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>No amount of training would cause a fly brain to be able to do what an octopus or bird brain can, or to model their behavioral generating process. Go back a f

>No amount of training would cause a fly brain to be able to do what an octopus or bird brain can, or to model their behavioral generating process. Go back a few evolutionary steps and sure you can. Most ANN architectures basically have relatively little to no biases baked in and the Transformer might be the most blank slate we've built yet. >No amount of training will cause a transformer to magically sprout feedback paths or internal memory, or an ability to alter it's own weights, etc. A transformer can perform any computation it likes in a forward pass and you can arbitrarily increase inference compute time with the token length. Feedback paths? Sure. Compute inefficient? Perhaps. Some extra programming around the Model to facilitate this ? Maybe but the architecture certainly isn't stopping you. Even if it couldn't, limited =/ trivial. The Human Brain is not Turing complete. Internal Memory ? Did you miss the memo ? Recurrency is overrated. Attention is all you need. That said, there are already state keeping language model architectures around. Altering weights ? Can a transformer continuously train ? Sure. It's not really compute efficient but architecture certainly doesn't prohibit it. >Architecture matters Compute Efficiency? Sure. What it is capable of learning? Not so much

HarHarVeryFunny 2y ago
> A transformer can perform any computation it likes in a forward pass No it can't. A transformer has a fixed number of layers - call it N. It performs N sequential steps of computation to derive it's output. If a computation requires > N steps, then a transformer most certainly can not perform it in a forward pass. FYI, "attention is all you need" has the implicit context of "if all you want to build is a language model". Attention is not all you need if what you actually want to build is a cognitive architecture.
- famouswaffles 2y ago
  Transformer produce the next token by manipulating K hidden vectors per layer, one vector per preceding token. So yes you can increase compute length arbitrarily by increasing tokens. Those tokens don't have to carry any information to work. https://arxiv.org/abs/2310.02226 https://arxiv.org/abs/2310.02226 And again, human brains are clearly limited in the number of steps it can compute without writing something down. Limited =/ Trivial >FYI, "attention is all you need" has the implicit context of "if all you want to build is a language model". Great. Do you know what a "language model" is capable of in the limit ? No These top research labs aren't only working on Transformers as they currently exist but it doesn't make much sense to abandon a golden goose before it has hit a wall.
  - HarHarVeryFunny 2y ago
    You are confusing number of sequential steps with total amount of compute spent. The input sequence is processed in parallel, regardless of length, so number of tokens has no impact on number of sequential compute steps which is always N=layers. > Do you know what a "language model" is capable of in the limit ? Well, yeah, if the language model is an N-layer transformer ...
    - famouswaffles 2y ago
      Fair Enough. Then increase N (N is almost always increased when a model is scaled up) and train or write things down and continue. A limitless iteration machine (without external aid) is currently an idea of fiction. Brains can't do it so I'm not particularly worried if machines can't either.
      - HarHarVeryFunny 2y ago
        Increasing number of layers isn't a smart way to solve it. It order to be able to reason effectively and efficiently the model needs to use as much, or as little, compute as needed for a given task. Completing "1+1=" should take less compute steps than "A winning sequence for white here is ...". This lack of "variable compute" is a widely recognized shortcoming of transformer-based LLMs, and there are plenty of others. The point apropos this thread is that you can't just train an LLM to be something that it is not. If the generating process required variable compute (maybe 1000's of steps) - e.g. to come up with a chess move - then no amount of training can make the LLM converge to model this generative process... the best it can do is to model the outcome of the generative process, not the process itself. The difference is that without having learnt the generative process, the model will fail when presented with a novel input that it didn't see during training, and therefore didn't memorize the "cheat sheet" answer for.
        famouswaffles 2y ago
        >Increasing number of layers isn't a smart way to solve it. The "smart way" is a luxury. Solving the problem is what matters. Think of a smart way later if you can. That's how a lot of technological advancement has worked. >It order to be able to reason effectively and efficiently the model needs to use as much, or as little, compute as needed for a given task. Completing "1+1=" should take less compute steps than "A winning sequence for white here is ...". Same thing. Efficiency is nice but a secondary concern. >If the generating process required variable compute (maybe 1000's of steps) - e.g. to come up with a chess move - then no amount of training can make the LLM converge to model this generative process. Every inference problem has itself a fixed number of compute steps it needs (yes even your chess move). Variability is a nice thing for between inferences(maybe move 1 required 500 but 2 only 240 etc) A nice thing but never a necessary thing. 3.5-turbo-instruct plays chess consistently at 1800 Elo so clearly the N of the current SOTA is already enough to play non-trivial chess at a level beyond most humans. There is an N large enough for every GI problem humans care about. Not to sound like a broken record but once again, limited =/ trivial.

HarHarVeryFunny 2y ago

> A transformer can perform any computation it likes in a forward pass No it can't. A transformer has a fixed number of layers - call it N. It performs N sequential steps of computation to derive it's output. If a computation requires > N steps, then a transformer most certainly can not perform it in a forward pass. FYI, "attention is all you need" has the implicit context of "if all you want to build is a language model". Attention is not all you need if what you actually want to build is a cognitive architecture.

famouswaffles 2y ago
Transformer produce the next token by manipulating K hidden vectors per layer, one vector per preceding token. So yes you can increase compute length arbitrarily by increasing tokens. Those tokens don't have to carry any information to work. https://arxiv.org/abs/2310.02226 https://arxiv.org/abs/2310.02226 And again, human brains are clearly limited in the number of steps it can compute without writing something down. Limited =/ Trivial >FYI, "attention is all you need" has the implicit context of "if all you want to build is a language model". Great. Do you know what a "language model" is capable of in the limit ? No These top research labs aren't only working on Transformers as they currently exist but it doesn't make much sense to abandon a golden goose before it has hit a wall.
- HarHarVeryFunny 2y ago
  You are confusing number of sequential steps with total amount of compute spent. The input sequence is processed in parallel, regardless of length, so number of tokens has no impact on number of sequential compute steps which is always N=layers. > Do you know what a "language model" is capable of in the limit ? Well, yeah, if the language model is an N-layer transformer ...
  - famouswaffles 2y ago
    Fair Enough. Then increase N (N is almost always increased when a model is scaled up) and train or write things down and continue. A limitless iteration machine (without external aid) is currently an idea of fiction. Brains can't do it so I'm not particularly worried if machines can't either.
    - HarHarVeryFunny 2y ago
      Increasing number of layers isn't a smart way to solve it. It order to be able to reason effectively and efficiently the model needs to use as much, or as little, compute as needed for a given task. Completing "1+1=" should take less compute steps than "A winning sequence for white here is ...". This lack of "variable compute" is a widely recognized shortcoming of transformer-based LLMs, and there are plenty of others. The point apropos this thread is that you can't just train an LLM to be something that it is not. If the generating process required variable compute (maybe 1000's of steps) - e.g. to come up with a chess move - then no amount of training can make the LLM converge to model this generative process... the best it can do is to model the outcome of the generative process, not the process itself. The difference is that without having learnt the generative process, the model will fail when presented with a novel input that it didn't see during training, and therefore didn't memorize the "cheat sheet" answer for.
      - famouswaffles 2y ago
        >Increasing number of layers isn't a smart way to solve it. The "smart way" is a luxury. Solving the problem is what matters. Think of a smart way later if you can. That's how a lot of technological advancement has worked. >It order to be able to reason effectively and efficiently the model needs to use as much, or as little, compute as needed for a given task. Completing "1+1=" should take less compute steps than "A winning sequence for white here is ...". Same thing. Efficiency is nice but a secondary concern. >If the generating process required variable compute (maybe 1000's of steps) - e.g. to come up with a chess move - then no amount of training can make the LLM converge to model this generative process. Every inference problem has itself a fixed number of compute steps it needs (yes even your chess move). Variability is a nice thing for between inferences(maybe move 1 required 500 but 2 only 240 etc) A nice thing but never a necessary thing. 3.5-turbo-instruct plays chess consistently at 1800 Elo so clearly the N of the current SOTA is already enough to play non-trivial chess at a level beyond most humans. There is an N large enough for every GI problem humans care about. Not to sound like a broken record but once again, limited =/ trivial.