basically he calls it the research is the art of the soluble. So, we need to sort of find

problems which are not yet solved, but which are approachable. And he sort of refers to this

sense that there is this problem which isn't quite solved yet, but it has a soft underbelly.

There is some place where you can spear the beast. And having that intuition that this

problem is ripe is a good thing, because otherwise you can just beat your head and not make progress.

So, I think that is important. So, if I have that and if I can convey that to students,

it's not just that they do great research while they're working with me, but that they continue

to do great research. So, in a sense, I'm proud of my students and their achievements and their

great research, even 20 years after they've seized being my student. So, some part developing,

helping them develop that sense that a problem is not yet solved, but is solvable. Correct.

The other thing which I have, which I think I bring to the table, is a certain intellectual

breadth. I've spent a fair amount of time studying psychology, neuroscience, relevant

areas of applied math and so forth. So, I can probably help them see some connections

to disparate things, which they might not have otherwise. So, the smart students coming into

Berkeley can be very deep in the sense, they can think very deeply, meaning very hard down one

particular path. But where I could help them is the shallow breadth, but whereas they would have

the narrow depth, but that's of some value. Well, it was beautifully refreshing just to hear you

naturally jump to psychology back to computer science and this conversation back and forth.

I mean, that's actually a rare quality and I think it's certainly for students empowering

to think about problems in a new way. So, for that and for many other reasons, I really enjoyed

this conversation. Thank you so much. It was a huge honor. Thanks for talking to me.

It's been my pleasure. Thanks for listening to this conversation with Jitendra Malik and thank

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Don't ask me how to spell that. I don't remember myself. And now let me leave you with some words

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and hope to see you next time.