YouSaid · the spoken record

François Chollet

lines on the record: 161
first: 2020-08-31
most recent: 2020-08-31
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“So I don't think driving is that much of a test influence because, again, there is no task for which skill at that task demonstrates intelligence unless it's a kind of meta-task that involves acquiring new skills. So I don't think, I think you can actually solve driving without having...”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's an example of driving. I mean, sure, you've seen a lot of cars in your life before you learn to drive. But let's say you've learned to drive in Silicon Valley and now you rent a car in Tokyo. Well, now everyone is driving on the other side of the road and the signs are different and the roads are more narrow and so on. So it's a very, very different environment. And a smart human, even an average human, should be able to just zero shot it to just be operational in this very different environment. Right away, despite having had no contact is the novel complexity that is contained in this environment, right? And that is novel complexity. It's not just interpolation over the situations that you've encountered previously, like learning to drive in the US, right?”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“But it is not a good medium for any sort of explicit reasoning. And in AI systems today, strong generalization tends to come from explicit models tend to come from abstractions in the human mind that are encoded in program form by human engineer, right? These are the abstractions you can actually generalize, not the sort of weak abstraction that is learned by a neural”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I'll tell you, you know, the thing is the amount of training data you would need to anticipate for pretty much every possible situation you encounter in the real world is such that it's not entirely unrealistic to think that at some point in the future we'll develop a system that's string on enough data, especially provided that we can simulate a lot of that data we don't necessarily need actual cars on the road for everything. But it's a massive effort. And it turns out you can create a system that's much more adaptive, that can generalize much better if you just add explicit models of the surroundings of the car. And if you use deep learning for what it's good at, which is to provide perceptive information. So in general, deep learning is a way to encode perception and a way to encode intuition.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“In fact, that's modeled breaks down Even very tasks that look relatively simple from a distance, like L5 self-driving, for instance, Google had a paper a couple of years back showing that something like 30 million different road situations were actually completely insufficient to train driving model. It wasn't even L2, right? And that's a lot of data. That's a lot more data than the 20 or 30 hours of driving that a human needs to learn to drive given the knowledge they've already accumulated.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Is why a system that's You tell that the system is intelligent when it's capable to adapt. So intelligence is going to require some amount of continuous learning. Also, going to require some amount of improvisation. It's not enough to assume that what you're going to be asked to do is something that you've seen before or something that is a simple interpolation of things you've seen before.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The power to adapt to something that is genuinely new. Because the thing is, even imagine you Could train on every bit of data ever generated in these three of humanity. It remains, that model would be capable of anticipating many different possible situations, but it remains that the future is going to be something different. For instance, if you train a GPT-3 model on data from the year 2002, for instance, and then use it today, it's going to be missing many things. It's going to be missing many common sense facts about the world. If I'm going to be missing vocabulary and so on.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think that's correct. I think the forms of reasoning that you sit perform are basically just reproducing patterns that it has seen in string data. So of course, if you're trained on the entire web, then you can produce an illusion of reasoning in many different situations, but it will break down if it's presented with a novel situation.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The information is not really in a format that's available to missions. So, no, I don't think the semantic web will ever work simply because it would be a lot of work to provide that information structured form. And there is not really any incentive for anyone to provide that work. So I think the way forward to make the knowledge on the web available to machines is actually something closer to unsupervised deep learning. The GBT3 is actually a bigger step in the direction of making the knowledge of the web available to machines than the semantic web was.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Automatically derived from tags on social media or just keywords in the same page as the image was found and so on. So it was very noisy and it turned out that you could easily get a better model, not just by trying, like if you train on more of the noisy data, you get an incrementally better model, but you very quickly hit diminishing returns. On the other hand, if you train on smaller data set with higher quality annotations, quality data annotations that are actually made by humans, you get a better model. And it also takes less time to train it.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, yeah, so the quality of the data is an interesting point. The thing is, if you're going to want to use these models in real products, then you want to fit them data that's as high quality, as factual, I would say, as unbiased as possible, but there's not really such a thing as unbiased data in the first place. But you probably don't want to train it on Reddit, for instance. Sounds like a bad plan. So from my personal experience working with large-scale deep learning models, so at some point I was working on a model at Google that trained 350 million labeled images. It's an image classification model. That's a lot of images. That's like probably most publicly available images on the web at the time. And it was very noisy data set because the labels were not originally annotated by hand, by humans.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“We can remove the compute barlock. I don't think it's a big problem. If you look at the pace at which we've improved the efficiency of deploying models in the past few years, I'm not worried about train time bottlenecks or model size bottlenecks. The bottleneck in the case of this generative transformer models is absolutely the trained data”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“No, I don't think so. So to begin with, the bottleneck with scaling up GPT models pre-train transformer models is not going to be the size of the model or how long it takes to train it. The bottleneck is going to be the train data because OpenAI is already training GPT-3 on accrail of basically the entire web. And that's a lot of data. So you could imagine training on more data than that, like Google could train on more data than that. But it would still be only incrementally more data. And I don't recall exactly how much more data GPD3 was trained on compared to GPT-2, but it's probably at less like 100 or maybe even 1,000x. I don't have the exact number. You're not going to be able to train the model on 100 more data than what you're already doing.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so if you try to make it generate programs, it will perform well for any program that it has seen in its training data. But because program space is not interpretative, it's not going to be able to generalize to problems it hasn't seen before.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Potentially, yes. So in general, the problem with these models, these generative models, is that they are very good at generating plausible text, but that's just not enough, right? I think one avenue that would be very interesting to make progress is to make it possible to write programs over the latent space that these models operate on that you would rely on these self-supervised models to generate a sort of flag pool of knowledge and concepts and common sense, and then you would be able to write explicit reasoning programs over it because the current problem with GPT-3 is that you can be quite difficult to get it to do what you want to do. If you want to turn GPT-3 into products, you need to put Constraints on it, you need to force it to obey certain rules. So you need a way to program it explicitly.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Is plausibility and it has no other constraints. It's not constrained to be self consistent, for instance, right? And so for this reason, one thing that I thought was very interesting with GPD3 is that you can predetermine the answer it will give you by asking the question in a specific way because it's very responsive to the way you ask the question since it has No understanding of the content of the question. And if you have the same question in two different ways that are basically adversarially engineered to produce certain answer, you will get two different answers, two contractor answers.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, if you train bigger model on more data, then your text will be increasingly more context aware and increasingly more plausible in the same way that GPT-3 is much better at generating plausible text compared to GPT2. But that said, I don't think just scaling up the model to more transformer layers and more train data is going to address the flaws of GPT-3, which is that it can generate plausible texts, but that text is not constrained by anything else other than plausibility. So in particular, it's not constrained by factualness or even consistency, which is why it's very easy to get GPT-3 to generate statements that are factually untrue or to generate statements that are even self-contradictory, because its only goal”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“What is it going to scale? How good is GPTN going to be? So I believe GPTN is gonna. Is going to improve on the strength. RGPT2 and 3, which is it will be able to generate ever more plausible text in context.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's possible that a significant chunk of intelligence is this giant associative memory. I definitely don't believe that intelligence is just a giant associative memory, but it may well be a big component.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I think what's interesting about GPT 3 is the idea that it may be able to learn new tasks after just being shown a few examples. So I think if it's actually capable of doing that, that's novel and that's very interesting and that's something we should investigate. That said, I must say I'm not entirely convinced that we have shown it's capable of doing that. It's very likely given the amount of data that the model is trained on, that what it's actually doing is pattern matching a new task you give it with a task that it's been exposed to in its string data. It's just recognizing the task instead of just developing a model of the task, right?”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“There are definitely people who argue that current deep planning techniques are already the way to general artificial intelligence and that all you need to do is to scale it up to all the available training data. And that's if you look at the waves that OpenAI's GPT-3 model has made, you see echoes of this idea.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, if you ask people who are seriously thinking about interagency, they will definitely not say that all you need to do is like the mind is just in your network. However, it's actually a view that's very popular, I think, in the deep learning community, that many people are kind of conceptually intellectually lazy about it.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“From experience, right? So it's a sponge that reflects the complexity of the world, the complexity of your life experience, essentially, that everything you know and everything we can do is a reflection of something you found in the outside world, essentially. So this is an idea that's very old, that was not very popular, for instance, in the 1970s, but that had gained a lot of vitality recently with the rise of connectionism in particular deep learning. And so today deep learning is the dominant paradigm in AI. And I feel like lots of AI researchers are conceptualizing the mind via a deep learning metaphor. Like they see the mind as a kind of randomly initialized neural network that starts blank when you're born and then that gets trained, yeah. Exposure to train data that Requires knowledge and scales for exposure to train data.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, it was constituted the mind was a collection of programs that were primarily logical and that all you needed to do to create a mine was to write down these programs. And they would operate over knowledge, which would be stored in some kind of database. And as long as your database would encompass everything about the world and your logical rules were comprehensive, then you would have a mind. So the other view of the mind is the brain as a sort of blank slate. This is a very old idea. You find it in John Locke's writings. This is the tabula. And this is this idea that the mind is some kind of like information sponge that starts empty, that starts blank, and that absorbs knowledge and”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Very much understood the mind through the metaphor of the mainframe computer because that was the tool they were working with, right? And so you had this static program, this collection of very different static programs operating over a database like memory. And in this picture, learning was not very important. Learning was considered to be just memorization. And in fact, learning is basically not featured in AI textbooks until the 1980s with the rise of machine learning.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, so the first part of the paper is an assessment of the different ways we've been thinking about intelligence and the different ways we've been evaluating progress in AI. And the history of cognitive sciences has been shaped by two views of the human mind. And one view is the evolutionary psychology view in which the mind is a collection of fairly static special purpose ad hoc mechanisms. There have been hard coded by evolution over our history as a species over a very long time. Early AI researchers, people like Marvin Minsky, for instance, they clearly subscribed to this view. And they saw the mind as a kind of collection of static programs similar to the programs they would run on mainframe computers.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Intelligence is. It should not just be a binary indicator that tells you this system is intelligent or it isn't. It should be actionable. It should have explanatory power. So you could use it as a feedback signal. It would show you the way towards building more intelligent systems.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the goal of the paper is to clear up some longstanding misunderstandings about the way we've been conceptualizing intelligence in the AI community and in the way we've been evaluating progress in AI. There's been a lot of progress recently in machine learning and people are extrapolating from that progress that we're about to solve general intelligence. And if you want to be able to evaluate these statements, you need to precisely define what you're talking about when you're talking about general intelligence. And you need a formal way, a reliable way to measure how much intelligence, how much general intelligence a system processes. And ideally, this measure of intelligence should be actionable. So it should not just describe what”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, if you see a very strong chess player, you know they weren't born knowing how to play chess. So they had to acquire that skill with their limited resources, with their limited lifetime. And they did that because they are generally intelligent. And so they may as well have acquired any other skill. You know they have this potential. And on the other hand, if you see a computer playing a chess, you cannot make these same assumptions because you cannot just assume the computer is generally intelligent. The computer may be born knowing how to play chess in the sense that it may have been programmed by a human that has understood chess for the computer and that has just encoded the output of that understanding in aesthetic program. And that program is not intelligent.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes. I mean, the skit is the only thing you can objectively measure. But yeah, so the thing to keep in mind is that when you see skill in the human, it Gives you a strong signal that that human is intelligent because you know they weren't born with that skill typically. Like you see a very strong chess player. Maybe you're a very strong chess player yourself.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Can generalize away from its evolution history is much greater than the degree to which a deep planning system today can generalize away from string data.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, there's definitely a little bit of that, but it's pretty clear to me that we're able to, you know, most of the things we do any given day in our modern civilization are things that are very, very different from what our ancestors a million years ago would have been doing in a given day. And your environment is very different. So I agree that everything we do, we do it with cognitive building blocks that we acquired over the course of evolution, right? And that anchors our cognition to a certain context, which is the human condition very much. But still, our mind is capable of a pretty remarkable degree of generality, far beyond anything we can create in artificial systems today, like the degree in which the mind can generalize from its evolutionary history.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“There's always deeper intelligence, I guess. You can argue that, but that does not take anything away from the fact that humans are intelligence. And you can tell that because they are capable of adaptation and generality. And you see that in particular in the fact that Human capable of handling situations and tasks that are quite different from anything that any of our evolutionary ancestors has ever encountered. So you are capable of generalizing very much out of distribution if you consider our evolutionary history as being in a way altering data.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Be able to tell the difference between the process and its output. We should not confuse the output and the process. It's the same as, you know, do not confuse a road building company and one specific road because one specific road takes you from point A to point B. But a road building company can take you from, can make a path from anywhere to anywhere else.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Not going to be able to perform well without human involvement because the source of intelligence, the entity that is capable of that process is the human programmer. So we should.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a big part of intelligence, yes. And intelligence is more precisely how efficiently you are able to adapt, how efficiently you are able to basically master your environment, how efficiently you can acquire new skills. And I think there's a big distinction to be drawn between intelligence, which is a process and the output of that process, which is skill. So, for instance, if you have a very smart human programmer that considers the game of chess and that writes down a static program that can play chess, then the intelligence is the process of developing that program. But the program itself is just encoding the output artifact of that process. The program itself is not intelligent. And the way you tell it's not intelligent is that if you put it in a different context, you ask it to play Go or something.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, being able to adapt, so not change, but certainly a change to the direction. Being able to adapt yourself to your environment.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So you would see intelligence on display, for instance. Whenever you see a human being or an AI creature adapt to a new environment that it has not seen before, that it's creators did not anticipate. When you see adaptation, when you see improvisation, when you see generalization, that's intelligence. In reverse, if you have a system that's when you put it in a slightly new environment, it cannot adapt, it cannot improvise, it cannot deviate from what it's hard-coded to do or what it has been trained to do. That is a system that is not intelligent. There's actually a quote from Einstein that captures this idea, which is the measure of intelligence is the ability to change. I like that quote. I think it captures at least part of this idea.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The definition of intelligence. So intelligence is the efficiency with which you acquire new skills, tasks that you did not previously know about, that you did not prepare for. So it is not intelligence is not skill itself. It's not what you know. It's not what you can do. It's how well and how efficiently you can learn new things.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so do you think. The superintelligent AIs of the future will want to remember us the way we remember humans from the past? And do you think they won't be ashamed of having a biological origin?”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Because everything is a vector and everything has to be a vector because everything has to be differentiable. If your space is discrete, it's no longer differentiable. You cannot do deep learning in it anymore. Well, you could, but you could only do it by embedding it in a bigger continuous space. So if you do topology in the context of deep learning, you have to do it by embedding your topology in a geometry.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, if you're talking about topologies, then points are either connected or not. So topology is more like a subway map. And geometry is when you're interested in the distance between things. And in subway map, you don't really have the concept of distance. You only have the concept of whether there is a train going from station A to station B. And what we do in deep learning is that we're actually dealing with geometric spaces. We are dealing with concept vectors, word vectors that have a distance between the logistics in terms of dot product.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's possible. I think it's reasonable to assume that Some level of topological processing in the brain that the brain is very associative in nature. And I also believe that a topological space is better medium to encode thoughts than a geometric space. So I think”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“If you have thousands of documents with your own thoughts in Google Docs, why don't you write some kind of search engine, like maybe a mind map, a piece of software, mind mapping software where you write down a concept. And then it gives you sentences or paragraphs from your Salz and Google Docs document that match this concept.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, like in a subway map, there are some nodes that are more connected than others, and there are some nodes that are more important than others. So there are destinations, but it's not going to be purely like a tree, for instance.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“If you want to organize your ideas by Writing down what you think, which I think is effective. How do you know what you think about something if you don't write it down? If you do that, the thing is that it imposes much more syntactic structure over your ideas, which is not required with a mind map. So MindMap is kind of like a lower level, more freehand way of organizing your thoughts. And once you've drawn it, then you can start actually voicing your thoughts in terms of paragraphs.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Which it's a mind map, it's your mind map, you're free to draw anything you want, you're free to draw any connection you want. Just make a difference if you think the central node is not the right node.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, it's more for graph than a tree. And it's not limited to just writing down words. You can also draw things. And it's not supposed to be purely hierarchical, right? Like you can, the point is that you can start once you start writing it down, you can start reorganizing it so that it makes more sense, so that it's connected in a more effective way.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, typically you draw a mind map to organize the way you think about a topic. So you would start by writing down the key concept about that topic. Like you would write intelligence or something. And then you would start adding associative connections. Like, what do you think about when you think about intelligence? What do you think are the key elements of intelligence? So maybe we'd have language, for instance, you'd have motion. And so you would start running nodes with these things. And then you would see What do you think about when you think about motion and so on? And you would go like that, like a tree.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Guess in Mind Map is a way to make kind of like the mess inside your mind to just put it on paper so that you gain more control over it. It's a way to organize things on paper and as kind of like a consequence of organizing things on paper, it starts being more organized inside your own mind.”
2020-08-31 · Lex Fridman Podcast · #120 – François Chollet: Measures of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source