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Personally, as a ML researcher, I find GPT-3 very unsatisfying. There aren't any novel architectural details, it doesn't improve our "fundamental" understanding

Personally, as a ML researcher, I find GPT-3 very unsatisfying. There aren't any novel architectural details, it doesn't improve our "fundamental" understanding of the field, and it requires the type of computation I have no chance of getting. As a fan of the field, however, it is undoubtedly very cool. I still think there's tasks that GPT-3 has no chance of tackling (say, generating code to solve a novel programming task), but the bitter lesson is a bitter one indeed...

FeepingCreature 6y ago
It's kind of LHC-like, isn't it? Same physics "but larger".. "will it work if we scale it up? Apparently: yes." I think the big point, and what scares me a bit, is that we have yet to discover any sort of fundamental conceptual limit to the Transformer architecture.
- sooheon 6y ago
  Each attention block in the Transformer models a fully connected graph (with the attention heads being learned edge attributes). A graph is the most general data structure possible, so yeah, I don't think there's really a fundamental limitation to them, just a computational one. Latest papers from ICLR explore how they fully model CNNs and RNNs, for example, and I'm sure papers on their theoretical equivalence with GNNs are coming.
  - contravariant 6y ago
    > I'm sure papers on their theoretical equivalence with GNNs are coming. Not exactly a paper, but you mean something like this article: https://graphdeeplearning.github.io/post/transformers-are-gnns/ https://graphdeeplearning.github.io/post/transformers-are-gn... ?
    - sooheon 6y ago
      Pretty much that, yeah. Thanks for the link. That kind of article sets the intuition, and papers like these give it a better theoretical treatment: - https://openreview.net/pdf?id=HJlnC1rKPB https://openreview.net/pdf?id=HJlnC1rKPB - https://openreview.net/pdf?id=ByxRM0Ntvr https://openreview.net/pdf?id=ByxRM0Ntvr
  - p1esk 6y ago
    I'm not sure we want to work with "the most general data structure possible". We can use a Hopfield-like network where every neuron is connected to every other neuron and to itself. It probably won't be very useful. NN design have been moving from more general to less general architectures.
    - Erlich_Bachman 6y ago
      It is implied that at least in theory, the promise is to use them with the similar total complexity (like numberr of parameters and amount of required calculation), in which case yes we do want the most general data structure possible. If we can have a more general data structure that provides similar performance characteristics, it is easier to apply, to debug, to understand, and it likely means that we have found something more fundamental about the underlying world in general.
    - sooheon 6y ago
      I think you're conflating "generality" of NN architecture and "generality" of the inductive bias the NN models.
- krick 6y ago
  Only very remotely "kind of". LHC isn't mindless "will it work if we scale it up?" type of experiment at all. The idea behind it is very simple: there are fundamental properties we know we are after, we have some predictions about how particles behave, and we know we want to smash the particles together hard enough to verify some of these predictions. We know how to accelerate charged particles, so here we go, now we need powerful particle accelerator. Building or even running LHC isn't the experiment by itself, it's just an "easy" way to find out how theoretically predicted particle states look in the real life (hopefully disproving some possible theories). Many of ML experiments, though, are exactly what you said: we ultimately have no slightest idea, what we are doing, but maybe if we throw enough compute-power into gradient-descent over some generic enough network, it will learn to do something interesting. Well, I tell you what, theoretically you need only 2 wide layers of neurons to compute anything there is to compute, there just isn't enough computational power in the entire world to possibly come up with the "correct" neuron weights entirely by chance, so what the research mostly really is about is coming up with various tricks to make do with less computational resources, which is essentially making sense of some computational problems and making neural networks "less random", to direct the learning process. It is either about new neural architectures, new ways of posing questions for NN to answer, ways to make more training data out of the same amounts of available external data, or the alternatives for the gradient-descent learning approach altogether. So, to wrap it up. It may be kinda interesting to know that GPT-2 type of network didn't reach its full capacity, and if we scale it up even more, it still learns something new. Inspecting how it behaves also might lead to some insights or new possible applications. But ultimately, if there's no novelty of any kind in an experiment, training an NN nobody can realistically reproduce is a great way to show off (or to achieve some practical goal for the owner of this NN, if it was used to find something practical), but doesn't really contribute much (or anything) to ML research.
  - FeepingCreature 6y ago
    "Yes, but." 2-wide layers can compute any computable function, but they do not scale in any manageable fashion with problem complexity. (Because you have to model problems as increasingly large lookup tables, whose size explodes rapidly.) The big thing about GPT is that so far, improvement in GPT size has corresponded to a .. I think logarithmic?- improvement in both quality of response and complexity of the problem domain that GPT can model. GPT scales, in exactly the way that a 2-layer network doesn't, and we don't yet have a handle on at what size it stops scaling.
Veedrac 6y ago
> I still think there's tasks that GPT-3 has no chance of tackling (say, generating code to solve a novel programming task) Umm https://www.youtube.com/watch?v=y5-wzgIySb4 https://www.youtube.com/watch?v=y5-wzgIySb4 That was a smaller model fine-tuned on Github IIRC.
- lambdadmitry 6y ago
  That's not a "novel" task though. It's an extremely common pattern.
- throwawaygh 6y ago
  I don't really find that very impressive at all. 1. Scientifically, synthesizing really simple loop-free programs from high-level specs is not really new. You can synthesize programs at this level of complexity with a few minutes on a single ten year old laptop using 5-10 year old algorithms. 2. Scientifically, it's already known that the architectures used in nlp models are also useful for simple program synthesis tasks. 3. From an engineering perspective, here was a good HN discussion about this a few weeks ago. IMO this comment hits the nail on the head (https://news.ycombinator.com/item?id=23256333 https://news.ycombinator.com/item?id=23256333): > This technology converts writing code into bug hunting in pre-written code. Finding bugs in code that you did not write is way harder than writing the code yourself. So if anything, this makes programming harder, not easier, and we will need more programmers, not less. Rings true for this example. I can write my intended implementation much faster than I can read the generated code, find the bug, and think of a comment that captures the correct spec. It's interesting, but it's not "generating code to solve a novel programming task".
  - simias 6y ago
    I agree with you and I doubt we'll get useful AI-generated code before we get strong AI, and by the time we get that we'll have much more interesting applications to showcase I'm sure. I can definitely see this tech leading to ever more clever linters though. Having an AI reading my code, interpreting the comments, variable and function names and use that to look for bugs in my implementation could be highly valuable, assuming that the number of false positives isn't overwhelming. "Your comment says that you apply the discount only on palindromes but your condition here is wrong and your code ends up doing the opposite".
empath75 6y ago
I wonder they could do with it if the us decided to do an ai moonshot. They would be the equivalent of spending 500 billion in today’s dollars over 10 years. That’s a lot of compute.
- kevingadd 6y ago
  If you do the math on best-case costs for some of Google's AI projects they've been spending tens to hundreds of millions worth of compute on things like training StarCraft bots, so I wouldn't be surprised if the industry as a whole is throwing $50b USD worth of compute at various tasks every year already. If you're a company like Amazon or Google using your own fleet for it you're not actually spending that much money, of course...
nl 6y ago
You don't find Image GPT[1] novel? I'm not aware of anything else showing how effective the transformer architecture is on image tasks, and I think that is a pretty fundamental breakthrough. I realise it's not GPT-3 (it's based on GPT-2), but this seems more a project resourcing issue than being fundamentally unsatisfying. [1] https://openai.com/blog/image-gpt/ https://openai.com/blog/image-gpt/
longtom 6y ago
It is not only unsatisfying to ML researchers, but pretty much everyone. We are mere prediction machines and AI is going to render us dispensable bags of meat using an embarrassingly simple algorithm that you could explain a bright child within a day.
physicsyogi 6y ago
About a month ago, Open AI demoed a language model that does python code generation, using a natural language prompt. It was trained on Github repos. https://news.ycombinator.com/item?id=23250379 https://news.ycombinator.com/item?id=23250379
- chillee 6y ago
  This is extremely impressive, but 1. It's a cherry picked demo (no paper has come out yet), and 2. This isn't really what I mean by "novel programming task". I'm thinking more in line the type of questions you might get in competitive programming.
  - deleted 6y ago
    [deleted]

FeepingCreature 6y ago

It's kind of LHC-like, isn't it? Same physics "but larger".. "will it work if we scale it up? Apparently: yes." I think the big point, and what scares me a bit, is that we have yet to discover any sort of fundamental conceptual limit to the Transformer architecture.

sooheon 6y ago
Each attention block in the Transformer models a fully connected graph (with the attention heads being learned edge attributes). A graph is the most general data structure possible, so yeah, I don't think there's really a fundamental limitation to them, just a computational one. Latest papers from ICLR explore how they fully model CNNs and RNNs, for example, and I'm sure papers on their theoretical equivalence with GNNs are coming.
- contravariant 6y ago
  > I'm sure papers on their theoretical equivalence with GNNs are coming. Not exactly a paper, but you mean something like this article: https://graphdeeplearning.github.io/post/transformers-are-gnns/ https://graphdeeplearning.github.io/post/transformers-are-gn... ?
  - sooheon 6y ago
    Pretty much that, yeah. Thanks for the link. That kind of article sets the intuition, and papers like these give it a better theoretical treatment: - https://openreview.net/pdf?id=HJlnC1rKPB https://openreview.net/pdf?id=HJlnC1rKPB - https://openreview.net/pdf?id=ByxRM0Ntvr https://openreview.net/pdf?id=ByxRM0Ntvr
- p1esk 6y ago
  I'm not sure we want to work with "the most general data structure possible". We can use a Hopfield-like network where every neuron is connected to every other neuron and to itself. It probably won't be very useful. NN design have been moving from more general to less general architectures.
  - Erlich_Bachman 6y ago
    It is implied that at least in theory, the promise is to use them with the similar total complexity (like numberr of parameters and amount of required calculation), in which case yes we do want the most general data structure possible. If we can have a more general data structure that provides similar performance characteristics, it is easier to apply, to debug, to understand, and it likely means that we have found something more fundamental about the underlying world in general.
  - sooheon 6y ago
    I think you're conflating "generality" of NN architecture and "generality" of the inductive bias the NN models.
krick 6y ago
Only very remotely "kind of". LHC isn't mindless "will it work if we scale it up?" type of experiment at all. The idea behind it is very simple: there are fundamental properties we know we are after, we have some predictions about how particles behave, and we know we want to smash the particles together hard enough to verify some of these predictions. We know how to accelerate charged particles, so here we go, now we need powerful particle accelerator. Building or even running LHC isn't the experiment by itself, it's just an "easy" way to find out how theoretically predicted particle states look in the real life (hopefully disproving some possible theories). Many of ML experiments, though, are exactly what you said: we ultimately have no slightest idea, what we are doing, but maybe if we throw enough compute-power into gradient-descent over some generic enough network, it will learn to do something interesting. Well, I tell you what, theoretically you need only 2 wide layers of neurons to compute anything there is to compute, there just isn't enough computational power in the entire world to possibly come up with the "correct" neuron weights entirely by chance, so what the research mostly really is about is coming up with various tricks to make do with less computational resources, which is essentially making sense of some computational problems and making neural networks "less random", to direct the learning process. It is either about new neural architectures, new ways of posing questions for NN to answer, ways to make more training data out of the same amounts of available external data, or the alternatives for the gradient-descent learning approach altogether. So, to wrap it up. It may be kinda interesting to know that GPT-2 type of network didn't reach its full capacity, and if we scale it up even more, it still learns something new. Inspecting how it behaves also might lead to some insights or new possible applications. But ultimately, if there's no novelty of any kind in an experiment, training an NN nobody can realistically reproduce is a great way to show off (or to achieve some practical goal for the owner of this NN, if it was used to find something practical), but doesn't really contribute much (or anything) to ML research.
- FeepingCreature 6y ago
  "Yes, but." 2-wide layers can compute any computable function, but they do not scale in any manageable fashion with problem complexity. (Because you have to model problems as increasingly large lookup tables, whose size explodes rapidly.) The big thing about GPT is that so far, improvement in GPT size has corresponded to a .. I think logarithmic?- improvement in both quality of response and complexity of the problem domain that GPT can model. GPT scales, in exactly the way that a 2-layer network doesn't, and we don't yet have a handle on at what size it stops scaling.

Veedrac 6y ago

> I still think there's tasks that GPT-3 has no chance of tackling (say, generating code to solve a novel programming task) Umm https://www.youtube.com/watch?v=y5-wzgIySb4 https://www.youtube.com/watch?v=y5-wzgIySb4 That was a smaller model fine-tuned on Github IIRC.

lambdadmitry 6y ago
That's not a "novel" task though. It's an extremely common pattern.
throwawaygh 6y ago
I don't really find that very impressive at all. 1. Scientifically, synthesizing really simple loop-free programs from high-level specs is not really new. You can synthesize programs at this level of complexity with a few minutes on a single ten year old laptop using 5-10 year old algorithms. 2. Scientifically, it's already known that the architectures used in nlp models are also useful for simple program synthesis tasks. 3. From an engineering perspective, here was a good HN discussion about this a few weeks ago. IMO this comment hits the nail on the head (https://news.ycombinator.com/item?id=23256333 https://news.ycombinator.com/item?id=23256333): > This technology converts writing code into bug hunting in pre-written code. Finding bugs in code that you did not write is way harder than writing the code yourself. So if anything, this makes programming harder, not easier, and we will need more programmers, not less. Rings true for this example. I can write my intended implementation much faster than I can read the generated code, find the bug, and think of a comment that captures the correct spec. It's interesting, but it's not "generating code to solve a novel programming task".
- simias 6y ago
  I agree with you and I doubt we'll get useful AI-generated code before we get strong AI, and by the time we get that we'll have much more interesting applications to showcase I'm sure. I can definitely see this tech leading to ever more clever linters though. Having an AI reading my code, interpreting the comments, variable and function names and use that to look for bugs in my implementation could be highly valuable, assuming that the number of false positives isn't overwhelming. "Your comment says that you apply the discount only on palindromes but your condition here is wrong and your code ends up doing the opposite".

empath75 6y ago

I wonder they could do with it if the us decided to do an ai moonshot. They would be the equivalent of spending 500 billion in today’s dollars over 10 years. That’s a lot of compute.

kevingadd 6y ago
If you do the math on best-case costs for some of Google's AI projects they've been spending tens to hundreds of millions worth of compute on things like training StarCraft bots, so I wouldn't be surprised if the industry as a whole is throwing $50b USD worth of compute at various tasks every year already. If you're a company like Amazon or Google using your own fleet for it you're not actually spending that much money, of course...

nl 6y ago

You don't find Image GPT[1] novel? I'm not aware of anything else showing how effective the transformer architecture is on image tasks, and I think that is a pretty fundamental breakthrough. I realise it's not GPT-3 (it's based on GPT-2), but this seems more a project resourcing issue than being fundamentally unsatisfying. [1] https://openai.com/blog/image-gpt/ https://openai.com/blog/image-gpt/

longtom 6y ago

It is not only unsatisfying to ML researchers, but pretty much everyone. We are mere prediction machines and AI is going to render us dispensable bags of meat using an embarrassingly simple algorithm that you could explain a bright child within a day.

physicsyogi 6y ago

About a month ago, Open AI demoed a language model that does python code generation, using a natural language prompt. It was trained on Github repos. https://news.ycombinator.com/item?id=23250379 https://news.ycombinator.com/item?id=23250379

chillee 6y ago
This is extremely impressive, but 1. It's a cherry picked demo (no paper has come out yet), and 2. This isn't really what I mean by "novel programming task". I'm thinking more in line the type of questions you might get in competitive programming.
- deleted 6y ago
  [deleted]