Dileep George is a researcher at the intersection of neuroscience and artificial intelligence, co-founder of Vicarious, formerly co-founder of Numenta. From the early work on Hierarchical temporal memory to Recursive Cortical Networks to today, Dileep's always sought to engineer intelligence that is closely inspired by the human brain.

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EPISODE LINKS:
Dileep's Twitter: https://twitter.com/dileeplearning
Vicarious Research: https://www.vicarious.com/science

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OUTLINE:
0:00 - Introduction
4:50 - Building a model of the brain
17:11 - Visual cortex
27:50 - Probabilistic graphical models
31:35 - Encoding information in the brain
36:56 - Recursive Cortical Network
51:09 - Solving CAPTCHAs algorithmically
1:06:48 - Hype around brain-inspired AI
1:18:21 - How does the brain learn?
1:21:32 - Perception and cognition
1:25:43 - Open problems in brain-inspired AI
1:30:33 - GPT-3
1:40:41 - Memory
1:45:08 - Neuralink
1:51:32 - Consciousness
1:57:59 - Book recommendations
2:06:49 - Meaning of life

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The following is a conversation with Dalipe George, a researcher at the intersection of neuroscience and artificial intelligence, co-founder of Vicarious with Scott Phoenix, and formerly co-founder of Numenta with Jeff Hawkins, who's been on this podcast, and Donna Dubinsky.

From his early work on hierarchical temporal memory to recursive cortical networks to today, De Leap's always sought to engineer intelligence that is closely inspired by the human brain.

As a side note, I think we understand very little about the fundamental principles underlying the function of the human brain, but the little we do know gives hints that may be more useful for engineering intelligence than any idea in mathematics, computer science, physics, and scientific fields outside of biology.

And so the brain is a kind of existence proof that says it's possible, keep at it.

I should also say that brain inspired AI is often overhyped and use this fodder, just as quantum computing for marketing speak.

But I'm not afraid of exploring these sometimes overhyped areas since where there's smoke, there's sometimes fire.

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And now here's my conversation with Dalip George.

Do you think we need to understand the brain in order to build it?

you want to build the brain, we definitely need to understand how it works.

So Blue Brain, or Henry Markram's project, is trying to build a brain without understanding it, just trying to put details of the brain from neuroscience experiments into a giant simulation by putting more and more neurons, more and more details.

But that is not going to work because when it doesn't perform as what you expect it to do, then what do you do?

You do, you just keep adding more details.

So it's a, so unless you understand, unless you have a theory about how the system is supposed to work, how the pieces are supposed to fit together, what they're going to contribute, you can't build it.

So can you actually linger on and describe the Blue Brain Project?

It's kind of a fascinating principle, an idea to try to simulate the brain.

We're talking about the human brain, right?

Right, human brains and rat brains or cat brains have lots in common, that the cortex, the neocortex structure is very similar.

So initially they were trying to just simulate a cat brain.

Or as it happens in most of the simulations, you easily get 1 thing out, which is oscillations.

If you simulate a large number of neurons, they oscillate.

And you can adjust the parameters and say that, oh, oscillations match the rhythm that we see in the brain, et cetera.

So the idea is, is the simulation at the level of individual neurons?

Yeah, so the Blue Brain project, the original idea as proposed was you put very detailed biophysical neurons, biophysical models of neurons, and you interconnect them according to the statistics of connections that we have found from real neuroscience experiments, and then turn it on and see what happens.

And these neural models are incredibly complicated in themselves, right?

Because these neurons are modeled using this idea called Hodgkin-Huxley models, which are about how signals propagate in a cable, and there are active dendrites, all those phenomena, which those phenomena themselves, we don't understand that well.

And then we put in connectivity, which is part guesswork, part observed.

And of course, if we do not have any theory about how it is supposed to work, we just have to take whatever comes out of it as, okay, this is something interesting.

But in your sense, like these models of the way signal travels along, or like with the axons and all the basic models, they're too crude.

Oh, well, actually, they are pretty detailed and pretty sophisticated, and they do replicate the neural dynamics.

If you take a single neuron, and you try to turn on the different channels, the calcium channels and the different receptors and see what the effect of turning on or off those channels are in the neuron's spike output, people have built pretty sophisticated models of that.

And they are, I would say, in the regime of correct.

Well, see, the correctness, that's interesting, because you mentioned it at several levels.

The correctness is measured by looking at some kind of aggregate statistics.

It would be more the spiking dynamics of a single neuron.

Yeah, and yeah, these models, because they are going to the level of mechanism, right?

So they are basically looking at, okay, what is the effect of turning on an ion channel?

And you can model that using electric circuits.

And then, so they are model, so it is not just a function fitting, it is people are looking at the mechanism underlying it and putting that in terms of electric circuit theory, signal propagation theory, and modeling that.

And so those models are sophisticated, but getting a single neurons model 99% right does not still tell you how to, you know, it would be the analog of getting a transistor model right and now trying to build a microprocessor.

And if you just observe, you know, if you did not understand how a microprocessor works, but you say, oh, I now can model 1 transistor well, and now I will just try to interconnect the transistors according to whatever I could guess from the experiments and try to simulate it, then it is very unlikely that you will produce a functioning microprocessor.

When you want to produce a functioning microprocessor, you want to understand Boolean logic, how do the gates work, all those things, and then understand how do those gates get implemented using transistors.

Yeah, there's actually, I remember, this reminds me, there's a paper, maybe you're familiar with it, that I remember going through in a reading group that approaches a microprocessor from a perspective of a neuroscientist.

I think it basically, it uses all the tools that we have of neuroscience to try to understand, like as if we just aliens showed up to study computers and to see if those tools could be used to get any kind of sense of how the microprocessor works.

I think the final, the takeaway from at least this initial exploration is that we're screwed.

There's no way that the tools of neuroscience would be able to get us to anything, like not even Boolean logic.

I mean, it's just any aspect of the architecture of the function of the processes involved, the clocks, the timing, all that, you can't figure that out from the tools of neuroscience.

Yeah, so I'm very familiar with this particular paper.

I think it was called, Can a Neuroscientist Understand a Microprocessor?

Following the methodology in that paper, even an electrical engineer would not understand microprocessors.

So I don't think it is that bad in the sense of saying neuroscientists do find valuable things by observing the brain.

They do find good insights, but those insights cannot be put together just as a simulation.

You have to investigate what are the computational underpinnings of those findings.

How do all of them fit together from an information processing perspective?

You have to, somebody has to painstakingly put those things together and build hypothesis.

So I don't want to diss all of neuroscience of saying, oh, they're not finding anything.

No, that paper almost went to that level of neuroscientists will never understand.

I think they do find lots of useful things, but it has to be put together in a computational framework.

Yeah, I mean, but you know, just the AI systems will be listening to this podcast a hundred years from now, and they will probably, there's some non-zero probability they'll find your words laughable.

They're like, I remember humans thought they understood something about the brain, they were totally clueless.

There's a sense about neuroscience that we may be in the very, very early days of understanding the brain.

In your perspective, how far are we into understanding any aspect of the brain?

So the dynamics of the individual neuro communication to the how in a collective sense, how they're able to store information, transfer information, how intelligence then emerges, all that kind of stuff.

Yeah, so timelines are very, very hard to predict.

We know that when we look back, the first flight was in

In 1900, there was a New York Times article on flying machines that do not fly.

And humans might not fly for another 100 years.

And so, but no, they flew 3 years after that.

So it is, you know, it's very hard to, so.

Well, and on that 0.1 of the Wright brothers, I think 2 years before said that, like he said, like some number, like 50 years, he has become convinced that it's

I mean, that's attributed to when, that's like the entrepreneurial battle of like depression of going through just like thinking this is impossible.

But there, yeah, there's something, even the person that's in it is not able to see, estimate correctly.

Exactly, But I can tell from the point of, you know, objectively, what are the things that we know about the brain and how that can be used to build AI models, which can then go back and inform how the brain works.

So my way of understanding the brain would be to basically say, look at the insights neuroscientists have found.

Understand that from a computational angle, information processing angle, build models using that.

And then building that model which functions, which is a functional model, which is doing the task that we want the model to do.

It is not just trying to model a phenomena in the brain.

It is trying to do what the brain is trying to do on the whole functional level.

And building that model will help you fill in the missing pieces that, you know, biology just gives you the hints and building the model, you know, fills in the rest of the pieces of the puzzle.

And then you can go and connect that back to biology and say, okay, now it makes sense that this part of the brain is doing this, or this layer in the cortical circuit is doing this.

And then continue this iteratively, because now that will inform new experiments in neuroscience.

And of course, building the model and verifying that in the real world will also tell you more about, does the model actually work?

And you can refine the model, find better ways of putting these neuroscience insights together.

So I would say it is, you know, so neuroscientists alone, just from experimentation, will not be able to build a model of the brain, a functional model of the brain.

So there's lots of efforts, which are very impressive efforts in collecting more and more connectivity data from the brain.

How are the microcircuits of the brain connected with each other?

And at the same time, those do not itself, by themselves, convey the story of how does it work.

And somebody has to understand, okay, why are they connected like that?

And we do that by building models in AI, using hints from neuroscience and repeat the cycle.

So what aspect of the brain are useful in this whole endeavor?

Which by the way, I should say, you're both a neuroscientist and AI person.

I guess the dream is to both understand the brain and to build AGI systems.

So it's like an engineer's perspective of trying to understand the brain.

So what aspects of the brain, functionally speaking, like you said, do you find interesting?

So 1 is, if you look at the visual cortex, and the visual cortex is a large part of the brain.

I forgot the exact fraction, but a huge part of our brain area is occupied by just vision.

So visual cortex is not just a feed-forward cascade of neurons.

There are a lot more feedback connections in the brain compared to the feed-forward connections.

And it is surprising to the level of detail neuroscientists have actually studied this.

If you go into neuroscience literature and poke around and ask, have they studied what will be the effect of poking a neuron in level IT in level V1?

And you will say, yes, they have studied that.

I mean, it's not a random exploration at all.

They are very, experimental neuroscientists are very, very systematic in how they probe the brain, because experiments are very costly to conduct.

So they are very hypothesis-driven in how they probe the brain.

And often what I find is that when we have a question in AI about, has anybody probed how lateral connections in the brain works?

And when you go and read the literature, yes, people have probed it, and people have probed it very systematically.

And they have hypothesis about how those lateral connections are supposedly contributing to visual processing.

But of course they haven't built very, very functional detailed models of it.

way, how do they, in those studies, Sorry to interrupt, do they stimulate like a neuron in 1 particular area of the visual cortex and then see how the signal travels kind of thing?

Fascinating, very, very fascinating experiments.

So I can give you 1 example I was impressed with.

This is, So before going to that, let me give you an overview of how the layers in the cortex are organized.

Visual cortex is organized into roughly 4 hierarchical levels.

Okay, so there is this, I'm talking about just object recognition pathway.

Okay, and then in V1 itself, So there is a very detailed microcircuit in V1 itself.

That is organization within a level itself.

The cortical sheet is organized into multiple layers, and there are columnar structure.

This layer-wise and columnar structure is repeated in V1, V2, V4, IT, all of them, right?

And the connections between these layers within a level, you know, in V1 itself, there are 6 layers, roughly, and the connections between them, there is a particular structure to them.

And now, so 1 example of an experiment people did is when you present a stimulus, which is, let's say, requires separating the foreground from the background of an object.

So it's a textured triangle on a textured background.

And you can check, does the surface settle first, or does the contour settle first?

Settle in the sense that, so when you finally form the percept of the triangle, you understand where the contours of the triangle are and you also know where the inside of the triangle is, right?

Now, you can ask, what is the dynamics of forming that final percept?

Do the neurons first find the edges and converge on where the edges are, and then they find the inner surfaces, or does it go the other way around?

In this case, it turns out that it first settles on the edges, it converges on the edge hypothesis first, and then the surfaces are filled in from the edges to the inside.

And the detail to which you can study this, it's amazing that you can actually not only find the temporal dynamics of when this happens, and then you can also find which layer in V1, which layer is encoding the edges, which layer is encoding the surfaces, and which layer is encoding the feedback, which layer is encoding the feedforward, and what's the combination of them that produces the final person.

And these kinds of experiments stand out when you try to explain illusions.

1 example of a favorite illusion of mine is the Kanitsa triangle.

I don't know that you are familiar with this 1.

So this is an example where it's a triangle, but only the corners of the triangle are shown in the stimulus.

And then your visual system hallucinates the edges.

And When you look at it, you will see a faint edge.

You can go inside the brain and look, do actually neurons signal the presence of this edge?

Because they are not receiving anything from the input.

The input is black for those neurons, right?

So if a real contour is present in the input, then the neurons immediately signal, okay, there is an edge here.

When it is an illusory edge, it is clearly not in the input.

And you can say that, okay, these are, it's the feedback connections that is causing them to fire.

And they happen later and you can find the dynamics of them.

So these studies are pretty impressive and very detailed.

So by the way, just a step back, you said that there may be more feedback connections and feedforward connections.

First of all, if it's just for like a machine learning folks, I mean, that's crazy that there's all these feedback connections.

deep learning, you start to think about the human brain as a kind of feed forward mechanism.

So what the heck are these feedback connections?

What are we supposed to think about them?

Yeah, so this fits into a very beautiful picture about how the brain works, right?

So The beautiful picture of how the brain works is that our brain is building a model of the world.

So our visual system is building a model of how objects behave in the world.

And we are constantly projecting that model back onto the world.

So what we are seeing is not just a feed forward thing that just gets interpreted in a feed forward part.

We are constantly projecting our expectations onto the world.

And what the final percept is a combination of what we project onto the world combined with what the actual sensory input is.

Almost like trying to calculate the difference and then trying to interpret the difference.

Yeah, I wouldn't put it as calculating the difference.

It's more Like what is the best explanation for the input stimulus based on the model of the world I have?

And that's where all the illusions come in.

But that's an incredibly efficient process.

So the feedback mechanism, it just helps you constantly, yeah, so hallucinate how the world should be based on your world model, and then just looking at if there's novelty, like trying to explain it.

Hence, that's why movement, we detect movement really well, there's all these kinds of things.

And this is like at all different levels of the cortex you're saying.

This happens at the lowest level, at the highest level.

In fact, feedback connections are more prevalent in everywhere in the cortex.

And so 1 way to think about it, and there's a lot of evidence for this, is inference.

So basically, if you have a model of the world, and when some evidence comes in, what you are doing is inference, right?

You are trying to now explain this evidence using your model of the world.

And this inference includes projecting your model onto the evidence and taking the evidence back into the model and doing an iterative procedure.

And this iterative procedure is what happens using the feed-forward feedback propagation.

And feedback affects what you see in the world and it also affects feed-forward propagation.

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Dileep George: Brain-Inspired AI | Lex Fridman Podcast #115