This is a talk for course 6.S099: Artificial General Intelligence. This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world.

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He's a professor at Northeastern University working on various aspects of computational agents that exhibit human level intelligence.

Thanks a lot and thanks for having me here.

So the title that was on the page was Cognitive Modeling.

I'll kind of get there, but I wanted to put it in context.

So the bigger theme here is I want to talk about what's called cognitive architecture.

And if you've never heard about that before, that's great.

And I wanted to contextualize that as, how is that 1 approach to get us to AGI?

And I say what my view of AGI is, and put up a whole bunch of TV and movie characters that I grew up with that inspire me.

That will lead us into what is this thing called cognitive architecture.

It's a whole research field that crosses neuroscience, psychology, cognitive science, and all the way into AI.

So I'll try to give you kind of the historical big picture view of it, what some of the actual systems are out there that might be of interest to you.

And then we'll kind of zoom in on 1 of them that I've done a good amount of work with called SOAR.

And what I'll try to do is tell a story, a research story of how we started with kind of a core research question.

We look to how humans operate, understood that phenomenon, and then took it and saw really interesting results from it.

And so at the end, if this field is of interest, there's a few pointers for you to go read more and go experience more of cognitive architecture.

So just a rough definition of AGI, given this is an AGI class.

Depending the direction that you're coming from.

It might be kind of understanding intelligence or it might be developing intelligence systems that are operating at the level of human level intelligence.

The typical differences between this and other sorts of maybe AI, machine learning systems.

We want systems that are gonna persist for a long period of time.

We want them robust to different conditions.

And here's the crux of it, working on different tasks.

And in a lot of cases, tasks they didn't know were coming ahead of time.

I got into this because I clearly watched too much TV and too many movies, and then I looked back at this and I realized I think I'm covering 70s, 80s, 90s, noughts, I guess it is, and today.

And so this is what I wanted out of AI, and this is what I wanted to work with.

And then there's the reality that we have today.

So instead of, so who's watched Knight Rider for instance?

I don't think that exists yet, but maybe we're getting there.

And in particular for fun, during the Amazon sale day, I got myself an Alexa, and I could just see myself at some point saying, hey Alexa, please write me an R-sync script, to sync my class.

And if you have an Alexa, you probably know the following phrase, this just always hurts me inside, which is, sorry, I don't know that 1.

That's, a lot of people have no idea what I'm asking, let alone how to do that.

So what I want Alexa to respond with after that is, do you have time to teach me?

And to provide some sort of interface by which back and forth we can kind of talk through this.

That we aren't there yet, to say the least, but I'll talk later about some work on a system called ROSI that's working in that direction.

We're starting to see some ideas about being able to teach systems how to work.

So folks who are in this field I think generally fall into these 3 categories.

They want to learn new things, generate knowledge, work on hard problems.

I think there are folks who are in that middle cognitive modeling realm.

It's really understanding how humans think, how humans operate, human intelligence at multiple levels.

And if you can do that, 1, there's just knowledge in and of itself of how we operate, but there's a lot of really important applications that you can think of.

If we were able to not only understand, but predict how humans would respond, react, and various tasks.

There's some work in HCI or HRI, I'll get to later, where if you can predict how humans would respond to a task, you can iterate tightly and develop better interfaces.

It's already being used in the realm of simulation and in defense industries.

I happen to fall into the latter group, or the bottom group, which is systems development, which is to say just the desire to build systems for various tasks that are working on tasks that kind of current AI machine learning can't operate on.

And I think when you're working at this level or on any system that nobody's really achieved before, what do you do?

You kind of look to the examples that you have, which in this case that we know of, it's just humans, right?

Irrespective of your motivation, when you have kind of an intent that you want to achieve in your research, you kind of let that drive your approach.

The Turing test you might have heard of, or variants of it that have come before, these were folks who were trying to create systems that acted in a certain way, that acted intelligently.

And the kind of line that they drew, the benchmark that they used was to say, let's make systems that operate like humans do.

Cognitive modelers will fit up into this top point here to say it's not enough to act that way, but by some definition of thinking, we want the system to do what humans do, or at least be able to make predictions about it.

So that might be things like, what errors would the human make on this task?

Or how long would it take them to perform this task?

Or what emotion would be produced in this task?

There are folks who are still thinking about how the computer is operating, but trying to apply kind of rational rules to it.

So a logician, for instance, would say, if you have A and you have B, A gives you B, B gives you C, A should definitely give you C.

And so there are folks operating in that direction.

And then if you go to intro AI class anywhere around the country, particularly Berkeley, because they have graphics designers that I get to steal from, the benchmark would be what the system produces in terms of action, and the benchmark is some sort of optimal rational bound.

Irrespective of where you work in this space, there's kind of a common output that arrives when you research these areas, which is you can learn individual bits and pieces, and it can be hard to bring them together to build a system that either predicts or acts on different tasks.

So this is part of the transfer learning problem but it's also part of having distinct theories that are hard to combine together.

So I'm gonna give an example that comes out of cognitive modeling, or perhaps 3 examples.

So if you were in a HCI class or some intro psychology classes, 1 of the first things you learn about is Fitts' Law, which provides you the ability to predict the difficulty level of basically human pointing from where they start to a particular place.

And it turns out that you can learn some parameters and model this based upon just the distance from where you are to the target and the size of the target.

So both moving a long distance will take a while, but also if you're aiming for a very small point, that can take longer than if there's a large area that you just kind of have to get yourself to.

And so this is held true for many humans.

So let's say we've learned this, and then we move on to the next task, and we learn about what's called the power law of practice, which has been shown true in a number of different tasks.

What I'm showing here is 1 of them where you're going to draw a line through sequential set of circles here, starting at 1, going to 2, and so forth, not making a mistake, or at least not trying to, and try to do this as fast as possible.

And so for a particular person, we would fit the A, B, and C parameters and we'd see a power loss.

So as you perform this task more, you're gonna see a decrease in the amount of reaction time required to complete the task.

Great, we've learned 2 things about humans.

So for those who might have done some reinforcement learning, TD learning is 1 of those approaches, temporal difference learning, that's had some evidence of similar sorts of processes in the dopamine centers of the brain.

And it basically says in a sequential learning task, you perform the task, you get some sort of reward, how are you going to kind of update your representation of what to do in the future, such as to maximize expectation of future reward.

And there are various models of how that changes over time, and you can build up functions that allow you to form better and better and better given trial and error.

Great, so we've learned 3 interesting models here that hold true over multiple people, multiple tasks, And so my question is, if we take these together and add them together, how do we start to understand a task as quote unquote simple as chess?

Which is to say, we could ask questions, how long would it take for a person to play?

After they played a few games, how would they adapt themselves?

Or, if we wanted to develop a system that ended up being good at chess, or at least learning to become better at chess.

My question is, if you could, there doesn't seem to be a clear way to take these very, very individual theories and kind of smash them together and get a reasonable answer of how to play chess or how do humans play chess.

And so, gentleman in this slide is Alan Newell, 1 of the founders of AI, did incredible work in psychology and other fields.

He gave a series of lectures at Harvard in 1987 and they were published in 1990 called the Unified Theories of Cognition.

And his argument to the psychology community at that point was the argument on the prior slide.

They had many individual studies, many individual results.

And so the question was, how do you bring them together to gain this overall theory?

And so his proposal was unified theories of cognition, which became known as cognitive architecture, which is to say, to bring together your core assumptions, your core beliefs of what are the fixed mechanisms and processes that intelligent agents would use across tasks.

So the representations, the learning mechanisms, the memory systems, bring them together, implement them in a theory, and use that across tasks.

And the core idea is that when you actually have to implement this and see how it's going to work across different tasks, the interconnections between these different processes and representations would add constraints.

And over time, the constraints would start limiting the design space of what is necessary and what is possible in terms of building intelligent systems.

And So the overall goal from there was to understand and exhibit human level intelligence using these cognitive architectures.

A natural question to ask is, okay, so we've gone from a methodology of science that we understand how to operate in.

We make a hypothesis, we construct a study, we gather our data, we evaluate that data, and we falsify or we do not falsify the original hypothesis.

And we can do that over and over again, and we know that we're making forward progress scientifically.

If I've now taken that model and changed it into I have a piece of software and it's representing my theories and to some extent I can configure that software in different ways to work on different tasks.

And so there's a form of science called lactoseum.

And it's kind of shown pictorially here where you start with your core of what your beliefs are about where your head, what is necessary for achieving the goal that you have.

And around that you'll have kind of ephemeral hypotheses and assumptions that over time may grow and shrink.

And so you're trying out different things, trying out different things.

And if an assumption is around there long enough, it becomes part of that core.

And so as you work on more tasks and learn more, either by your work or by data coming in from someone else, the core is growing larger and larger.

You've got more constraints and you've made more progress.

And so what I wanted to look at were in this community, what are some of the core assumptions that are driving forward scientific progress?

So 1 of them actually came out of those lectures that are referred to as Newell's Time Scales of Human Action.

And so off on the left, the left 2 columns are both time units, just expressed somewhat differently.

Second from the left being maybe more useful to a lot of us in understanding daily life.

1 step over from there would be kind of at what level processes are occurring.

So the lowest 3 are down at kind of the substrate, the neuronal level.

We're building up to deliberate tasks that occur in the brain and tasks that are operating on the order of 10 seconds.

Some of these might occur in the psychology laboratory, but probably a step up to minutes and hours.

And then above that really becomes interactions between agents over time.

And so if we start with that, the things to take away is that the hypothesis is that regularities will occur at these different time scales and that they're useful.

And so those who operate at that lowest time scale might be considering neuroscience, cognitive neuroscience.

When you shift up to the next couple levels, what we would think about in terms of the areas of science that deal with that would be psychology and cognitive science, and then we shift up a level and we're talking about sociology and economics and the interplay between agents over time.

And so what we'll find with cognitive architecture is that most of them will tend to sit at the deliberate act.

We're trying to take knowledge of a situation and make a single decision And then sequences of decisions over time will build to tasks, and tasks over time will build to more interesting phenomenon.

I'm actually going to show that that isn't strictly true, that there are folks working in this field that actually do operate 1 level below.

So this is Herb Simon receiving the Nobel Prize in Economics and part of what he received that award for was an idea of bounded rationality.

So In various fields we tend to model humans as rational.

And his argument was, let's consider that human beings are operating under various kinds of constraints.

And so to model the rationality with respect to and bounded by how complex the problem is that they're working on, how big is that search space that they have to conquer, cognitive limitations, so speed of operations, amount of memory, short-term as well as long-term, as well as other aspects of our computing infrastructure that are going to keep us from being able to arbitrarily solve complex problems, as well as how much time is available to make that decision.

And so This is actually a phrase that came out of his speech when he received the Nobel Prize.

Decision makers can satisfice either by finding optimum solutions for a simplified world, which is to say, take your big problem, simplify it in some way, and then solve that, Or by finding satisfactory solutions for a more realistic world.

Take the world in all its complexity, take the problem in all its complexity, and try to find something that works.

Neither approach in general dominates the other, and both have continued to coexist.

And so what you're actually going to see throughout the cognitive architecture community is this understanding that some problems you're not gonna be able to get an optimal solution to if you consider, for instance, bounded amount of computation, bounded time, the need to be reactive to a changing environment, these sorts of issues.

And so in some sense, we can decompose problems that come up over and over again into simpler problems, solve those near optimally or optimally, Fix those in, optimize those, but more general problems we might have to satisfy some.

There's also the idea of the symbol system hypothesis.

So this is Alan Newell and Herb Simon there considering how a computer could play the game of chess.

So the physical symbol system talks about the idea of taking something, some signal, abstractly referred to as symbol, combining them in some ways to form expressions, and then having operations that produce new expressions.

A weak interpretation of the idea that symbol systems are necessary and sufficient for intelligent systems.

A very weak way of talking about it is the claim that there's nothing unique about the neuronal infrastructure that we have.

But if we got the software right, we could implement it in the bits, bytes, RAM, and processor that make up modern computers.

That's kind of the weakest way to look at this, that we can do it with silicon and not carbon.

Stronger way that this used to be looked at was more of a logical standpoint, which is to say if we can encode rules of logic, these tend to line up if we think intuitively of planning and problem-solving.

And if we can just get that right and get enough facts in there and enough rules in there that somehow intelligence, well, that's what we need for intelligence and eventually we can get to the point of intelligence and that's what you need for intelligence.

And that was a starting point that lasted for a while.

I think by now most folks in this field would agree that that's necessary to be able to operate logically, but that there are going to be representations and processes that will benefit from non-symbolic representation.

So particularly perceptual processing, visual, auditory, and processing things in a more standard machine learning sort of way, as well as taking advantage of statistical representations.

So we're getting closer to actually looking at cognitive architectures.

I did want to go back to the idea that different researchers are coming with different research foci.

And we'll start off with kind of the lowest level and understanding biological modeling.

So Lieber and Spahn both tried to model different degrees of low level details, parameters, firing rates, connectivities between different kind of levels of neuronal representations.

They build that up and then they try to build tasks above that layer, but always being very cautious about being true to human biological processes.

And a layer above there would be psychological modeling, which is to say trying to build systems that are true in some sense to areas of the brain, interactions in the brain, and being able to predict errors that we made, timing that we produced by the human mind.

And so there I'll talk a little bit about ActR.

This final level down here, These are systems that are focused mainly on producing functional systems that exhibit really cool artifacts and solve really cool problems.

And so I'll spend most of the time talking about Soar, but I want to point out a relative newcomer in the game called Sigma.

So to talk about spawn a little bit, we'll see if the sound works in here.

I'm going to let the creator take this 1, or not.

My name is Chris Weisman and I'm the director of the Centre for Theory of Neuroscience at the University of Waterloo.

And I'm actually jointly appointed between philosophy and engineering.

The philosophy allows me to consider general conceptual issues about how the mind works.

But of course, if I want to make claims about how the mind works, I have to understand also how the brain works.

And this is where engineering plays a critical role.

Engineering allows me to break down equations and very precise descriptions which we can test by building actual models.

1 model that we built recently is called the Spine model.

This model, Spine, has about 2 and a half million individual neurons that are simulated in it.

And the input to the model is an eye, and the output from the model is the movement of an arm.

So essentially it can see images of numbers and then do something like categorize them, in which case it would just draw the number that it sees, or it can actually try to reproduce the style of the number that it's looking at.

So for instance, if it sees a loopy 2, or 2 with a big loop on the bottom, it can actually reproduce that particular style of 2.

On the medical side, we all know that we have cognitive challenges that show up as we get older.

And we can try to address those challenges by simulating the aging process with these kinds of models.

Another potential area of impact is on artificial intelligence.

A lot of work in artificial intelligence attempts to build agents that are extremely good at 1 task, for instance, playing chess.

What's special about Spine is that it's quite good at many different tasks.

And this adds the additional challenge of trying to figure out how to coordinate the flow of information through different parts of the model.

Something that animals seem to be very lucky.

He's got a really cool book called How to Build a Brain.

And if you Google him, you can Google spawn, you can find a toolkit where you can kind of construct circuits that will approximate functions that you're interested in, connect them together, set certain properties that you would want at a low level, and build them up and actually work on tasks at the level of vision and robotic actuation.

As we move into architectures that are sitting above that biological level, I wanted to give you kind of an overall sense of what they're going to look like, what a prototypical architecture is going to look like.

So they're going to have some ability to have perception.

The modalities typically are more digital symbolic, but they will, depending on the architecture, be able to handle vision, audition, and various sensory inputs.

These will get represented in some sort of short-term memory, whatever the state's representation for the particular system is.

It's typical to have a representation of the knowledge of what tasks can be performed, when they should be performed, how they should be controlled.

And so these are typically both actions that take place internally that manage the internal state of the system and perform internal computations, but also about external actuation.

And external might be a digital system, a game AI, but it might also be some sort of robotic actuation in the real world.

There's typically some sort of mechanism by which to select from the available actions in a particular situation.

There's typically some way to augment this procedural information, which is to say, learn about new actions, possibly modify existing ones.

There's typically some semblance of what's called declarative memory.

So whereas procedural, at least in humans, if I asked you to describe how to ride a bike, you might be able to say get on the seat and pedal, but in terms of keeping your balance there, you'd have a pretty hard time describing it declaratively.

So that's kind of the procedural side, the implicit representation of knowledge, whereas declarative would include facts, geography, math, but it could also include experiences that the agent has had, a more episodic representation of declarative memory.

And they'll typically have some way of learning this information, augmenting it over time.

And then finally, some way of taking actions in the world.

And they'll all have some sort of cycle, which is perception comes in, knowledge that the agent has is brought to bear on that, an action is selected, knowledge that knows to condition on that action will act accordingly, both with internal processes as well as eventually to take action, and then rinse and repeat.

So when we talk about, in an AI system, an agent, in this context, that would be the fixed representation, which is whatever architecture we're talking about, plus set of knowledge that is typically specific to the task but might be more general.

So oftentimes these systems could incorporate a more general knowledge base of facts, of linguistic facts, of geographic facts.

Let's take Wikipedia and let's just stick it in the brain of the system.

There'll be more task in general, but then also whatever it is that you're doing right now, how should you proceed in that?

And then it's typical to see this processing cycle, and going back to the prior assumption, the idea is that These primitive cycles allow for the agent to be reactive to its environment.

So if new things come in that has react to, if the lion's sitting over there, I better run and maybe not do my calculus homework, right?

So as long as this cycle is going, I'm reactive, but at the same time, if multiple actions are taken over time, I'm able to get complex behavior over the long term.

So this is the ACT-R cognitive architecture.

It has many of the kind of core pieces that I talked about before.

Let's see if the, is the mouse, yes, mouse is useful up there.

A short-term memory is going to be these buffers that are on the outside.

The procedural memory is encoded as what I call production rules, or if-then rules.

If this is the state of my short-term memory, this is what I think should happen as a result.

You have a selection of the appropriate rule to fire and an execution.

You're seeing associated parts of the brain being represented here.

Cool thing that has been done over time in the ActR community is to make predictions about brain areas and then perform MRIs and gather that data and correlate that data.

So when you use the system you will get predictions about things like timing of operations, errors that will occur, probabilities that something is learned, but you also get predictions about, to the degree that they can, kind of brain areas that are going to light up.

And if you want to, that's actively being developed at Carnegie Mellon.

To the left is John Anderson, who developed this cognitive architecture 30-ish years ago.

See all Lex Fridman transcripts on Youtube

MIT AGI: Cognitive Architecture (Nate Derbinsky)