Recently I’ve been interested in François Chollet’s thinking on generality in artificial intelligence. Even more recently I’ve been reading this paper, On the Measure of Intelligence. I thought I’d blog some notes to sort of encourage myself to think more intelligently about it.
I’ll just awkwardly munge together my opinions and Chollet’s opinions in these notes. Go read his paper if something here intrigues you and you want to learn more.
What is “intelligence”?
Chollet thinks that most AI progress has been on “specific tasks” and to be really “intelligent” a system needs to be able to handle general tasks. AI has been successful at specific tasks, like playing chess, or recognizing handwritten digits. Arguably this is not “intelligence” because you aren’t testing the system’s ability to generalize, you aren’t testings its “ability to handle situations it hasn’t seen before”.
Even the Turing test is not really general enough in this view. The Turing test is a weird bar - personally I feel like I am administering a Turing test to new chat bots when I test out something like character.ai. But it’s like a software engineering interview. Just because I believe I can tell when something isn’t intelligent doesn’t mean that I think a program that fools other people is important to try for.
Chollet talks a lot about, what counts as generalization. This seems like a spectrum to me, there’s no clear line where more generalization is a lot better, generalizing does seem like a good feature for a system to have, okay.
IQ tests don’t seem all that great for measuring AI systems. It’s just too much of a diversion to go do the things that make you better at IQ tests. Or at least, why bother testing on the same exact thing that humans test on? There’s a PR sense where it convinces the public that AI is happening, but it doesn’t necessarily lead in the right direction.
Some interesting criticisms of games
OpenAI trained the AI “Five” to play Dota 2. At first it beat human players, but a few days later after the humans practiced they could beat it. It’s essentially a very slow learner by human standards if you measure by “gameplay time” rather than “clock time” - the AI needed 45,000 years of gametime, and even if in practice you can do that fast on a big cluster, it’s still showing that the underlying learning algorithm isn’t working as well as whatever humans do, because Dota pros spend more like a single digit number of years, max, learning Dota.
AlphaGo and AlphaZero haven’t been useful outside of board games. This is sort of true, but on the other hand IMO they provide a good demonstration of how you can build a larger system out of smaller parts, with different parts using different AI models. And this is basically how we are making progress on self-driving cars, or maybe the fact that self-driving cars are working slower than expected is an indicator that things aren’t working well enough here.
The AI systems can learn on hundreds of Atari games but still don’t play a new Atari game very well. A human expert game player, on the other hand, is usually pretty good on their first playthrough of a new game.
It’s interesting to think about chess historically… there were people described in this paper who assumed that solving chess would naturally require a huge array of mental skills since those are what people used. Of course in practice the alpha-beta algorithm is super useful for chess and not really useful for anything that isn’t like chess. Back in the 90’s when I was taking undergrad AI classes it did seem like people thought the chess and game work would be more relevant than it’s turned out to be.
I never thought of this before, but a parallel question to “what is intelligence” is “what is physical fitness”.
Obviously there is such a thing as physical fitness. You just know Lebron James is going to be better at juggling than me if we both practice for a day.
But if you think of physical fitness as “performance on an average task” then you could easily come up with an incompatible metric. What if you took an average position, anywhere in the solar system? You’d end up thinking that humans all had fitness zero because we couldn’t do anything in outer space. Lol.
Robots certainly don’t have general fitness if you think of it in a human sense. Even these industrial robots tend to be like, this robot installs the rear windows on a Mazda CX-9, and it does it way faster and more accurately than a human can. But it can’t juggle even as well as me, with days of practice. Much less as well as Lebron James can juggle.
Humans have some parts of intelligence hardcoded. Like dimensions. Humans have all this instinct for solving 2D geometry problems, and 3D geometry problems, and then you give the simplest of 4D problems and it’s just completely impossible.
Another funny example is shortest-path problems. Humans are pretty good at instinctively finding the shortest path that meets some conditions. But they are terrible at finding the longest path. For a computer it’s basically the same thing!
Chollet thinks it’s important to give an AI system a very similar set of priors to the set that humans have. I am not sure if I agree with this or not. Things like object persistence, small number manipulation. I dunno - personally I feel like the whole notion of “prior” is overrated because it’s mathematically convenient. I don’t really think the human mind works with priors. A prior is more like, an awkward way of badly summarizing someone’s belief system, hinting at some deep mathematical optimization system that isn’t really optimal in practice.
Chollet’s definition of intelligence:
The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty.
Personally, I neither agree nor disagree with this statement. It just doesn’t really bother me how we define intelligence. I guess that makes it funny that I am thinking so much about this paper that is entirely about defining intelligence!
What definitely seems true is that current AI systems require too much training data. Humans learn things with a lot less training data, and we don’t really have incredible priors that are solving the problem for us. The best example I think is multiple video games. You play 100 Steam first-person shooters, you’re going to pick up the 101st pretty quickly. Like you play it through once and you do pretty well on that playthrough.
There is not quite an analog for, study this small number of entities and learn what you can. Like meditating on it. How much can you train on a single image? The whole supervised learning thing doesn’t really make sense on it. You need some other… some other something.
I am pretty interested in video games and AI playing video games. I tried for a while to make a reinforcement learning agent play Slay The Spire. I fell completely short, mostly because it seemed like I would never get enough training data to make any of the RL techniques work.
What it “feels” like is that the AI doesn’t really understand things that a human picks up very quickly. Just the basic mechanics like, okay we have a deck of cards, every turn we are drawing five cards from that deck. An AI model isn’t learning that underlying logical structure. Deep learning can learn this but in some crazily inefficient way where it’s memorizing a ton of pairs of inputs and outputs. All that inefficiency I think just adds up to not letting you play the whole game.
Why is this interesting at all? I don’t know, maybe it’s like a curse. I have this instinct where I try to do something for a while, and then I end up thinking, hmm, I wonder if a computer could do this better. And then I think the same way when I’m doing, not some professional mundane task, but having fun, playing a game. I end up a bit bored when a computer can solve a game - like chess - but I think the games that computers currently can’t solve - like Magic: the Gathering or Slay the Spire - are pretty interesting. But if an AI did solve them, I think I would get bored by them. I guess that’s okay though.
Evaluating general intelligence
Okay, so there’s a whole lot of notation on how to evaluate intelligent agents. Basically it’s like, instead of having one task, you have a bunch of task-categories and what you really want is to pick up each new task-category quickly.
I am not sure what exactly the difference is between this and a more normal model. You can just think of a task as a more general thing. Like instead of “is this picture a cat or a dog” your task is “your new task: categorize cats and dogs. here’s n examples, categorize example n+1”. Yeah, you can add up the scores different and look at asymptotes of things, but I feel like it all adds up to just saying, we need to be measuring more abstract, more general tasks. And then you can have thetas with lots of subscripts, but, I just know that’s not quite going to stick in my head.
The Algorithmic Complexity of a string is the length of the shortest description of the string in a fixed universal language.
Like a Turing machine. Although in practice Turing machines are quite inconvenient, I’d rather go with some minimalist lisp here.
So literally should we just be looking for small Lisp programs that generate given outputs? I mean, that seems like a possible thing to try to code. The best ARC solution on Kaggle, as far as I can tell, is brute forcing combinations of some hard coded set of 100 or so functions.
There’s some point here that I don’t understand. Chollet doesn’t want to simply measure the goodness of a solution by how short it is. Instead, first there is a definition of “generalization difficulty”. But, the generalization difficulty refers to the shortest possible of all solutions that achieve at least a certain skill rate during evaluation. This seems… completely uncalculateable? If you could actually find the shortest program that generates a particular output that would probably violate some sort of diagonalization principle. I’m not sure whether I’m understanding this right, but if I am understanding it, then I don’t think I agree with it.
I like the more basic point of, just looking for small programs that generate a particular output is a very general task by its nature. If anything, the 2D grids of ARC are anthropocentric. A 2D grid isn’t all that natural. It’s just a really great fit for human eyeballs, terminal programs, and GPUs. A plain old list is more logical; you use lists all the time in your head like, I have this list of three errands to do before dinnertime. I’m never making a 2D grid in my head to go about everyday life.
“Program synthesis” sounds pretty cool. Chollet says his line of reasoning “encourages interest in program synthesis”. Cool.
I wonder what the simplest program synthesis task is. ARC is pretty simple but you can see from the top scoring results that you get a ton of value by hardcoding in 2D-specific transforms.
I know deep learning has trouble on just basic O(n) recursive problems like reversing a list or adding two numbers. The whole structure of deep learning doesn’t really set itself up to learn a pattern of doing one particular thing a number of times recursively. The gradients disappear, or by the “lottery ticket” hypothesis you just don’t have enough lottery tickets to make the whole system work in one click. You need some way to learn substructure without having the whole problem solved.
Oh, maybe this paper was written slightly before the ARC dataset was released? I guess I am thinking this whole thing through backwards. Ah well.
So Chollet has all these priors, these assumptions that he thinks are good ones for ARC.
- Object cohesion
- Object persistence
- Object influence via contact
- Shape upscaling or downscaling
- Drawing lines, connecting points, orthogonal projections
To me this is aethetically displeasing. Objects that influence each other by touching each other. Okay, the visual real world works that way, but 2D arrays generally don’t. But fine. It just makes me think that for ARC you want some logical core and then you want to boost it up by giving it some sort of hard coded 2D-grid-handling stuff.
There’s some interesting reading linked on program synthesis, I’ll have to check that out.
From working on radio telescope stuff recently I am starting to develop this theory, that GPU programming is going to overwhelm CPU programming in every scientific or numerical field, and the whole AI / deep learning boom is just a leading indicator of this, it’s happening there first because there’s a huge industry investment into Tensorflow and PyTorch and so on, but it’ll happen in other places soon. It’s way too hard to program CUDA stuff for most academic research groups to do it well. So maybe there’s something promising here.
More on program shortness
Chollet writes about a possible ARC approach.
Select top candidates among these programs based on a criterion such as program simplicity or program likelihood. Note that we do not expect that merely selecting the simplest possible program that works on training pairs will generalize well to test pairs.
That’s a little weird to me. Why would the shortest possible program not be the best way to describe something?
Eh, I’m probably getting too hung up on this. There might be some sort of “cheater programs” which are doing something like, hardcoding some exceptions, hardcoding in part of the output, and if your training data is really small like three examples, this cheating might end up being shorter. So you would just have a difference between “is your program aesthetically cheating” versus “is your program super short”. Seems like the sort of thing you can only really know in practice.
In practice, it seems like the biggest problem by far that we can’t actually find the shortest program that maps inputs to outputs. I’m not entirely sure about that but that’s my take from reading the top ARC solution writeup.
I’m interested to read more about program synthesis. I have a vague feeling that you should be able to do better with clever GPU stuff, and also by doing some simultaneous forward and backward searching where you look for overlap. (That’s how rewrite search in Lean to simplify a given mathematical expressions into a target works, for example. And in general automatic theorem proving often is more successful working backwards than forwards.)
But I don’t think that will quite be enough, you need some way to learn interesting things even when in “solution space” you are nowhere near the right answer. Hmm.