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>Two, all the loud successes of statistical machine learning in the last couple of decades are closely tied to minutely specialised neural net architectures: CN

>Two, all the loud successes of statistical machine learning in the last couple of decades are closely tied to minutely specialised neural net architectures: CNNs for image classification, LSTMs for translation, Transformers for vision, Difussion models and Ganns for image generation. If that's not encoding knowledge of a domain, what is? Transformers, Diffusion for Vision, Image generation are really odd examples here. None of those architectures or training processes are tuned for Vision in mind lol. It was what? 3 years after Attention 2017 before the famous Vit paper. CNNs have lost a lot of favor to Vits, LSTMs are not the best performing translators today. The bitter lesson is that less encoding of "expert" knowledge results in better performance and this has absolutely held up. The "encoding of knowledge" you call these architectures is nowhere near that of the GOFAI kind and even more than that, less biased NN architectures seem to be winning out. >That's because the minutely specialised architectures in point number two are inefficient as all hell; the result of not having a good way to encode expert knowledge. Inefficient is a whole lot better than can't even play the game, the story of GOFAI for the last few decades. >If capabilities were improving, we should see the number of examples required to train a state-of-the-art system either staying the same, or going down. Well, they ain't. The capabilities of models are certainly increasing. Even your example is blatantly wrong. Do you realize how much more data and compute it would take to train a Vanilla RNN to say GPT-3 level performance?

YeGoblynQueenne 3y ago
>> Inefficient is a whole lot better than can't even play the game, the story of GOFAI for the last few decades. See e.g. my link above where GOFAI plays the game (Atari) very well indeed. Also see Watson winning Jeopardy (a hybrid system, but mainly GOFAI - using frames and Prolog for knowledge extraction, encoding and retrieval). And Deep Blue beating Kasparov. And MCTS still the SOTA search algo in Go etc. And EURISCO playing Traveller as above. And Pluribus playing Poker with expert game-playing knowledge. And the recent neuro-symbolic DeepMind thingy that solves geometry problems from the maths olympiad. etc. etc. [Gonna stop editing and adding more as they come to my mind here.] And that's just playing games. As I say in my comment above planning and scheduling, SAT, constraints, verification, theorem proving- those are still dominated by classical systems and neural nets suck at them. Ask Yan LeCun: "Machine learning sucks". He means it sucks in all the things that classical AI does best and he means he wants to do them with neural nets, and of course he'll fail.
- famouswaffles 3y ago
  That was a figure of speech. I didn't literally mean games (not that GOFAI performs better than NNs in those games anyway). I simply went off your own examples - Vision, Image generation, Translation etc. >As I say in my comment above planning and scheduling, SAT, constraints, verification, theorem proving- those are still dominated by classical systems You can use NNs for all these things. It wouldn't make a lot of sense because GOFAI would be perfect and the former would be inefficient but you certainly could which is again more than I can say for GOFAI and the domains you listed.
  - YeGoblynQueenne 3y ago
    I don't understand your comment. Clarify. As it is, your comment seems to tell me that neural nets are good at neural net things and GOFAI is good at GOFAI things, which is obvious, and is what I'm saying: neural nets can make only very limited use of expert knowledge and so suck in all domains where domain knowledge is abundant and abundantly useful, which are the same domains where GOFAI dominates. GOFAI can make very good use of expert knowledge but is traditionally not as good in domains where only tacit knowledge is available, because we don't understand the domain well enough yet, like in anything to do with pattern recognition, which is the same domains where neural nets dominate. If explicit, expert knowledge was available for those domains, then GOFAI would dominate, and neural nets would fall behind, completely contrary to what Sutton thinks. So, the bitter lesson is only bitter for those who are not interested in what classical AI systems can do best. For those of us who are, the lesson is sweet indeed: we're making progress, algorithmic progress, progress in understanding, scientific progress, and don't need to burn through thousands of credit to train on server farms to do anything of note. That's even a running joke in my team: hey, do you need any server time? Nah, I'll run the experiment on my laptop over lunch. And then beat the RL algo (PPO) that needs three days training on GPUs. To solve mazes badly.
    - famouswaffles 3y ago
      NNs can do the things GOFAI is good at a whole lot better than GOFAI can do the things NNs are good at.
adw 3y ago
Yeah, all of those architectures are _themselves_ hacks to get around having insufficient compute! They absolutely were encoding inductive biases into the network to get around not being able to train enough, and transformers (handwaving hard enough to levitate, the currently-trainable model family with the least inductive bias) have eaten the world in all domains. This is evidence _for_ the Bitter Lesson, not against it.
- YeGoblynQueenne 3y ago
  They haven't (eaten the world etc). They just happen to be the models that trend hard right now. I bet if you could compare like for like you'd be able to see some improvement in performance from Transformers, but that 'd be extremely hard to separate from the expected improvement from the constantly increasing amounts of data and compute. For example, you could, today, train a much bigger and deeper Multi-Layered Perceptron than you could thirty years ago, but nodoy is trying because that's so 1990's, and in any case they have the data and compute to train much bigger, much more inefficient (contrary to what you say if I got that right) architectures. Wait a few years and the Next Big Thing in AI will come along, hot on the heels of the next generation of GPUs, or tensor units or whatever the hardware industry can cook up to sell shovels for the gold rush. By then, Transfomers will have hit the plateau of diminishing returns, there'll be gold in them there other hills and nobody would talk of LLMs anymore because that's so 2020s. We've been there so many times before.
  - adw 3y ago
    > much more inefficient The tricky part here is that "efficiency" is not a single dimension! Transformers are much more "efficient" in one sense, in that they appear to be able to absorb much more data before they saturate; they're in general less computationally efficient in that you can't exploit symmetries as hard, for example, at implementation time. Let's talk about that in terms of a concrete example: the big inductive bias of CNNs for vision problems is that CNNs essentially presuppose that the model should be translation-invariant. This works great — speeds up training and makes it more stable – until it doesn't and that inductive bias starts limiting your performance, which is in the large-data limit. Fully-connected NNs are more general than transformers, but they have _so many_ degrees of freedom that the numerical optimization problem is impractical. If someone figures out how to stabilize that training and make these implementable on current or future hardware, you're absolutely right that you'll see people use them. I don't think transformers are magic; you're entirely correct in saying that they're the current knee on the implementability/trainability curve, and that can easily shift given different unit economics. I think one of the fundamental disconnects here is that people who come at AI from the perspective of logic down think of things very differently to people like me who come at it from thermodynamics _up_. Modern machine learning is just "applications of maximum entropy", and to someone with a thermodynamics background, that's intuitively obvious (not necessarily correct! just obvious) –in a meaningful sense the _universe_ is a process of gradient descent, so "of course" the answer for some local domain models is maximum-entropy too. In that world view, the higher-order structure is _entirely emergent_. I'm, by training, a crystallographer, so the idea that you can get highly regular structure emerging from merciless application of a single principle is just baked into my worldview very deeply. Someone who comes at things from the perspective of mathematical logic is going to find that worldview very weird, I suspect.

YeGoblynQueenne 3y ago

>> Inefficient is a whole lot better than can't even play the game, the story of GOFAI for the last few decades. See e.g. my link above where GOFAI plays the game (Atari) very well indeed. Also see Watson winning Jeopardy (a hybrid system, but mainly GOFAI - using frames and Prolog for knowledge extraction, encoding and retrieval). And Deep Blue beating Kasparov. And MCTS still the SOTA search algo in Go etc. And EURISCO playing Traveller as above. And Pluribus playing Poker with expert game-playing knowledge. And the recent neuro-symbolic DeepMind thingy that solves geometry problems from the maths olympiad. etc. etc. [Gonna stop editing and adding more as they come to my mind here.] And that's just playing games. As I say in my comment above planning and scheduling, SAT, constraints, verification, theorem proving- those are still dominated by classical systems and neural nets suck at them. Ask Yan LeCun: "Machine learning sucks". He means it sucks in all the things that classical AI does best and he means he wants to do them with neural nets, and of course he'll fail.

famouswaffles 3y ago
That was a figure of speech. I didn't literally mean games (not that GOFAI performs better than NNs in those games anyway). I simply went off your own examples - Vision, Image generation, Translation etc. >As I say in my comment above planning and scheduling, SAT, constraints, verification, theorem proving- those are still dominated by classical systems You can use NNs for all these things. It wouldn't make a lot of sense because GOFAI would be perfect and the former would be inefficient but you certainly could which is again more than I can say for GOFAI and the domains you listed.
- YeGoblynQueenne 3y ago
  I don't understand your comment. Clarify. As it is, your comment seems to tell me that neural nets are good at neural net things and GOFAI is good at GOFAI things, which is obvious, and is what I'm saying: neural nets can make only very limited use of expert knowledge and so suck in all domains where domain knowledge is abundant and abundantly useful, which are the same domains where GOFAI dominates. GOFAI can make very good use of expert knowledge but is traditionally not as good in domains where only tacit knowledge is available, because we don't understand the domain well enough yet, like in anything to do with pattern recognition, which is the same domains where neural nets dominate. If explicit, expert knowledge was available for those domains, then GOFAI would dominate, and neural nets would fall behind, completely contrary to what Sutton thinks. So, the bitter lesson is only bitter for those who are not interested in what classical AI systems can do best. For those of us who are, the lesson is sweet indeed: we're making progress, algorithmic progress, progress in understanding, scientific progress, and don't need to burn through thousands of credit to train on server farms to do anything of note. That's even a running joke in my team: hey, do you need any server time? Nah, I'll run the experiment on my laptop over lunch. And then beat the RL algo (PPO) that needs three days training on GPUs. To solve mazes badly.
  - famouswaffles 3y ago
    NNs can do the things GOFAI is good at a whole lot better than GOFAI can do the things NNs are good at.

adw 3y ago

Yeah, all of those architectures are _themselves_ hacks to get around having insufficient compute! They absolutely were encoding inductive biases into the network to get around not being able to train enough, and transformers (handwaving hard enough to levitate, the currently-trainable model family with the least inductive bias) have eaten the world in all domains. This is evidence _for_ the Bitter Lesson, not against it.

YeGoblynQueenne 3y ago
They haven't (eaten the world etc). They just happen to be the models that trend hard right now. I bet if you could compare like for like you'd be able to see some improvement in performance from Transformers, but that 'd be extremely hard to separate from the expected improvement from the constantly increasing amounts of data and compute. For example, you could, today, train a much bigger and deeper Multi-Layered Perceptron than you could thirty years ago, but nodoy is trying because that's so 1990's, and in any case they have the data and compute to train much bigger, much more inefficient (contrary to what you say if I got that right) architectures. Wait a few years and the Next Big Thing in AI will come along, hot on the heels of the next generation of GPUs, or tensor units or whatever the hardware industry can cook up to sell shovels for the gold rush. By then, Transfomers will have hit the plateau of diminishing returns, there'll be gold in them there other hills and nobody would talk of LLMs anymore because that's so 2020s. We've been there so many times before.
- adw 3y ago
  > much more inefficient The tricky part here is that "efficiency" is not a single dimension! Transformers are much more "efficient" in one sense, in that they appear to be able to absorb much more data before they saturate; they're in general less computationally efficient in that you can't exploit symmetries as hard, for example, at implementation time. Let's talk about that in terms of a concrete example: the big inductive bias of CNNs for vision problems is that CNNs essentially presuppose that the model should be translation-invariant. This works great — speeds up training and makes it more stable – until it doesn't and that inductive bias starts limiting your performance, which is in the large-data limit. Fully-connected NNs are more general than transformers, but they have _so many_ degrees of freedom that the numerical optimization problem is impractical. If someone figures out how to stabilize that training and make these implementable on current or future hardware, you're absolutely right that you'll see people use them. I don't think transformers are magic; you're entirely correct in saying that they're the current knee on the implementability/trainability curve, and that can easily shift given different unit economics. I think one of the fundamental disconnects here is that people who come at AI from the perspective of logic down think of things very differently to people like me who come at it from thermodynamics _up_. Modern machine learning is just "applications of maximum entropy", and to someone with a thermodynamics background, that's intuitively obvious (not necessarily correct! just obvious) –in a meaningful sense the _universe_ is a process of gradient descent, so "of course" the answer for some local domain models is maximum-entropy too. In that world view, the higher-order structure is _entirely emergent_. I'm, by training, a crystallographer, so the idea that you can get highly regular structure emerging from merciless application of a single principle is just baked into my worldview very deeply. Someone who comes at things from the perspective of mathematical logic is going to find that worldview very weird, I suspect.