Why AI is Harder Than We Think (Machine Learning Research Paper Explained)
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#aiwinter #agi #embodiedcognition

The AI community has gone through regular cycles of AI Springs, where rapid progress gave rise to massive overconfidence, high funding, and overpromise, followed by these promises being unfulfilled, subsequently diving into periods of disenfranchisement and underfunding, called AI Winters. This paper examines the reasons for the repeated periods of overconfidence and identifies four fallacies that people make when they see rapid progress in AI.

OUTLINE:
0:00 - Intro & Overview
2:10 - AI Springs & AI Winters
5:40 - Is the current AI boom overhyped?
15:35 - Fallacy 1: Narrow Intelligence vs General Intelligence
19:40 - Fallacy 2: Hard for humans doesn't mean hard for computers
21:45 - Fallacy 3: How we call things matters
28:15 - Fallacy 4: Embodied Cognition
35:30 - Conclusion & Comments

Paper: https://arxiv.org/abs/2104.12871

My Video on Shortcut Learning: https://youtu.be/D-eg7k8YSfs

Abstract:
Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment ("AI spring") and periods of disappointment, loss of confidence, and reduced funding ("AI winter"). Even with today's seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.

Authors: Melanie Mitchell

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 Hello there, welcome back. Today we're going to look at why AI is harder than we think by Melanie Mitchell of the Santa Fe Institute. This paper argues that the cycles of AI spring and AI winter come about by people making too overconfident of predictions and then everything breaks down. And Mitchell here goes into why people make these overconfident predictions. She outlines four fallacies that researchers make and details them and gives some suggestions of what can be done better. So it's a bit of a different paper than we usually look at, but I'd still be interested in your opinions. Let me know in the comments what you think. Share this video out and of course subscribe if you're interested in machine learning content. All right, why AI is harder than we think. In the abstract here, Mitchell makes the case that since the 1950s when AI was sort of beginning to develop, there were repeating periods of what are called AI springs, which are periods of optimistic predictions and massive investment. And on the other hand, periods of disappointment, loss of confidence and reduced funding, which are called AI winters. And she says, even today, where AI has a number of breakthroughs, the development of long promised technologies, such as self driving cars, housekeeping robots and conversational companions has turned out to be much harder than many people expected. And she says one reason of this is our limited understanding, she says, of the nature and complexity of intelligence itself. And there are four fallacies she describes and common assumptions which can lead to these overconfident predictions. So if you know anything a little bit about the history of AI, you are aware that there is this cycle of these springs and winters. And this has been the case from the very beginning. And she outlines very clearly here that, you know, when, for example, the perceptron was invented, people thought, oh, we're going to do all of this extremely cool things. Here, Claude Shannon said, I confidently expect that within a matter of 10 to 15 years, something will emerge from the laboratory, which is not too far from the robots of science fiction fame. And Marvin Minsky forecasts that within a generation, the problems of creating artificial intelligence will be substantially solved. So this is due to the fact they saw real good progress in a very short amount of time and they just extrapolated that progress. And that did not turn out to be the case. And then, of course, there was a winter, a downturn in enthusiasm after all of these promises didn't materialize. Then again, in the 1980s, there were more more AI systems coming up. There was a upswing again and a disappointment again. And then in the 1990s and 2000s, finally, machine learning was introduced. By the way, the 1980s, the time of like expert systems. So people first people developed the other perceptron and thought that was the that was the best. And then expert systems, people thought if we just kind of develop these rules and have these rule solvers and sort of these rule searching algorithms, then we can build AI that did not turn out. And now in the current paradigm, we are in the machine learning paradigm where people develop machine learning algorithms and they think, OK, that's the way to go. So she makes the case here that also this time we might be in a period of overconfidence. She says, however, around 2000 deep learning in which brain inspired multilayer neural networks are trained from data emerged from this backwater from its backwater position and rose to superstar status in machine learning has been around since the 1970s. But recently, with big data sets and big compute, you know, we can we can scale up to a large number of unsolved challenges and solve them. So we can do speech recognition, machine translation, chatbot, image recognition, game playing, protein folding and many more things. And people, let's say, call this AI. Right. In essence, this is machine learning and machine learning and AI are almost synonymous nowadays. But we shouldn't forget that AI is a different thing than machine learning. It's just that many people today believe that you can use machine learning in order to achieve AI. And there was all at once a new round of optimism about the prospects of what has been variously called general, true or human level AI. And she goes through a little bit of what tech CEOs say like co-founder of Google DeepMind predicted that in 2008 that human level AI will be passed in the mid 2020s. I guess that's soon. Mark Zuckerberg declared that one of Facebook goals for the next five to 10 years is to basically get better than human level at all the primary human senses, vision, hearing, language and general cognition. Also, that would be very soon. These 10 years come to an end. So she says, in spite of all this optimism, it didn't take long for cracks to appear in deep learning's facade of intelligence. So already she's calling it a facade of intelligence and not intelligence itself. Turns out, like all AI systems of the past, deep learning can exhibit brittleness, unpredictable errors when facing situations that differ from the training data. She says these things are susceptible to shortcut learning. I've done a video on shortcut learning. If you're interested in that, it's a criticism of neural networks that is well summarized here by saying learning statistical associations in the training data. That allow the machine to produce correct answers, but sometimes for the wrong reasons. One should add the correct answers in the test data set. And this stems a lot from the fact of how these data sets are generated. So maybe there was this famous paper that where they tried to detect criminality from a face portrait. And they just happened to assemble their data set. They took all the criminal ones from their mugshots. But they took all the non-criminal ones from LinkedIn. And the model could just learn who is dressed well and who smiles and had nothing to do with actual criminality. And this shortcut learning is essentially where you say, look, you know, the way you construct the data set, you might there might be something in there where the model learns to give you the correct answer on your test set, because that's constructed equally. However, it doesn't really learn the true thing you want it to learn. Right. That is certainly, certainly exists. However, that is, I feel that is like a data set problem, not a problem with deep learning itself. Now, humans have that, right. So, by the way, in other words, these mechanisms don't learn the concepts we are trying to teach them, but rather they learn shortcuts to correct answers on the training set. And such shortcuts will not lead to good generalizations. So, if you think of humans, humans do that as well. Like if, you know, with branding and all, like if you ever bought a pair of Nike shoes, and you didn't exactly check their quality or evaluate them and so on, like maybe some of you do, but others are just like, oh, it's this brand that, you know, tells me something about its it's made like about the quality of the shoes or something like this. Like, you know, they're not the cheapest and you know, they're not the cheapest manufacturer, even though that might not be true. But you attach all of this to the brand symbol. And so essentially, humans perform shortcut learning all the time. But you know, point taken, these networks are brittle, they sometimes learn the wrong attack. They're of course, they're vulnerable to adversarial perturbations, though I don't think that's like a that's like a an exact criticism. It just means that the networks, they see the world in a little bit a different way than we do, right. And you can exploit that little difference in order to make them do weird things. But you know, you need to really target that it's not like that happens by itself. The I think the big challenge here is what what she says next. However, it seems clear from their non human like errors and vulnerability to adversarial perturbations that these systems are not actually understanding the data the process, at least not in the human sense of understand. It's still a matter of debate in the AI community, whether such understanding can be achieved by adding network layers, and more training data, or whether something more fundamental is missing. So a couple of comments right here, this understanding and she says this correctly, it's like in the human sense of understand and puts it in quotes. It's like, I don't think I've met yet anyone who can actually tell me what understanding means and or suggest a rigorous test for understanding. I think Wally Sabah came the closest to actually, you know, put saying look here, if this and this and this happens, then I claim it understands but most people just say something like, well, I'll, I'll know it when I see it, right. So, this seems a bit the sorry moving the bit of moving the goalpost of what it means to, to understand. But I agree, most people here wouldn't think that today's AI systems actually understand the data in the same way humans do for whatever definition of understand that is commonly used. The other point here is whether that understanding can be achieved by adding network layers and more training data or whether something more fundamental is missing. Now, you have to remember that, you know, human intelligence, however smart it might be, runs on hardware, right, it runs on neurons. And later, the authors here make the case for embodied cognition, but ultimately it runs on hardware, like it's in, it's an algorithm implemented in hardware and in very much all the same, it's all neurons. Sure, they're super specialized in some fashions, but ultimately you only have the chemistry that you have. And we know for a fact that intelligence arises from an algorithm on that hardware. So, yes, you can ask whether the current neural networks architectures are going to be sufficient, but I don't, I don't know what fundamental thing here might be missing like there might be better approaches, more efficient approaches and so on. But ultimately, the human brain is hardware too. But yeah, we could more purpose built, let's say network architectures if we know that something specific is missing. Maybe it's a different structure of network or a different type of algorithm on the hardware, we could build that in. Okay, so as we go on. She is going to into her four fallacies right now. And remember, so she claims that because these fallacies exist, people make overconfident predictions about the future of AI, and we shouldn't do that because if we make overconfident predictions, that means we won't meet our goals. And then we will, you know, the funding will dry up because we've set too high expectations, and then we'll go into another AI winter, which is a valid thing to say, though at some point, she also quotes Elon Musk here about you know, self driving cars and that they're not fully, fully self driving. I think that's, that's up here. Yeah, so, Elon Musk 2019 promised a year from now we'll have over a million cars with full self driving software and everything. And despite attempts to redefine full self driving into existence, none of these predictions have come true. So, so this reference here is to a link where the where Tesla I think towards the DMV so towards the regulators they say oh we're actually not doing fully self driving. So I think it's a bit, it's a bit, it's a bit, it's a bit weird to criticize, you know, Tesla on on that, like, I'm sure no other company ever has said has had a different tone and messaging when they do marketing than when they talk to the regularities like I'm sure that that never happens. Anywhere on the planet except with Tesla right. And that being said, Elon Musk does over promise all the time. On the other hand, he also achieves things that no one else achieves, I think it drives certain people mad that even though he's like over promising so much he still like achieves insane results, just not as insane as he promises, but I like that it makes people mad a bit. Okay, so first fallacy is narrow intelligence is on a continuum with general intelligence. So that's the fallacy the fallacy is thinking that if we develop something like deep blue. It was hailed as the first step of an AI revolution, or GPT three was called a step towards general intelligence. And the fallacy here is that we think that there's this this continuum, like, if we get better on individual tasks, we make progress towards general AI. The first step fallacy is the claim that ever since our first work on computer intelligence, we have been inching along a continuum, at the end of which is AI, so that any improvement in our programs, no matter how trivial counts as progress. It was like claiming that the first monkey that climbed a tree was making progress towards landing on the moon. This has connections to like Kenneth Stanley, as work on on exploration on reinforcement learning without, you know, goal, goal, undirected reinforcement learning, exploration based learning, where you can deceive yourself by just going towards a goal. Maybe you need an entirely different approach. And I guess the the fallacy here is to to say that whatever progress we make, you know, we're going to interpret that as our whatever successes we have, we're going to interpret that as, as a success, or as a step towards general AI. And, you know, honestly, I get it, I get it. Deep Blue is not general AI. And I get it that with like a min-max search tree, and a bunch of handcrafted rules, you cannot get to general AI. However, you know, the principles are still in use, like Deep Blue isn't so different from AlphaGo. And the concept that you need like an AI that goes to a certain depth as a look ahead, in order to achieve AI is not stupid, like it is. And the demonstration that such a systems can beat human at a previously unbeaten task is, I think, definitely progress towards general AI. I doubt we'll ever be able to do that. Towards general AI, I doubt we'll find a general AI that does not have something that at least resembles such a module. The same with GPT-3. Like, I'm fairly convinced that a general AI will have some some type of self supervised learning of language going on. And to not call GPT-3 a step into the direction of general intelligence. Like, sure, it, you know, all the criticism, it's just interpolating training data, yada, yada, yada. You can leverage that. But it's undeniable that that GPT-3 and the family of models there are tremendous progress, and I would argue progress towards general AI. I guess the more question is, how much of a progress is it? Like, is it halfway there? Or is it 1% there? In a way, the monkey climbing on the moon is a bit of progress going towards the moon because they, you know, they see the moon and they may want to go to the moon. Yeah. So I agree a little bit. I don't know. I don't know how, how, how valid that is, though. Fallacy two, easy things are easy and hard things are hard. So that's the fallacy where the correct, the corrected version would actually be easy things are hard and hard things are easy. And this is all about arguing that we assume that, you know, the hard problems for computers are also the hard problems for humans. So whenever we solve the hard problems for humans, we think, wow, that's a, you know, the computer must be super smart because only a super smart human would achieve such a thing. For example, researchers at Google DeepMind in talking about AlphaGo's triumph described the game of Go as one of the most challenging of domains. But correctly, this paper asks challenging for whom? For humans, perhaps. But as psychologist Gary Marcus pointed out, there are domains, including games, that while easy for humans are much more challenging than Go for AI systems. One example is charades. And this is a, it's a valid criticism that people, you know, fall, people fall victim to. How often have you seen someone interact with not even an AI system, but any, anything technical and asking like, why can't the stupid computer just, you know, do this? Like, how easy is that? You know, and you, you have maybe coded previously and you recognize it. It's not that easy, even though it seems super easy to a human. Yeah, so that's correct. It's a correct criticism. I do think deep learning has brought us a lot closer here, like in all of these things where humaness shines. I think deep learning, especially in the perception domain, has brought us a lot closer. Though this paper argues that there's still this kind of notion of common sense that isn't yet there for machines, which I also agree. Fallacy number three, the lure of wishful mnemonics. And this is a bit about how we call things. So the argument is, the argument is here. A major source of simple mindedness in AI programs is the use of mnemonics like understand or goal to refer to programs and data structures. If a researcher calls the main loop of his program understand, he is until proven innocent, merely begging the question. He may mislead a lot of people, most prominently himself. What he should do instead is refer to the main loop as G0034 and see how it can, how, if he can conceive itself or anyone else that G0034 implements at least some part of understanding. Many instructive example of wishful mnemonics by AI researchers come to mind once you see this point. So this is about how we talk about AI systems and the fact that we call things as we do. They give a more recent example here. Again, for deep, for some reason, deep mind is a lot. So IBM Watson is of course here too, deep mind as well. You know, granted, they do make a lot of claims about intelligence and their systems. So so Demis Hassabis says AlphaGo's goal is to be the best human players, not just mimic them. David Silver said, we can always ask AlphaGo how well it thinks it's doing during the game. It was only towards the end of the game that AlphaGo thought it would win. And the cursive words here are goal, thinks and thought it would win. And this, the fallacy here is that we use these words, and we sort of ascribe human tendencies, human wants, human needs to those systems. So the author here argues that AlphaGo doesn't have a goal per se, right? We just say this. AlphaGo doesn't think anything about itself and winning doesn't mean anything to it. Now, I agree that by calling things certain names, we implicitly, you know, we imply that there's something happening, we ascribe human-ness to these machines that might not exist. However, I don't necessarily agree that AlphaGo, for example, has no goal. Like, you know, what does it mean to have a goal? You know, how can you even measure that humans have a goal, right? Unless you ask someone like, what's your goal? But if you can't ask human, you observe their behavior, they seem to be acting, you know, to achieve a certain result, AlphaGo does the same. Like, I don't see why AlphaGo doesn't have a goal in the same way. At least you can't give me like a tangible definition of goal that does not include AlphaGo unless you explicitly carve it such that, you know, AlphaGo is excluded. But the same with, you know, how it thinks it's doing during the game. It was only towards the end that AlphaGo thought it would win. This is a bit more dicey, right? Because actually AlphaGo isn't even thinking how much it would win against in the current game. It's actually evaluating its value function against itself, right? So against the sort of the best opponent it knows. So it constantly underestimates its chances of winning because, you know, unless someone is better than AlphaGo. However, again, you know, of course, winning doesn't mean anything to AlphaGo. However, what does, you know, you also can't do this for a human like, hey, human, what does winning mean? Who knows, right? AlphaGo does have a concept of winning a game of getting positive reward. Like there is a clear state in its state space that relates to a winning game position. So again, it's a valid criticism that we shouldn't attribute human-ness to these machines. However, I do think a lot of a lot of these examples here are not as clear, right? The more clear ones are down here. Now, when we have data sets and tasks such as the Stanford question and answering data set, this is SQUAD short, or the the race reading comprehension data set, the general language understanding evaluation, right? Glue and its derivative super glue. These these are named, of course, if you if you work with them, you know fairly quickly that this is if it is question answering, it's a very limited set of question answering. Like it's a very specific kind of question answering. It's not the ability to answer questions. And you know that. But you have to give it some name, right? The the thought here is that to the public, it might seem that, you know, when when then the press writes things as Microsoft's AI has outperformed humans in natural language understanding, then that might be overly that might appear overly optimistic, which is, of course, true. However, the researchers I feel are only mildly to blame for this. You know, of course, there's marketing and research, but I would maybe, you know, like there's a high chance that in this article here, it was the journalist that massively up those statements to gather more clicks. And I agree, though, that to the public, then it's over promising. Maybe there's a politician that reads this right directs more funding because wow, and so on. And then you get this over promising and disappointment cycle. Then fallacy four is intelligence is all in the brain. And this is about embodied cognition and we should pay more attention to embodied cognition. So the fallacy is that intelligence is all in the brain. And she criticized here the information processing model of the mind, and essentially saying that there is lots of evidence that here the assumption that intelligence is on the brain has led to the speculation that to achieve human level AI, we simply need to scale up machines to match the brain's computing capacity and then develop the appropriate software for this brain matching hardware. Okay, so Jeff Hinton is there saying, you know, in the brain, we have X many connections, you know, once this is a hardware problem. However, there are these researchers in embodied cognition gaining steam since the mid 1970s, and they have a lot of evidence. Body cognition means that the representation of conceptual knowledge is dependent on the body. It's multimodal, not a modal symbolic or abstract. This theory suggests that our thoughts are grounded or inextricably associated with perception, action, emotion, and that our brain and body work together to have cognition. There is there's a lot of evidence that, you know, we work that way, our intelligence works that way. However, I so if if I have to leverage some criticism here, I would say maybe the maybe the author here also has a bit of a human ness fallacy in making this argument, right? Just because human intelligence has those properties doesn't mean that that's the only way to reach intelligence, even human level intelligence, or human like intelligence. Just because humans don't work without a body doesn't necessarily mean right that we can't build intelligence. Otherwise, I could also say, so the argument, I mean, there, there is, there are good arguments for this, don't get me wrong. But if you say something like, look, all the intelligence we ever see is body based, like human intelligence is the only intelligence we know. And that is intelligence that interacts with a body right in acts in the world and so on. I can also I can also hear it's not it's not at all clear. So instead, what we've learned from research and embodied cognition is that human intelligence seems to be a strongly integrated system with closely interconnected attributes, including emotions, desires, strong sense of selfhood and autonomy, and a common sense understanding of the world is not at all clear that these attributes can be separated. I want to leave out the common sense understanding of the world right now and and and focus on like the embodiment in the same vein, you can say, you know, all human intelligence we've ever encountered looks something like, you know, like, like, like this, there's a brain stem right here. There's the frontal thing I am terrible at drawing brains. This is a brain. Okay, brain. And all human intelligence looks like this. And you know, maybe there is the spine. And there are the, the nerves here. So this is a nervous system, human intelligence looks like this. Why don't you know, our computers, you know, must also look like this otherwise, because all the intelligence we ever see looks like this. Right. So since you know, since we don't have that, we need to build it. It's not it's not like I get it. We all this intelligence we see is a brain and the central nervous system and the body doesn't mean that we need it. Even it might be that, you know, the evolutionary pressure on humans, given their body made their intelligence super entangled and the development of intelligence dependent on having a body. But again, ultimately, we have to acknowledge that intelligence is something that's implemented in hardware. And it is the case that, you know, paraplegics have intelligence. I get it. Things like things like emotions and desires and so on. They're still there and, and they might play a role in the development of intelligence, but in, you know, paraplegics have intelligence, but what doesn't have intelligence is someone who's been to the guillotine, right, that there's no intelligence there in, you know, the, the body part. So there's, there's fairly good evidence, I'd say that intelligence exists independent of the body, because we can remove like every part of the body and still have intelligence except the brain. However, the body and embodiment might be necessary to efficiently develop intelligence and the same in my sense goes a bit for common sense. This common sense is a bit of, it's a bit of a mystery word that people use, I feel. So common sense, they mean like, oh, you know, the things that you just know, right. But I would say, you know, this, this is this common sense that people mean is the result of ginormous years of evolution, you know, built into your brain or at least making your brain extremely adapt to learning these things really quickly, right. That's what evolution has done. So in that way, it is very much a scale problem. It's very much a data plus scale problem. And maybe some, you know, clever neuromorphic algorithms or something like this, but it's not, it's not like, you know, we all we have to put in common sense, it seems like a scale problem. We could accelerate it by, you know, directly programming in common sense, but it's not the it's not like a qualitatively different thing, at least I feel. I do agree that embodiment is probably a good way to go in order to develop a general AI in order to push the next boundary of AI, especially in a kind of multi multimodal, multi sensory intelligence. And also reinforcement learning. So models that act in the world and observe their own actions, but we have that kind of to like, they're like a recommender system like YouTube or something they do, you know, the actions have influence on the system and so on. It just doesn't handle it super well for now. So that were the four fallacies. She lays out a bit of a future future plan here, especially, you know, what we call the future future plan here, especially, you know, focusing on, you know, we need to get these machines, a bit of common sense that's still missing, we attribute too much humanness to them. We need to go after maybe more after embodied cognition because that seems to be very promising. We shouldn't use wishful mnemonics. So we shouldn't call our things something like maybe something like attention, like we shouldn't maybe call our, our routines attention because, you know, it's not the same kind of attention that we call attention. We shouldn't assume that the same things are hard for humans as they are for machines. And finally, we where was it, we shouldn't assume that just any new solved task as a step towards general intelligence. Those are the four fallacies. And that was this paper, I invite you to read it in full. It's some has some good stuff in what I didn't read right now. Go check it out. Tell me what you think in the comments and I'll see you next time. Bye bye.
 Hello there, welcome back. Today we're going to look at why AI is harder than we think
 by Melanie Mitchell of the Santa Fe Institute. This paper argues that the cycles of AI spring and AI winter
 come about by people making too overconfident of predictions and then everything breaks down.
 And Mitchell here goes into why people make these overconfident predictions. She outlines four fallacies
 that researchers make and details them and gives some suggestions of what can be done better.
 So it's a bit of a different paper than we usually look at, but I'd still be interested in your opinions.
 Let me know in the comments what you think. Share this video out and of course subscribe if you're interested in machine learning content.
 All right, why AI is harder than we think. In the abstract here, Mitchell makes the case that since the 1950s
 when AI was sort of beginning to develop, there were repeating periods of what are called AI springs,
 which are periods of optimistic predictions and massive investment.
 And on the other hand, periods of disappointment, loss of confidence and reduced funding, which are called AI winters.
 And she says, even today, where AI has a number of breakthroughs, the development of long promised technologies,
 such as self driving cars, housekeeping robots and conversational companions has turned out to be much harder than many people expected.
 And she says one reason of this is our limited understanding, she says, of the nature and complexity of intelligence itself.
 And there are four fallacies she describes and common assumptions which can lead to these overconfident predictions.
 So if you know anything a little bit about the history of AI, you are aware that there is this cycle of these springs and winters.
 And this has been the case from the very beginning. And she outlines very clearly here that, you know, when, for example,
 the perceptron was invented, people thought, oh, we're going to do all of this extremely cool things.
 Here, Claude Shannon said, I confidently expect that within a matter of 10 to 15 years, something will emerge from the laboratory,
 which is not too far from the robots of science fiction fame.
 And Marvin Minsky forecasts that within a generation, the problems of creating artificial intelligence will be substantially solved.
 So this is due to the fact they saw real good progress in a very short amount of time and they just extrapolated that progress.
 And that did not turn out to be the case. And then, of course, there was a winter, a downturn in enthusiasm after all of these promises didn't materialize.
 Then again, in the 1980s, there were more more AI systems coming up.
 There was a upswing again and a disappointment again. And then in the 1990s and 2000s, finally, machine learning was introduced.
 By the way, the 1980s, the time of like expert systems.
 So people first people developed the other perceptron and thought that was the that was the best.
 And then expert systems, people thought if we just kind of develop these rules and have these rule solvers and sort of these rule searching algorithms,
 then we can build AI that did not turn out.
 And now in the current paradigm, we are in the machine learning paradigm where people develop machine learning algorithms and they think, OK, that's the way to go.
 So she makes the case here that also this time we might be in a period of overconfidence.
 She says, however, around 2000 deep learning in which brain inspired multilayer neural networks are trained from data emerged from this backwater from its backwater position
 and rose to superstar status in machine learning has been around since the 1970s.
 But recently, with big data sets and big compute, you know, we can we can scale up to a large number of unsolved challenges and solve them.
 So we can do speech recognition, machine translation, chatbot, image recognition, game playing, protein folding and many more things.
 And people, let's say, call this AI.
 Right. In essence, this is machine learning and machine learning and AI are almost synonymous nowadays.
 But we shouldn't forget that AI is a different thing than machine learning.
 It's just that many people today believe that you can use machine learning in order to achieve AI.
 And there was all at once a new round of optimism about the prospects of what has been variously called general, true or human level AI.
 And she goes through a little bit of what tech CEOs say like co-founder of Google DeepMind predicted that in 2008 that human level AI will be passed in the mid 2020s.
 I guess that's soon. Mark Zuckerberg declared that one of Facebook goals for the next five to 10 years is to basically get better than human level at all the primary human senses, vision, hearing, language and general cognition.
 Also, that would be very soon. These 10 years come to an end.
 So she says, in spite of all this optimism, it didn't take long for cracks to appear in deep learning's facade of intelligence.
 So already she's calling it a facade of intelligence and not intelligence itself.
 Turns out, like all AI systems of the past, deep learning can exhibit brittleness, unpredictable errors when facing situations that differ from the training data.
 She says these things are susceptible to shortcut learning.
 I've done a video on shortcut learning. If you're interested in that, it's a criticism of neural networks that is well summarized here by saying learning statistical associations in the training data.
 That allow the machine to produce correct answers, but sometimes for the wrong reasons.
 One should add the correct answers in the test data set.
 And this stems a lot from the fact of how these data sets are generated.
 So maybe there was this famous paper that where they tried to detect criminality from a face portrait.
 And they just happened to assemble their data set. They took all the criminal ones from their mugshots.
 But they took all the non-criminal ones from LinkedIn.
 And the model could just learn who is dressed well and who smiles and had nothing to do with actual criminality.
 And this shortcut learning is essentially where you say, look, you know, the way you construct the data set, you might there might be something in there where the model learns to give you the correct answer on your test set, because that's constructed equally.
 However, it doesn't really learn the true thing you want it to learn.
 Right. That is certainly, certainly exists. However, that is, I feel that is like a data set problem, not a problem with deep learning itself.
 Now, humans have that, right. So, by the way, in other words, these mechanisms don't learn the concepts we are trying to teach them, but rather they learn shortcuts to correct answers on the training set.
 And such shortcuts will not lead to good generalizations. So, if you think of humans, humans do that as well.
 Like if, you know, with branding and all, like if you ever bought a pair of Nike shoes, and you didn't exactly check their quality or evaluate them and so on, like maybe some of you do, but others are just like, oh, it's this brand that, you know, tells me something about its
 it's made like about the quality of the shoes or something like this. Like, you know, they're not the cheapest and you know, they're not the cheapest manufacturer, even though that might not be true.
 But you attach all of this to the brand symbol. And so essentially, humans perform shortcut learning all the time.
 But you know, point taken, these networks are brittle, they sometimes learn the wrong attack. They're of course, they're vulnerable to adversarial perturbations, though I don't think that's like a that's like a an exact criticism.
 It just means that the networks, they see the world in a little bit a different way than we do, right. And you can exploit that little difference in order to make them do weird things.
 But you know, you need to really target that it's not like that happens by itself.
 The I think the big challenge here is what what she says next.
 However, it seems clear from their non human like errors and vulnerability to adversarial perturbations that these systems are not actually understanding the data the process, at least not in the human sense of understand.
 It's still a matter of debate in the AI community, whether such understanding can be achieved by adding network layers, and more training data, or whether something more fundamental is missing.
 So a couple of comments right here, this understanding and she says this correctly, it's like in the human sense of understand and puts it in quotes. It's like, I don't think I've met yet anyone who can actually tell me what understanding means and or suggest a rigorous test for understanding.
 I think Wally Sabah came the closest to actually, you know, put saying look here, if this and this and this happens, then I claim it understands but most people just say something like, well, I'll, I'll know it when I see it, right.
 So, this seems a bit the sorry moving the bit of moving the goalpost of what it means to, to understand.
 But I agree, most people here wouldn't think that today's AI systems actually understand the data in the same way humans do for whatever definition of understand that is commonly used.
 The other point here is whether that understanding can be achieved by adding network layers and more training data or whether something more fundamental is missing. Now, you have to remember that, you know, human intelligence, however smart it might be, runs on hardware, right, it runs on neurons.
 And later, the authors here make the case for embodied cognition, but ultimately it runs on hardware, like it's in, it's an algorithm implemented in hardware and in very much all the same, it's all neurons.
 Sure, they're super specialized in some fashions, but ultimately you only have the chemistry that you have. And we know for a fact that intelligence arises from an algorithm on that hardware.
 So, yes, you can ask whether the current neural networks architectures are going to be sufficient, but I don't, I don't know what fundamental thing here might be missing like there might be better approaches, more efficient approaches and so on.
 But ultimately, the human brain is hardware too.
 But yeah, we could more purpose built, let's say network architectures if we know that something specific is missing.
 Maybe it's a different structure of network or a different type of algorithm on the hardware, we could build that in.
 Okay, so as we go on.
 She is going to into her four fallacies right now.
 And remember, so she claims that because these fallacies exist, people make overconfident predictions about the future of AI, and we shouldn't do that because if we make overconfident predictions, that means we won't meet our goals.
 And then we will, you know, the funding will dry up because we've set too high expectations, and then we'll go into another AI winter, which is a valid thing to say, though at some point, she also quotes Elon Musk here about you know, self driving cars and that they're not fully, fully self driving.
 I think that's, that's up here.
 Yeah, so, Elon Musk 2019 promised a year from now we'll have over a million cars with full self driving software and everything.
 And despite attempts to redefine full self driving into existence, none of these predictions have come true.
 So, so this reference here is to a link where the where Tesla I think towards the DMV so towards the regulators they say oh we're actually not doing fully self driving.
 So I think it's a bit, it's a bit, it's a bit, it's a bit weird to criticize, you know, Tesla on on that, like, I'm sure no other company ever has said has had a different tone and messaging when they do marketing than when they talk to the regularities like I'm sure that that never happens.
 Anywhere on the planet except with Tesla right. And that being said, Elon Musk does over promise all the time. On the other hand, he also achieves things that no one else achieves, I think it drives certain people mad that even though he's like over promising so much he still like achieves
 insane results, just not as insane as he promises, but I like that it makes people mad a bit.
 Okay, so first fallacy is narrow intelligence is on a continuum with general intelligence. So that's the fallacy the fallacy is thinking that if we develop something like deep blue. It was hailed as the first step of an AI revolution, or GPT three was called a step towards general intelligence.
 And the fallacy here is that we think that there's this this continuum, like, if we get better on individual tasks, we make progress towards general AI. The first step fallacy is the claim that ever since our first work on computer intelligence, we have been inching along a continuum, at the end of which is AI, so that any improvement in our programs, no matter how trivial counts as progress.
 It was like claiming that the first monkey that climbed a tree was making progress towards landing on the moon. This has connections to like Kenneth Stanley, as work on on exploration on reinforcement learning without, you know, goal, goal, undirected reinforcement learning, exploration based
 learning, where you can deceive yourself by just going towards a goal. Maybe you need an entirely different approach. And I guess the the fallacy here is to to say that whatever progress we make, you know, we're going to interpret that as our whatever successes we have, we're going to interpret that as, as a success, or as a step towards general AI. And, you know, honestly,
 I get it, I get it. Deep Blue is not general AI. And I get it that with like a min-max search tree, and a bunch of handcrafted rules, you cannot get to general AI. However, you know, the principles are still in use, like Deep Blue isn't so different from AlphaGo. And the concept that you need like an AI
 that goes to a certain depth as a look ahead, in order to achieve AI is not stupid, like it is. And the demonstration that such a systems can beat human at a previously unbeaten task is, I think, definitely progress towards general AI. I doubt we'll ever be able to do that.
 Towards general AI, I doubt we'll find a general AI that does not have something that at least resembles such a module. The same with GPT-3. Like, I'm fairly convinced that a general AI will have some some type of self supervised learning of language going on.
 And to not call GPT-3 a step into the direction of general intelligence. Like, sure, it, you know, all the criticism, it's just interpolating training data, yada, yada, yada. You can leverage that. But it's undeniable that that GPT-3 and the family of models there are tremendous progress, and I would argue progress towards general AI.
 I guess the more question is, how much of a progress is it? Like, is it halfway there? Or is it 1% there? In a way, the monkey climbing on the moon is a bit of progress going towards the moon because they, you know, they see the moon and they may want to go to the moon. Yeah.
 So I agree a little bit. I don't know. I don't know how, how, how valid that is, though.
 Fallacy two, easy things are easy and hard things are hard. So that's the fallacy where the correct, the corrected version would actually be easy things are hard and hard things are easy. And this is all about arguing that we assume that, you know, the hard problems for computers are also the hard problems for humans.
 So whenever we solve the hard problems for humans, we think, wow, that's a, you know, the computer must be super smart because only a super smart human would achieve such a thing.
 For example, researchers at Google DeepMind in talking about AlphaGo's triumph described the game of Go as one of the most challenging of domains.
 But correctly, this paper asks challenging for whom? For humans, perhaps. But as psychologist Gary Marcus pointed out, there are domains, including games, that while easy for humans are much more challenging than Go for AI systems.
 One example is charades. And this is a, it's a valid criticism that people, you know, fall, people fall victim to. How often have you seen someone interact with not even an AI system, but any, anything technical and asking like, why can't the stupid computer just, you know, do this?
 Like, how easy is that? You know, and you, you have maybe coded previously and you recognize it. It's not that easy, even though it seems super easy to a human.
 Yeah, so that's correct. It's a correct criticism. I do think deep learning has brought us a lot closer here, like in all of these things where humaness shines. I think deep learning, especially in the perception domain, has brought us a lot closer.
 Though this paper argues that there's still this kind of notion of common sense that isn't yet there for machines, which I also agree.
 Fallacy number three, the lure of wishful mnemonics. And this is a bit about how we call things. So the argument is, the argument is here. A major source of simple mindedness in AI programs is the use of mnemonics like understand or goal to refer to programs and data structures.
 If a researcher calls the main loop of his program understand, he is until proven innocent, merely begging the question.
 He may mislead a lot of people, most prominently himself. What he should do instead is refer to the main loop as G0034 and see how it can, how, if he can conceive itself or anyone else that G0034 implements at least some part of understanding.
 Many instructive example of wishful mnemonics by AI researchers come to mind once you see this point. So this is about how we talk about AI systems and the fact that we call things as we do.
 They give a more recent example here. Again, for deep, for some reason, deep mind is a lot. So IBM Watson is of course here too, deep mind as well. You know, granted, they do make a lot of claims about intelligence and their systems.
 So so Demis Hassabis says AlphaGo's goal is to be the best human players, not just mimic them. David Silver said, we can always ask AlphaGo how well it thinks it's doing during the game.
 It was only towards the end of the game that AlphaGo thought it would win. And the cursive words here are goal, thinks and thought it would win. And this, the fallacy here is that we use these words, and we sort of ascribe human tendencies, human wants, human needs to those systems.
 So the author here argues that AlphaGo doesn't have a goal per se, right? We just say this. AlphaGo doesn't think anything about itself and winning doesn't mean anything to it.
 Now, I agree that by calling things certain names, we implicitly, you know, we imply that there's something happening, we ascribe human-ness to these machines that might not exist.
 However, I don't necessarily agree that AlphaGo, for example, has no goal. Like, you know, what does it mean to have a goal? You know, how can you even measure that humans have a goal, right?
 Unless you ask someone like, what's your goal? But if you can't ask human, you observe their behavior, they seem to be acting, you know, to achieve a certain result, AlphaGo does the same. Like, I don't see why AlphaGo doesn't have a goal in the same way.
 At least you can't give me like a tangible definition of goal that does not include AlphaGo unless you explicitly carve it such that, you know, AlphaGo is excluded.
 But the same with, you know, how it thinks it's doing during the game. It was only towards the end that AlphaGo thought it would win.
 This is a bit more dicey, right? Because actually AlphaGo isn't even thinking how much it would win against in the current game. It's actually evaluating its value function against itself, right?
 So against the sort of the best opponent it knows. So it constantly underestimates its chances of winning because, you know, unless someone is better than AlphaGo.
 However, again, you know, of course, winning doesn't mean anything to AlphaGo.
 However, what does, you know, you also can't do this for a human like, hey, human, what does winning mean?
 Who knows, right? AlphaGo does have a concept of winning a game of getting positive reward.
 Like there is a clear state in its state space that relates to a winning game position. So again, it's a valid criticism that we shouldn't attribute human-ness to these machines.
 However, I do think a lot of a lot of these examples here are not as clear, right?
 The more clear ones are down here. Now, when we have data sets and tasks such as the Stanford question and answering data set, this is SQUAD short, or the the race reading comprehension data set, the general language understanding evaluation, right?
 Glue and its derivative super glue. These these are named, of course, if you if you work with them, you know fairly quickly that this is if it is question answering, it's a very limited set of question answering.
 Like it's a very specific kind of question answering. It's not the ability to answer questions. And you know that. But you have to give it some name, right?
 The the thought here is that to the public, it might seem that, you know, when when then the press writes things as Microsoft's AI has outperformed humans in natural language understanding, then that might be overly that might appear overly optimistic, which is, of course, true.
 However, the researchers I feel are only mildly to blame for this. You know, of course, there's marketing and research, but I would maybe, you know, like there's a high chance that in this article here, it was the journalist that massively up those statements to gather more clicks.
 And I agree, though, that to the public, then it's over promising. Maybe there's a politician that reads this right directs more funding because wow, and so on. And then you get this over promising and disappointment cycle.
 Then fallacy four is intelligence is all in the brain. And this is about embodied cognition and we should pay more attention to embodied cognition. So the fallacy is that intelligence is all in the brain.
 And she criticized here the information processing model of the mind, and essentially saying that there is lots of evidence that here the assumption that intelligence is on the brain has led to the speculation that to achieve human level AI, we simply need to scale up machines to match the brain's computing capacity and then develop the appropriate software for this brain matching hardware.
 Okay, so Jeff Hinton is there saying, you know, in the brain, we have X many connections, you know, once this is a hardware problem. However, there are these researchers in embodied cognition gaining steam since the mid 1970s, and they have a lot of evidence.
 Body cognition means that the representation of conceptual knowledge is dependent on the body. It's multimodal, not a modal symbolic or abstract. This theory suggests that our thoughts are grounded or inextricably associated with perception, action, emotion, and that our brain and body work together to have cognition.
 There is there's a lot of evidence that, you know, we work that way, our intelligence works that way. However, I so if if I have to leverage some criticism here, I would say maybe the maybe the author here also has a bit of a human ness fallacy in making this argument, right?
 Just because human intelligence has those properties doesn't mean that that's the only way to reach intelligence, even human level intelligence, or human like intelligence. Just because humans don't work without a body doesn't necessarily mean right that we can't build intelligence.
 Otherwise, I could also say, so the argument, I mean, there, there is, there are good arguments for this, don't get me wrong. But if you say something like, look, all the intelligence we ever see is body based, like human intelligence is the only intelligence we know.
 And that is intelligence that interacts with a body right in acts in the world and so on. I can also I can also hear it's not it's not at all clear. So instead, what we've learned from research and embodied cognition is that human intelligence seems to be a strongly integrated system with closely
 interconnected attributes, including emotions, desires, strong sense of selfhood and autonomy, and a common sense understanding of the world is not at all clear that these attributes can be separated.
 I want to leave out the common sense understanding of the world right now and and and focus on like the embodiment in the same vein, you can say, you know, all human intelligence we've ever encountered looks something like, you know, like, like, like this, there's a brain stem right here.
 There's the frontal thing I am terrible at drawing brains. This is a brain. Okay, brain. And all human intelligence looks like this. And you know, maybe there is the spine. And there are the, the nerves here. So this is a nervous system, human intelligence looks like this.
 Why don't you know, our computers, you know, must also look like this otherwise, because all the intelligence we ever see looks like this. Right. So since you know, since we don't have that, we need to build it. It's not it's not like I get it.
 We all this intelligence we see is a brain and the central nervous system and the body doesn't mean that we need it. Even it might be that, you know, the evolutionary pressure on humans, given their body made their intelligence super entangled and the development of intelligence dependent on having a body.
 But again, ultimately, we have to acknowledge that intelligence is something that's implemented in hardware. And it is the case that, you know, paraplegics have intelligence. I get it. Things like things like emotions and desires and so on.
 They're still there and, and they might play a role in the development of intelligence, but in, you know, paraplegics have intelligence, but what doesn't have intelligence is someone who's been to the guillotine, right, that there's no intelligence there in, you know, the, the body part.
 So there's, there's fairly good evidence, I'd say that intelligence exists independent of the body, because we can remove like every part of the body and still have intelligence except the brain.
 However, the body and embodiment might be necessary to efficiently develop intelligence and the same in my sense goes a bit for common sense. This common sense is a bit of, it's a bit of a mystery word that people use, I feel.
 So common sense, they mean like, oh, you know, the things that you just know, right. But I would say, you know, this, this is this common sense that people mean is the result of ginormous years of evolution, you know, built into your brain or at least making your brain extremely adapt to learning these things really quickly, right.
 That's what evolution has done. So in that way, it is very much a scale problem. It's very much a data plus scale problem. And maybe some, you know, clever neuromorphic algorithms or something like this, but it's not, it's not like, you know, we all we have to put in common sense, it seems like a scale problem.
 We could accelerate it by, you know, directly programming in common sense, but it's not the it's not like a qualitatively different thing, at least I feel. I do agree that embodiment is probably a good way to go in order to develop a general AI in order to push the next boundary of AI, especially in a kind of multi multimodal, multi sensory intelligence.
 And also reinforcement learning. So models that act in the world and observe their own actions, but we have that kind of to like, they're like a recommender system like YouTube or something they do, you know, the actions have influence on the system and so on.
 It just doesn't handle it super well for now. So that were the four fallacies. She lays out a bit of a future future plan here, especially, you know, what we call the
 future future plan here, especially, you know, focusing on, you know, we need to get these machines, a bit of common sense that's still missing, we attribute too much humanness to them.
 We need to go after maybe more after embodied cognition because that seems to be very promising.
 We shouldn't use wishful mnemonics. So we shouldn't call our things something like maybe something like attention, like we shouldn't maybe call our, our routines attention because, you know, it's not the same kind of attention that we call attention.
 We shouldn't assume that the same things are hard for humans as they are for machines. And finally, we where was it, we shouldn't assume that just any new solved task as a step towards general intelligence.
 Those are the four fallacies. And that was this paper, I invite you to read it in full. It's some has some good stuff in what I didn't read right now. Go check it out. Tell me what you think in the comments and I'll see you next time. Bye bye.