On the Measure of Intelligence by François Chollet - Part 1: Foundations (Paper Explained)
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper chollet keras google francois intelligence iq iq test deep neural networks prior skill performance measurement measure test number intelligent smart learning generalization ability experience humans evolution nature nurture psychometrics range adaptability arc kaggle difficulty entropy core knowledge objectness navigation contact agent goal
How does one measure the Intelligence of an AI? Is AlphaGo intelligent? How about GPT-3? In this landmark paper, Chollet proposes a solid measure of intelligence for AI that revolves around generalization, rather than skill.

OUTLINE:
0:00 - Intro
1:15 - The need for a measure of intelligence
3:35 - Intelligence as generalization ability
5:45 - Nature vs nurture
11:45 - Skill-based evaluation
18:30 - Generalization based evaluation
30:25 - Inspiration from psychometrics
36:30 - Conclusion

https://arxiv.org/abs/1911.01547

Abstract:
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.

Authors: François Chollet

Thumbnail: Photo by mohamed hassan

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 Hello there! Today we're going to look at On the Measure of Intelligence by François Cholet of Google. This is a bit of a special episode I would say because if you look at the paper it is first of all it's very long and then second of all it is a wall of text basically. Now it's very interesting text but if I were to go through this with you we basically just be kind of scrolling and reading along. So what I've done is I've basically read this and taken notes and I will attempt to just tell you what happens at least for the first part. So I intend for this to be a multi-part series because it's so long. So the first part as you can see here is context and history which is a little less boring than it sounds. The second part is going to be a new perspective where Cholet proposes his measure of intelligence and the third part is going to be about the benchmark, the ARC benchmark that is currently I believe running on Kaggle. So as it looks right now three parts and today we're going to dive into that first part. So here we go. He basically says that we need to define what intelligence means. We need an explicit goal to measure where if we think about AI like artificial intelligence what does intelligence mean? We need something where we can basically put a number or multiple numbers on it and says that's intelligent, that's not intelligent. What we have right now is just basically anecdotes. We all kind of feel what seems intelligent but we are not like sure and sometimes it's very misleading. He brings up the Turing test for example which is that you have a computer or a human behind a wall and on the other end sits a human and the human needs to kind of communicate without seeing what he or she communicates with and then determine whether or not on the other side of the wall is a human or a computer and if the computer could fool a human into kind of a 50-50 guess then the computer would be passing the Turing test and therefore intelligent. Now Shirley doesn't go right now into why that's not sufficient but he's basically saying this is not sufficient it's distracting and second of all it's basically just outsourcing the problems of defining intelligence to human right to this human right here who is fallible and noisy and you know doesn't all doesn't really know all you all you tell the human is basically like is this thing intelligent does this thing seem human to you it's also not clearly defined so we need some something more and Shirley says the definitions that exist today of intelligence are basically they they have implicit they are implicit definitions that are loaded with biases and biases basically from a human perspective on what intelligence is and if we want to really make progress in terms of in of measuring intelligence we need to point out these biases that are in these measures okay they has a range of quotes namely one here intelligence measures and agents ability to achieve goals in a wide range of environments that was I believe the conclusion of a an author that distilled lots of different definitions and try to distill them into one one sentence and that's it intelligence measures and agents ability to achieve goals in a wide range of environments so the crucial parts here is to ability to achieve goals so it must the agent must be you know doing something useful doing it like in reinforcement learning we'd say it must be getting higher rewards and the second part is in a wide range of environments so the the notion right here that we're going to encounter time and time again is basically an addition of skill and adaptivity so if you have it's not enough to have high skill you also need to be kind of adaptive to very very different environments to a range of environments and that's this this is the main issue that surely has with the current sort of definitions of intelligence and the current direction of the AI field because it mostly measures skill and not generalization or adaptivity now he he says in this thing in this sentence right here that you just saw there is an implicit sort of something is said implicitly namely that these these these skills this ability to achieve goals in this wide range of environments it must be acquired it must be learned these these new tasks these different environments the agent should basically learn to adapt to the different environments then and then the agent is intelligent it's not that intelligent when it is sort of pre-programmed to already handle these environments so he says that's that's sort of implicit in that statement and we're going to see how this is made explicit later he goes into basically there are two two different viewpoints on intelligence this old nature versus nurture debate and that refers to two things like crystallized intelligence versus skill acquisition intelligence so the evolutionary view would be that intelligence is sort of this set of static programs and here we simply kind of boil down these two views to their extremes right so don't I don't think any major evolutionary biologist is complete like is apps is that extreme right now but these were historical set of views that were held one of them was that intelligence is basically just it's all pre-group pre-programmed into you by evolution so you can you can solve this puzzle because during evolution you know your ancestors that could solve these puzzles were were survived you can plan your path through a a tree jungle because you know that was beneficiary to you and so evolution put that into your brain and therefore what results is AI is the science of making machines capable of performing tasks that would require intelligence if done by humans that's basically what Minsky says a quote I believe by Minsky at least Cholay says it's by Minsky or I misread where if you have this this set of view that that AI is basically just this set of static programs that means that if a human applies that set of programs to a task right and the human achieves 200 points it means if the if an AI comes along and achieves 201 points then it is intelligent because it has simply the better set of the better it has outperformed the static set of programs intelligence is this static set of programs and the AI has a better set of static set of programs so it's basically Minsky says if we know of a task that would require intelligence if done by a human then if that something that can solve that task is intelligent and this equates learning basically just to memorization if you if you ask a proponent of this viewpoint well what's what's learning like if everything's pre-programmed what we can still learn and they would say yeah but the learning is just you memorize situations and that particular ability is also pre-programmed into you the other extreme viewpoint is this tabula rasa viewpoint where it basically says you come into this world and your brain is a blank slate and everything you all of your abilities you basically must acquire through learning throughout your life so this is another extreme viewpoint and in terms of intelligence where that leads is following AI is the science and engineering of making machines do tasks they have never seen and have not been prepared for beforehand and that's a quote by McCarthy and Friedberg if we are ever to make a machine that will speak understand translate human languages solve mathematical problems with imagination practice a profession or direct an organization either we must reduce these activities to a science so exact that we can tell a machine precisely how to go about doing them or we must develop a machine that can do things without being told precisely how so this leads to more of of these notions right here that you can see here how the machines have not been prepared for a particular situation so if we make a machine that can do a task that it has not been prepared for we we know it's basically intelligent and again so if we make a machine that can do all of these things all of the things right here then either Friedberg says we must reduce these activities to exciting so basically we must program the solution in there already or we must develop a machine that can do things without being told precisely how and as you as you might realize this is much closer to the to the machine learning paradigm it's basically it's all about how much you say precisely because the extreme proponent of this thing would would basically recognize any sort of learning anything that you haven't seen before is an intelligent right if you if you can handle any new situation you're intelligent and show they is going to argue that that's also not really the case like we have to be a bit more graded about it but this is basically the machine learning approach it's it's we build machines that can do things without being told precisely how that they have not been prepared for beforehand like it can solve things that are not in the training data that's one interpretation and if you're a very strong proponent of this you would call that intelligent and she'll is going to argue that the truth of course is somewhere in the middle between these two viewpoints and therefore defining intelligence in either of these terms is going to lack in in expressivity and in usefulness so how do we evaluate AI and show legos through different levels here of AI evaluation so first of all he contrasts these these two things right here skill-based evaluation and generalization based evaluation so in skill-based evaluation you basically go for one given task so you evaluate a system on one given task one example here is for example the touring test and that's done by human review another example is where you have like a proof so you evaluate a system in by giving its optimality proof you can analyze it and you can say it is always correct at this particular task what you can also do is this pure competition so this is maybe what we see in sort of like chess so we let the bots play first humans and then we let them play other bots and we determine which one's the best and also the most familiar one benchmarks so this would be where your I don't know your image net net test set is right that's right here that's a skill-based evaluation that's one given task how well can you solve the image net test set without looking at it that's one task so the problem surely says with these this skill-based evaluation is sort of obvious it's like a single focus you can't like you are only good at this particular thing and that is one of the examples of this is the fact that the Kaggle models are usually the winning Kaggle models are usually useless outside of that particular data set because they're just so hyper optimized and hyper focused on winning that particular Kaggle competition so it's actually it's actually pretty strong science on how to set up a Kaggle competition such that you can then use the model the winning model afterwards for doing something actually useful no there are no conditions on how to arrive at a solution and there surely lets a bit of that that's basically his point that's gonna come in to the the measurement later into the math where he says you simply have to arrive at a solution in this skill-based evaluation it this skill-based evaluation usually doesn't care how you arrive there so the image net test set score doesn't care how you got the neural network or whatnot that you got it simply cares how many images do you classify correctly and this leads to what is called the AI effect which I didn't know it was called like this until recently but it's fairly obvious where people say people say people come up with a task that's that is intelligent so people used to say oh checkers the game of checkers it requires intelligence and then you build a machine to solve checkers because you can just I don't know search do like a bit of a smart tree search and you solve it and you tell them here's like a tree search that does checkers and they'll say well but that's not that's not really intelligent it's just like a tree search but but chess chess you can't possibly do the tree the full tree search so chess is intelligent and the then you build like a smarter tree search they build stockfish and they're like yeah but that's just you know that's just this machine thing and so the goal posts keep moving every time they come up with a task and you solve the tasks they'll just say wow that's not really intelligence this next task that's intelligence and it's easy to see that if you just do this skill-based evaluation you will never get there because it's always going to be now the next task the next task the next task it's overly anthropocentric it's overly based on how humans view the world and what is not left in here and again this this acquisition what is not in this definition is the fact that why do we think that someone that plays chess very well like why do we think Magnus Carlsen is smart why do we think someone like a go master is very intelligent and that's because we know that this person is human at least we believe there are doubts about is some of these grand masters but we believe that they are humans and therefore we know that they have only had whatever 20 30 years to learn this and they must eat regularly and they can only think so fast and it's it's hard to memorize things as a human so we know all of their constraints that went into learning this and we we basically know there is it's not like we are not aware of something like Neo has in the matrix where you can just upload the solution to chess into your brain we know what's required to achieve that level of success and we know the only way it this can be done is through general intelligence we know that there is this correlation in humans that if you are good at chess you must have this or you're very very likely to have this general problem-solving ability right that's a human centric view and that does not count for machines machines can take forever to calculate they can distill years and years of experience like thousands of years and this would also this would be the same case with this open AI dota 5 right dota 5 is exactly here alpha go is exactly here we only think they might be intelligent if a human does it because we know what's required for humans to get there again focus skill acquisition now you might be a board a little bit okay it's about skill acquisition but think about it it's it's not that easy to actually define this skill acquisition thing it without falling back into the exact same trap so it goes into say okay as opposed to this skill based we can measure generalization so what's the generalization generalization is the broad ability to handle tasks that differ from previous tasks so they they you have a task and it's different from previous tasks you generalize now there are two ways you can view this there is system centric generalization and that's basically if you take the strict definition here so this would be a machine learning system trains on the training set and then is evaluated on the test set it has never seen the test set before so it's generalizing right that's called system centric generalization but that's not really enough here because we also need to take into account the developer of the system so developer aware generalization means that you generalize two situations that are new to the system and to the developer so a developer of an image net model knows that it is going to be evaluated on the image net test set and that is that is in this category system centric because the developer knows however a broader generalization this developer aware generalization also takes into account that fact and it would say developer aware generalization is only when the system generalizes to something that is not known to developer that is new to even to the developer themselves they don't they haven't foreseen that so this accounts for prior knowledge of the developer it shall it defines different degrees of generalization largely along these lines so absence of generalization is when you have like an algorithm that you know you absolutely have built in that it works for every possible situation like a certain assorting algorithm that you have proven mathematically proven to work for all sequences of numbers no generalization everything has been foreseen then there is local generalization and this in machine learning we call this something like robustness this would be your test set robustness your a small distribution shift so the test set here comes from a known distribution so this is the notion of known unknowns you you have an idea of what can come at your system and you require basically you require a dense sampling of the input space usually machine learning training sets are very very densely sample that means there's a lot of data there that we can learn from so we have like lots and lots and lots and lots and lots of data and when the test point comes it is going to be like somewhere really in within between all of these training data points so we can infer from the surrounding training data points what the test data point is going to be like if there's a classification boundary right here we can sort of nearest neighbor it and there are arguments that deep networks are basically large nearest neighbor classifiers but that's a topic for another day and we are here basically we are here in machine learning right now we do local generalization we know our unknowns we know our test set as the opposition to this is broad generalization broad generalization is where you don't know what you don't know unknown unknowns you don't know what comes at test time and you can't pre-build sort of your expectations into the system this is more akin to something like level five autonomous driving where you build this car but you don't really know what kind of situations coming no no this is a this is a fuzzy definition right I mean you do sort of know what situations will come at the car you can certainly probabilistically make a statement about what so this is it's not a clear-cut definition and I think we're going to so in the math it seems clear-cut but when we get there I don't think it is that clear-cut honestly it's still kind of a an intuition thing what you categorize as local and broad and so on also here the Wozniak coffee cup example where it basically Wozniak says you should be able to build a robot that goes into any kitchen and gets you a cup of coffee and here you have known sorry unknown unknowns because you can't possibly foresee all possible kitchen arrangements there might be obstacles and so on do you know the coffee might there might be different coffee makers that you've never encountered before but I've long been saying that this is a bit of a trick right here because what what you can always do is you can construct a room a kitchen right and right here is the coffee machine so there's the how do we draw this there's the coffee machine right here one of these fancy Nespresso machines you put in a capsule here and here's the coffee machine okay but then you you build a wall around it and the wall has a door and the door the door will only open if you solve an IQ test right so or any sort of any surface so whatever you put whatever you put in that spot that's the level of generalization you you can achieve basically so you can always up the level of generalization to or you can put I don't know you can put the halting problem here right you can you can you can here you can say you only solve this door if you can whatever give me a proof of the ABC conjecture something like this so coffee cup example kind of kind of has some back doors in any case you sort of know what was the acme needs you should be able to go into a standard kitchen but the standard kitchens are still diverse enough you can't foresee all of them like I don't if any of you has this sort of kitchen that I'm talking about like mad respect all we will all get this robot and you'll you'll just have to wait for the next iteration ok then there's extreme extreme generalization is where you have kind of open-ended you you don't know what's going to come you don't even know the broad category of tasks that is going to come right broad here is still broad still refers to a broad category of related tasks so it is sort of a general ability and the extreme generalization just means you know whatever whatever comes you can solve it but it is different from universal universal generalization surely says is any conceivable task in the universe and that's pointless it's pointless because it's just too much there's this no free lunch theorem right plus what we actually want is we want human level intelligence and human level intelligence has this property of extreme generalization with extreme generalization we mean the scope see it's dependent on a scope we mean the scope of all human tasks of all tasks that humans could produce or could find useful could find themselves in or could pose of this system not all tasks that the universe could pose so that here you you don't even have the relation between tasks the relation between tasks are at most abstract so there maybe it's like the general ability of sorting things generally in in whatever fashion in and things whatever these things are with whatever properties or the general ability to communicate an idea or something like this and this in humans is called the g factor if you or it's related to but we're going to take a like surely it really goes after psychometrics here and really models its his framework after psychometrics for humans and the sort of achievement in psychometrics one of the achievements is this measure of the g factor and that's what we humans usually call intelligence he says note that humans have system centric and developer aware generalization though you know that one this this and count this contains the other one so why because we can handle situations that previous humans haven't experienced now I'm not I'm not sure he basically says humans have developer aware generalization because we can we can fare well in situations that no humans during evolution have experienced prior but okay let's let's have this abstractly let's say our developer is the evolution process you still have to ask can humans really solve things that the evolutionary process has not built into them in some sort I guess that refers back to the nature versus nurture like humans humans cannot you know multiply long floating point numbers it doesn't matter how I get without a pen and paper it doesn't matter how how much you learn or something like this there are some things that they just can't do but would want to do and I guess the evolutionary path simply didn't provide us for doing that kind of stuff we have a finite working memory and so on so I think the discussion here is still to be had if we really do have developer aware generalization if you consider our developer to be the evolutionary process but but we can forgive a little bit here so this is the general diagram that also emerges from kind of theories of intelligence from psychology where generally you have a general intelligence factor which is one factor this is quite remarkable in humans there is one general intelligence factor statistically all all these general intelligence tasks they broadly correlate and lead to one statistical factor it's not it's not obvious why that should be but turns out to be one factor and that distributes hierarchically into these things which are called broad abilities broad cognitive abilities and in shoeless framework that would correspond to broad generalization and then these are again hierarchically subdivided and sometimes as you can see your shared task specific skills okay and this in in shoeless framework would be local or no generalization so again he basically goes into psychometrics and specifically IQ tests for humans can they inform the measuring process the note and the thing to note here according to shalé is in an IQ test you want to measure these broad abilities you want to measure ultimately you want to measure G but if even if you measure different things in psychometrics you want to measure these broad abilities but these are like these are abstract concepts so what you're left with what you can only do is you can only measure really tasks okay and is this wrongly numbered or is this intentional I don't know you can only measure tasks but you somehow have to make an inference about the broad ability from measuring the tasks so that's the difficulty in psychometrics right you you you want to measure the abilities but you can only measure tasks the abilities are abstract concepts and the skill are the measurable things where you can put a number on it now you you can so what these IQ tests do they usually usually employ these broad battery of tests so you don't give the human just one tasks you give you give the human a lot of tasks you like okay complete this series which number comes next draw like rotate this in your head and so on but there there are so very human centric things like reading comprehension and so on but you do this broad battery of tests and you might think oh oh okay this is sort of like the Atari you know sweet where one reinforcement learning agent has to solve these whole bunch of Atari games or a super glue in NLP where one NLP system has to learn to do all these different NLP tasks you know there is entailment there is sentiment there is Boolean question answering and but this is according to chalet it's sort of not really it's not really the case that these are equivalent because it is a battery but it is known to the developer so the developer knows that the NLP system has to solve the super glue thing so the developer can first of all train the system until it reaches a good super glue score but then also it will have built in already the assumptions of the developer that you have to solve this so the second important thing about these battery of tests and IQ test is that they are unknown to the tested the tested cannot or ideally should not practice for them that's why people keep developing new and new IQ tests because we sort of know they all correlate first of all so they measure the same thing but also second because otherwise people if you just always do the same test people could practice it and then you would no longer measure the general ability you would only measure that one test by the way that's also why a lot of these you know brain brain exercise apps and so on they none of them really ups your intelligence you you you only get better at one app if you do that you don't you don't get smarter in general so if and and show they says there have been a number of attempts at making machines making AI solve human IQ tests right well the reasoning is the follows like oh okay humans develop IQ tests for humans and presumably those are no you're not known and so on but again the tasks broadly of IQ tests are known I guess really IQ tests work on humans because they only work on humans who don't really care like if someone really really really really cared they would you know research what kind of tests there are they would look at all the tests from history there's only so many tests you can come up with the new ones are going to be like variations on the old ones so you could technically if you really wanted you could like prepare super hard and that's exactly what developers are going to do they're basically going to look at all these tasks they're going to pre solve the problem and then they're going to program their you know pre-solved solution into an AI system so we can't just let AI systems solve human IQ tests what we need are tests that are reliable which means they're reproducible that are valid that means they really measure IQ or if they really measure artificial intelligence and not you know just task specific skill or something else they're standardized across the spectrum so they're standardized so everyone can do them in the same way by the way the current benchmarks are standardized that's the good part about them and they should be they should be free from bias which means they should not measure anything orthogonal to what they claim to measure and the example he gives is they should not measure reaction time which is also a big component in human IQ tests you also measure how fast the human is at the test and the machine obviously if you simply put more electrons through the cable it's going to run faster you or if you put more more GPUs there so in broad terms we what we should focus on is this new skill acquisition as I said from the beginning but it is not as easy as you might think right now and we're going to dive into the next episode and is going to be math heavy and that's going to be fun so I hope you enjoyed this kind of special episode maybe let me know if you like this style the paper doesn't have any pictures so you're just left with what I'm what I'm drawing yeah if you enjoyed this leave a like leave comments share it out and I'll see you next time bye bye
 Hello there! Today we're going to look at On the Measure of Intelligence by
 François Cholet of Google. This is a bit of a special episode I would say because
 if you look at the paper it is first of all it's very long and then second of
 all it is a wall of text basically. Now it's very interesting text but if I were
 to go through this with you we basically just be kind of scrolling and reading
 along. So what I've done is I've basically read this and taken notes and
 I will attempt to just tell you what happens at least for the first part. So
 I intend for this to be a multi-part series because it's so long. So the first
 part as you can see here is context and history which is a little less boring
 than it sounds. The second part is going to be a new perspective where Cholet
 proposes his measure of intelligence and the third part is going to be about the
 benchmark, the ARC benchmark that is currently I believe running on Kaggle. So
 as it looks right now three parts and today we're going to dive into
 that first part. So here we go. He basically says that we need to define
 what intelligence means. We need an explicit goal to measure where if we
 think about AI like artificial intelligence what does intelligence
 mean? We need something where we can basically put a number or multiple
 numbers on it and says that's intelligent, that's not intelligent. What we have
 right now is just basically anecdotes. We all kind of feel what seems
 intelligent but we are not like sure and sometimes it's very misleading. He brings
 up the Turing test for example which is that you have a computer or a human
 behind a wall and on the other end sits a human and the human needs to kind of
 communicate without seeing what he or she communicates with
 and then determine whether or not on the other side of the wall is a human or a
 computer and if the computer could fool a human into kind of a 50-50 guess then
 the computer would be passing the Turing test and therefore intelligent. Now
 Shirley doesn't go right now into why that's not sufficient but he's basically
 saying this is not sufficient it's distracting and second of all it's
 basically just outsourcing the problems of defining intelligence to human right
 to this human right here who is fallible and noisy and you know doesn't all
 doesn't really know all you all you tell the human is basically like is this
 thing intelligent does this thing seem human to you it's also not clearly
 defined so we need some something more and Shirley says the definitions that
 exist today of intelligence are basically they they have implicit they
 are implicit definitions that are loaded with biases and biases basically from a
 human perspective on what intelligence is and if we want to really make
 progress in terms of in of measuring intelligence we need to point out these
 biases that are in these measures okay they has a range of quotes namely one
 here intelligence measures and agents ability to achieve goals in a wide range
 of environments that was I believe the conclusion of a an author that distilled
 lots of different definitions and try to distill them into one one sentence and
 that's it intelligence measures and agents ability to achieve goals in a
 wide range of environments so the crucial parts here is to ability to
 achieve goals so it must the agent must be you know doing something useful doing
 it like in reinforcement learning we'd say it must be getting higher rewards
 and the second part is in a wide range of environments so the the notion right
 here that we're going to encounter time and time again is basically an addition
 of skill and adaptivity so if you have it's not enough to have high skill you
 also need to be kind of adaptive to very very different environments to a range of
 environments and that's this this is the main issue that surely has with the
 current sort of definitions of intelligence and the current direction of
 the AI field because it mostly measures skill and not generalization or
 adaptivity now he he says in this thing in this sentence right here that you
 just saw there is an implicit sort of something is said implicitly namely that
 these these these skills this ability to achieve goals in this wide range of
 environments it must be acquired it must be learned these these new tasks these
 different environments the agent should basically learn to adapt to the
 different environments then and then the agent is intelligent it's not that
 intelligent when it is sort of pre-programmed to already handle these
 environments so he says that's that's sort of implicit in that statement and
 we're going to see how this is made explicit later he goes into basically
 there are two two different viewpoints on intelligence this old nature versus
 nurture debate and that refers to two things like crystallized intelligence
 versus skill acquisition intelligence so the evolutionary view would be that
 intelligence is sort of this set of static programs and here we simply kind
 of boil down these two views to their extremes right so don't I don't think any
 major evolutionary biologist is complete like is apps is that extreme right now
 but these were historical set of views that were held one of them was that
 intelligence is basically just it's all pre-group pre-programmed into you by
 evolution so you can you can solve this puzzle because during evolution you know
 your ancestors that could solve these puzzles were were survived you can plan
 your path through a a tree jungle because you know that was beneficiary to
 you and so evolution put that into your brain and therefore what results is AI
 is the science of making machines capable of performing tasks that would
 require intelligence if done by humans that's basically what Minsky says a
 quote I believe by Minsky at least Cholay says it's by Minsky or I misread
 where if you have this this set of view that that AI is basically just this set
 of static programs that means that if a human applies that set of programs to a
 task right and the human achieves 200 points it means if the if an AI comes
 along and achieves 201 points then it is intelligent because it has simply the
 better set of the better it has outperformed the static set of programs
 intelligence is this static set of programs and the AI has a better set of
 static set of programs so it's basically Minsky says if we know of a task that
 would require intelligence if done by a human then if that something that can
 solve that task is intelligent and this equates learning basically just to
 memorization if you if you ask a proponent of this viewpoint well what's
 what's learning like if everything's pre-programmed what we can still learn
 and they would say yeah but the learning is just you memorize situations and that
 particular ability is also pre-programmed into you the other extreme
 viewpoint is this tabula rasa viewpoint where it basically says you come into
 this world and your brain is a blank slate and everything you all of your
 abilities you basically must acquire through learning throughout your life so
 this is another extreme viewpoint and in terms of intelligence where that leads
 is following AI is the science and engineering of making machines do tasks
 they have never seen and have not been prepared for beforehand and that's a
 quote by McCarthy and Friedberg if we are ever to make a machine that will
 speak understand translate human languages solve mathematical problems
 with imagination practice a profession or direct an organization either we must
 reduce these activities to a science so exact that we can tell a machine
 precisely how to go about doing them or we must develop a machine that can do
 things without being told precisely how so this leads to more of of these notions
 right here that you can see here how the machines have not been prepared for a
 particular situation so if we make a machine that can do a task that it has
 not been prepared for we we know it's basically intelligent and again so if we
 make a machine that can do all of these things all of the things right here then
 either Friedberg says we must reduce these activities to exciting so
 basically we must program the solution in there already or we must develop a
 machine that can do things without being told precisely how and as you as you
 might realize this is much closer to the to the machine learning paradigm it's
 basically it's all about how much you say precisely because the extreme
 proponent of this thing would would basically recognize any sort of learning
 anything that you haven't seen before is an intelligent right if you if you can
 handle any new situation you're intelligent and show they is going to
 argue that that's also not really the case like we have to be a bit more
 graded about it but this is basically the machine learning approach it's it's
 we build machines that can do things without being told precisely how that
 they have not been prepared for beforehand like it can solve things that
 are not in the training data that's one interpretation and if you're a very
 strong proponent of this you would call that intelligent and she'll is going to
 argue that the truth of course is somewhere in the middle between these
 two viewpoints and therefore defining intelligence in either of these terms is
 going to lack in in expressivity and in usefulness so how do we evaluate AI and
 show legos through different levels here of AI evaluation so first of all he
 contrasts these these two things right here skill-based evaluation and
 generalization based evaluation so in skill-based evaluation you basically go
 for one given task so you evaluate a system on one given task one example
 here is for example the touring test and that's done by human review another
 example is where you have like a proof so you evaluate a system in by giving
 its optimality proof you can analyze it and you can say it is always correct at
 this particular task what you can also do is this pure competition so this is
 maybe what we see in sort of like chess so we let the bots play first humans and
 then we let them play other bots and we determine which one's the best and also
 the most familiar one benchmarks so this would be where your I don't know your
 image net net test set is right that's right here that's a skill-based
 evaluation that's one given task how well can you solve the image net test
 set without looking at it that's one task so the problem surely says with
 these this skill-based evaluation is sort of obvious it's like a single focus
 you can't like you are only good at this particular thing and that is one of the
 examples of this is the fact that the Kaggle models are usually the winning
 Kaggle models are usually useless outside of that particular data set
 because they're just so hyper optimized and hyper focused on winning that
 particular Kaggle competition so it's actually it's actually pretty strong
 science on how to set up a Kaggle competition such that you can then use
 the model the winning model afterwards for doing something actually useful no
 there are no conditions on how to arrive at a solution and there surely lets a
 bit of that that's basically his point that's gonna come in to the the
 measurement later into the math where he says you simply have to arrive at a
 solution in this skill-based evaluation it this skill-based evaluation usually
 doesn't care how you arrive there so the image net test set score doesn't care
 how you got the neural network or whatnot that you got it simply cares how
 many images do you classify correctly and this leads to what is called the AI
 effect which I didn't know it was called like this until recently but it's fairly
 obvious where people say people say people come up with a task that's that
 is intelligent so people used to say oh checkers the game of checkers it
 requires intelligence and then you build a machine to solve checkers because you
 can just I don't know search do like a bit of a smart tree search and you solve
 it and you tell them here's like a tree search that does checkers and they'll
 say well but that's not that's not really intelligent it's just like a
 tree search but but chess chess you can't possibly do the tree the full
 tree search so chess is intelligent and the then you build like a smarter tree
 search they build stockfish and they're like yeah but that's just you know
 that's just this machine thing and so the goal posts keep moving every time
 they come up with a task and you solve the tasks they'll just say wow that's
 not really intelligence this next task that's intelligence and it's easy to see
 that if you just do this skill-based evaluation you will never get there
 because it's always going to be now the next task the next task the next task
 it's overly anthropocentric it's overly based on how humans view the world and
 what is not left in here and again this this acquisition what is not in this
 definition is the fact that why do we think that someone that plays chess very
 well like why do we think Magnus Carlsen is smart why do we think someone like a
 go master is very intelligent and that's because we know that this person is
 human at least we believe there are doubts about is some of these grand
 masters but we believe that they are humans and therefore we know that they
 have only had whatever 20 30 years to learn this and they must eat regularly
 and they can only think so fast and it's it's hard to memorize things as a human
 so we know all of their constraints that went into learning this and we we
 basically know there is it's not like we are not aware of something like Neo has
 in the matrix where you can just upload the solution to chess into your brain we
 know what's required to achieve that level of success and we know the only
 way it this can be done is through general intelligence we know that there
 is this correlation in humans that if you are good at chess you must have this
 or you're very very likely to have this general problem-solving ability right
 that's a human centric view and that does not count for machines machines can
 take forever to calculate they can distill years and years of experience
 like thousands of years and this would also this would be the same case with
 this open AI dota 5 right dota 5 is exactly here alpha go is exactly here we
 only think they might be intelligent if a human does it because we know what's
 required for humans to get there again focus skill acquisition now you might be
 a board a little bit okay it's about skill acquisition but think about it
 it's it's not that easy to actually define this skill acquisition thing it
 without falling back into the exact same trap so it goes into say okay as opposed
 to this skill based we can measure generalization so what's the
 generalization generalization is the broad ability to handle tasks that
 differ from previous tasks so they they you have a task and it's different from
 previous tasks you generalize now there are two ways you can view this there is
 system centric generalization and that's basically if you take the strict
 definition here so this would be a machine learning system trains on the
 training set and then is evaluated on the test set it has never seen the test
 set before so it's generalizing right that's called system centric
 generalization but that's not really enough here because we also need to take
 into account the developer of the system so developer aware generalization means
 that you generalize two situations that are new to the system and to the
 developer so a developer of an image net model knows that it is going to be
 evaluated on the image net test set and that is that is in this category system
 centric because the developer knows however a broader generalization this
 developer aware generalization also takes into account that fact and it
 would say developer aware generalization is only when the system generalizes to
 something that is not known to developer that is new to even to the developer
 themselves they don't they haven't foreseen that so this accounts for prior
 knowledge of the developer it shall it defines different degrees of
 generalization largely along these lines so absence of generalization is when you
 have like an algorithm that you know you absolutely have built in that it works
 for every possible situation like a certain assorting algorithm that you
 have proven mathematically proven to work for all sequences of numbers no
 generalization everything has been foreseen then there is local
 generalization and this in machine learning we call this something like
 robustness this would be your test set robustness your a small distribution
 shift so the test set here comes from a known distribution so this is the notion
 of known unknowns you you have an idea of what can come at your system and you
 require basically you require a dense sampling of the input space usually
 machine learning training sets are very very densely sample that means there's a
 lot of data there that we can learn from so we have like lots and lots and lots
 and lots and lots of data and when the test point comes it is going to be like
 somewhere really in within between all of these training data points so we can
 infer from the surrounding training data points what the test data point is going
 to be like if there's a classification boundary right here we can sort of
 nearest neighbor it and there are arguments that deep networks are
 basically large nearest neighbor classifiers but that's a topic for another day and we
 are here basically we are here in machine learning right now we do local
 generalization we know our unknowns we know our test set as the opposition to
 this is broad generalization broad generalization is where you don't know
 what you don't know unknown unknowns you don't know what comes at test time and
 you can't pre-build sort of your expectations into the system this is
 more akin to something like level five autonomous driving where you build this
 car but you don't really know what kind of situations coming no no this is a
 this is a fuzzy definition right I mean you do sort of know what situations
 will come at the car you can certainly probabilistically make a statement
 about what so this is it's not a clear-cut definition and I think we're
 going to so in the math it seems clear-cut but when we get there I don't
 think it is that clear-cut honestly it's still kind of a an intuition thing what
 you categorize as local and broad and so on also here the Wozniak coffee cup
 example where it basically Wozniak says you should be able to build a robot that
 goes into any kitchen and gets you a cup of coffee and here you have known sorry
 unknown unknowns because you can't possibly foresee all possible kitchen
 arrangements there might be obstacles and so on do you know the coffee might
 there might be different coffee makers that you've never encountered before but
 I've long been saying that this is a bit of a trick right here because what what
 you can always do is you can construct a room a kitchen right and right here is
 the coffee machine so there's the how do we draw this there's the coffee machine
 right here one of these fancy Nespresso machines you put in a capsule here and
 here's the coffee machine okay but then you you build a wall around it and the
 wall has a door and the door the door will only open if you solve an IQ test
 right so or any sort of any surface so whatever you put whatever you put in
 that spot that's the level of generalization you you can achieve
 basically so you can always up the level of generalization to or you can put I
 don't know you can put the halting problem here right you can you can you
 can here you can say you only solve this door if you can whatever give me a proof
 of the ABC conjecture something like this so coffee cup example kind of kind
 of has some back doors in any case you sort of know what was the acme needs you
 should be able to go into a standard kitchen but the standard kitchens are
 still diverse enough you can't foresee all of them like I don't if any of you
 has this sort of kitchen that I'm talking about like mad respect all we
 will all get this robot and you'll you'll just have to wait for the next
 iteration ok then there's extreme extreme generalization is where you have
 kind of open-ended you you don't know what's going to come you don't even
 know the broad category of tasks that is going to come right broad here is still
 broad still refers to a broad category of related tasks so it is sort of a
 general ability and the extreme generalization just means you know
 whatever whatever comes you can solve it but it is different from universal
 universal generalization surely says is any conceivable task in the universe and
 that's pointless it's pointless because it's just too much there's this no
 free lunch theorem right plus what we actually want is we want human level
 intelligence and human level intelligence has this property of extreme
 generalization with extreme generalization we mean the scope see
 it's dependent on a scope we mean the scope of all human tasks of all tasks
 that humans could produce or could find useful could find themselves in or could
 pose of this system not all tasks that the universe could pose so that here you
 you don't even have the relation between tasks the relation between tasks are at
 most abstract so there maybe it's like the general ability of sorting things
 generally in in whatever fashion in and things whatever these things are with
 whatever properties or the general ability to communicate an idea or
 something like this and this in humans is called the g factor if you or it's
 related to but we're going to take a like surely it really goes after
 psychometrics here and really models its his framework after psychometrics for
 humans and the sort of achievement in psychometrics one of the achievements is
 this measure of the g factor and that's what we humans usually call intelligence
 he says note that humans have system centric and developer aware
 generalization though you know that one this this and count this contains the
 other one so why because we can handle situations that previous humans haven't
 experienced now I'm not I'm not sure he basically says humans have developer
 aware generalization because we can we can fare well in situations that no
 humans during evolution have experienced prior but okay let's let's have this
 abstractly let's say our developer is the evolution process you still have to
 ask can humans really solve things that the evolutionary process has not built
 into them in some sort I guess that refers back to the nature versus nurture
 like humans humans cannot you know multiply long floating point numbers it
 doesn't matter how I get without a pen and paper it doesn't matter how how
 much you learn or something like this there are some things that they just
 can't do but would want to do and I guess the evolutionary path simply didn't
 provide us for doing that kind of stuff we have a finite working memory and so
 on so I think the discussion here is still to be had if we really do have
 developer aware generalization if you consider our developer to be the
 evolutionary process but but we can forgive a little bit here so this is the
 general diagram that also emerges from kind of theories of intelligence from
 psychology where generally you have a general intelligence factor which is one
 factor this is quite remarkable in humans there is one general intelligence
 factor statistically all all these general intelligence tasks they broadly
 correlate and lead to one statistical factor it's not it's not obvious why
 that should be but turns out to be one factor and that distributes hierarchically
 into these things which are called broad abilities broad cognitive abilities and
 in shoeless framework that would correspond to broad generalization and
 then these are again hierarchically subdivided and sometimes as you can see
 your shared task specific skills okay and this in in shoeless framework would
 be local or no generalization so again he basically goes into psychometrics
 and specifically IQ tests for humans can they inform the measuring process the
 note and the thing to note here according to shalé is in an IQ test you
 want to measure these broad abilities you want to measure ultimately you want
 to measure G but if even if you measure different things in psychometrics you
 want to measure these broad abilities but these are like these are abstract
 concepts so what you're left with what you can only do is you can only measure
 really tasks okay and is this wrongly numbered or is this intentional I don't
 know you can only measure tasks but you somehow have to make an inference about
 the broad ability from measuring the tasks so that's the difficulty in
 psychometrics right you you you want to measure the abilities but you can only
 measure tasks the abilities are abstract concepts and the skill are the
 measurable things where you can put a number on it now you you can so what
 these IQ tests do they usually usually employ these broad battery of tests so
 you don't give the human just one tasks you give you give the human a lot of
 tasks you like okay complete this series which number comes next draw like rotate
 this in your head and so on but there there are so very human centric things
 like reading comprehension and so on but you do this broad battery of tests and
 you might think oh oh okay this is sort of like the Atari you know sweet where
 one reinforcement learning agent has to solve these whole bunch of Atari games
 or a super glue in NLP where one NLP system has to learn to do all these
 different NLP tasks you know there is entailment there is sentiment there is
 Boolean question answering and but this is according to chalet it's sort of not
 really it's not really the case that these are equivalent because it is a
 battery but it is known to the developer so the developer knows that the NLP
 system has to solve the super glue thing so the developer can first of all train
 the system until it reaches a good super glue score but then also it will have
 built in already the assumptions of the developer that you have to solve this so
 the second important thing about these battery of tests and IQ test is that
 they are unknown to the tested the tested cannot or ideally should not
 practice for them that's why people keep developing new and new IQ tests because
 we sort of know they all correlate first of all so they measure the same thing
 but also second because otherwise people if you just always do the same test
 people could practice it and then you would no longer measure the general
 ability you would only measure that one test by the way that's also why a lot of
 these you know brain brain exercise apps and so on they none of them really ups
 your intelligence you you you only get better at one app if you do that you
 don't you don't get smarter in general so if and and show they says there have
 been a number of attempts at making machines making AI solve human IQ tests
 right well the reasoning is the follows like oh okay humans develop IQ tests for
 humans and presumably those are no you're not known and so on but again the
 tasks broadly of IQ tests are known I guess really IQ tests work on humans
 because they only work on humans who don't really care like if someone really
 really really really cared they would you know research what kind of tests
 there are they would look at all the tests from history there's only so many
 tests you can come up with the new ones are going to be like variations on the
 old ones so you could technically if you really wanted you could like prepare
 super hard and that's exactly what developers are going to do they're
 basically going to look at all these tasks they're going to pre solve the
 problem and then they're going to program their you know pre-solved
 solution into an AI system so we can't just let AI systems solve human IQ tests
 what we need are tests that are reliable which means they're reproducible that
 are valid that means they really measure IQ or if they really measure
 artificial intelligence and not you know just task specific skill or something
 else they're standardized across the spectrum so they're standardized so
 everyone can do them in the same way by the way the current benchmarks are
 standardized that's the good part about them and they should be they should be
 free from bias which means they should not measure anything orthogonal to what
 they claim to measure and the example he gives is they should not measure
 reaction time which is also a big component in human IQ tests you also
 measure how fast the human is at the test and the machine obviously if you
 simply put more electrons through the cable it's going to run faster you or if
 you put more more GPUs there so in broad terms we what we should focus on is this
 new skill acquisition as I said from the beginning but it is not as easy as you
 might think right now and we're going to dive into the next episode and is going
 to be math heavy and that's going to be fun so I hope you enjoyed this kind of
 special episode maybe let me know if you like this style the paper doesn't have
 any pictures so you're just left with what I'm what I'm drawing yeah if you
 enjoyed this leave a like leave comments share it out and I'll see you next time
 bye bye