5 ms·

The whole "mystery" of transformer is that instead of a linear sequence of static weights times values in each layer, you now have 3 different matrices that are

The whole "mystery" of transformer is that instead of a linear sequence of static weights times values in each layer, you now have 3 different matrices that are obtained from the same input through multiplication of learned weights, and then you just multiply the matrices together. I.e more parallelism which works out nice, but very restrictive since the attention formula is static. We arent going to see more progress until we have a way to generalize the compute graph as a learnable parameter. I dunno if this is even possible in the traditional sense of gradients due to chaotic effects (i.e small changes reflect big shifts in performance), it may have to be some form of genetic algorithm or pso that happens under the hood.

ex3ndr 3y ago
This is basically this - it can learn ignore some paths, and amplify something more important, then you can just cut this paths without sensible loss of quality. The problem is that you are not going to win anything from this - non-matrix multiplication would be slower or the same.
- ActorNightly 3y ago
  The issue is that you are thinking of this in terms of information compression, which is what LLMs are. Im more concerned with an LLM having the ability to be trained to the point where a subset of the graph represents all the nand gates necessary for a cpu and ram, so when you ask it questions it can actually run code to compute them accurately instead of offering a statistical best guess, i.e decompression after lossy compression.
  - exe34 3y ago
    Just give it a computer? Even a virtual machine. It can output assembly code or high level code that gets compiled.
    - ActorNightly 3y ago
      The issue is not having access to the cpu, the issue is that the model being able to be trained in such a way that it has representative structures for applicable problem solving. Furthermore, the structures itself should Philosophically, you can start ad hoc-ing functionalities on top of LLMs and expect major progress. Sure, you can make them better, but you will never get to the state where AI is massively useful. For example, lets say you gather a whole bunch of experts in respective fields, and you give them a task to put together a detailed plan on how to build a flying car. You will have people doing design, doing simulations, researching material sourcing, creating CNC programs for manufacturing parts, sourcing tools and equipment, writing software, e.t.c. And when executing this plan, they would be open to feedback for anything missed, and can advise on how to proceed. The AI with above capability should be able to go out on the internet, gather respective data, run any soft of algorithms it needs to run, and perhaps after a month of number crunching on a cloud rented TPU rack produce step by step plan with costs on how to do all of that. And it would be better than those experts because it should be able to create a much higher fidelity simulations to account for things like vibration and predict if some connector if going to wobble loose .
      - beambot 3y ago
        > Philosophically, you can start ad hoc-ing functionalities on top of LLMs and expect major progress. Sure, you can make them better, but you will never get to the state where AI is massively useful. Evolution created various neural structures in biological brains (visual cortex, medulla, thalamus, etc) rather ad-hoc, and those resulted in "massively useful" systems. Why should AI be different?
        ActorNightly 3y ago
        I mean, we could definitely run architectures through simulated evolution with genetic algorithms, but then you arrive at the same problem as humans do, which is that you end up with a statistically best solution for given conditions. Sure, that could be a form of AI but there is likely a better (and likely faster) way to build an AI that isn't fundamentally statistical in nature and is adaptable to any and all problems.
  - sroussey 3y ago
    Well, just remember that NAND gates are made of transistors themselves which are a statistical model of a sort… just designed to appear digital when combined to that NAND level. This is why I am very interested in analog again—quantum stuff is statistical already, so why go from statistical (analog) to digital (huge drop off of performance, e.g. just look at basic addition in a ALU) and back to statistical. Very interested. Not sure if it will ever be worth it, but can’t rule it out.
necroforest 3y ago
> We arent going to see more progress until we have a way to generalize the compute graph as a learnable parameter That's a bold statement since a ton of progress has been made without learning the compute graph.
- nomel 3y ago
  From my naive perspective, there seems to be a plateau, that everyone is converging on, somewhere between ChatGPT 3.5 and 4 level of performance, with some suspecting that the implementation of 4 might involve several expert models, which would already be extra sauce, external to the LLM. This, combined with the observation that generative models converge to the same output, given the same training data, regardless of architecture (having trouble finding the link, it was posted here some weeks ago), external secret sauce, outside the model, might be where the near term gains are. I suppose we'll see in the next year!
  - manojlds 3y ago
    We already have competitors to Transformers https://arxiv.org/abs/2312.00752 https://arxiv.org/abs/2312.00752
    - heyoni 3y ago
      Where do I enter in my credit card info?
      - taneq 3y ago
        You hire people to implement a product based on this?
- ActorNightly 3y ago
  We have made progress in efficiency, not functionality. Instead of searching google or stack overflow or any particular documentation, we just go to Chatgpt. Information compression is cool, but I want actual AI.
knightoffaith 3y ago
How can gradient descent work on compute graphs when the space of compute graphs is discrete?
- tnecniv 3y ago
  It can’t. There’s no gradient since it’s not a sufficiently nice space for them. You can use gradient free methods but I’d be shocked if there was an efficient enough way to do that
- blackbear_ 3y ago
  Perhaps in a way similar to this paper: https://arxiv.org/abs/1806.09055 https://arxiv.org/abs/1806.09055
  - knightoffaith 3y ago
    I wonder why this hasn't taken off.
    - ActorNightly 3y ago
      From brief look at the paper, they are doing a gradient descent of the architecture based on validation loss, which does good for efficiency, but its not ground breaking. The problem is that you are still training towards a target of a correct answer. I don't think this is gonna be applicable in the future, in the sense that we have to train on other things (like logical consistency somehow encoded into the network), as well as correct answers.
      - knightoffaith 3y ago
        Your expectations are pretty high. Differentiable architecture search as you mentioned in the original comment is one thing; going beyond empirical risk minimization-based learning is another thing entirely. In fact, they seem mostly orthogonal. That aside, it seems like AI has had the most empirical success by not imposing hard constraints/structure, but letting models learn completely "organically". The computationalists (the folks who have historically been more into this "AI has to have things like logical consistency embedded into its structure" kind of thinking) seem to have basically lost, empirically. Who even knows what Soar[1] is nowadays? Maybe some marriage of the two paradigms will lead to better results, but I doubt that things will head in that direction anytime soon given how massively far just having parallelizable architectures and adding more parameters has gotten us. [1] https://en.wikipedia.org/wiki/Soar_(cognitive_architecture) https://en.wikipedia.org/wiki/Soar_(cognitive_architecture)
kevindamm 3y ago
hyperparameter tuning does already go some of the way towards learning the compute graph, though very constrained and with a lot more training required.
api 3y ago
Genetic algorithms figured out GI the first time, but it took a while.
- mdp2021 3y ago
  Could you please expand?
  - api 3y ago
    Evolution built our brains. Though to be fair, actual biological evolution is more complex than simple genetic algorithms. More like evolution strategies with meta-parameter-learning and adaptive rate tuning among other things.
mikeknoop 3y ago
Couldn't find your contact info. Email me?
logicchains 3y ago
>The whole "mystery" of transformer is that instead of a linear sequence of static weights times values in each layer, you now have 3 different matrices that are obtained from the same input through multiplication of learned weights, and then you just multiply the matrices together. I.e more parallelism which works out nice, but very restrictive since the attention formula is static. That's not it at all. What's special about transformers is they allow each element in a sequence to decide which parts of data are most important to it from each other element in the sequence, then extract those out and compute on them. The big theoretical advantage over RNNs (which were used for sequences prior to transformers), is that transformers support this in a lossless way, as each element has full access to all the information in every other element in the sequence (or at least all the ones that occurred before it in time sequences). RNNs and "linear transformers" on the other hand compress past values, so generally the last element of a long sequence will not have access to all the information in the first element of the sequence (unless the RNN internal state was really really big so it didn't need to discard any information).
- ActorNightly 3y ago
  >What's special about transformers is they allow each element in a sequence to decide which parts of data are most important to it from each other element in the sequence, then extract those out and compute on them. They do that in theory. In practice, its just all matrix multiplication. You could easily structure a transformer as a bunch of fully connected deep layers and it would be mathematically equivalent, just computationally inefficient.
cyberpewpew 3y ago
can you explain exactly what you mean by this? I understand what a compute graph is, but I'm not getting the idea of making a learnable parameter.
- cyberpewpew 3y ago
  nevermind, after looking at my own question 3-4 times it clicked.
lambdatronics 3y ago
>a way to generalize the compute graph as a learnable parameter. Agreed. Seems analogous with how human mental processes are used to solve the kind of problems we'd like LLMs to solve (going beyond "language processing" which transformers do well, to actual reasoning which they can only mimic). Although you risk it becoming a Turing machine by giving it flow control & then training is a problem as you say. Perhaps not intractable though.

ex3ndr 3y ago

This is basically this - it can learn ignore some paths, and amplify something more important, then you can just cut this paths without sensible loss of quality. The problem is that you are not going to win anything from this - non-matrix multiplication would be slower or the same.

ActorNightly 3y ago
The issue is that you are thinking of this in terms of information compression, which is what LLMs are. Im more concerned with an LLM having the ability to be trained to the point where a subset of the graph represents all the nand gates necessary for a cpu and ram, so when you ask it questions it can actually run code to compute them accurately instead of offering a statistical best guess, i.e decompression after lossy compression.
- exe34 3y ago
  Just give it a computer? Even a virtual machine. It can output assembly code or high level code that gets compiled.
  - ActorNightly 3y ago
    The issue is not having access to the cpu, the issue is that the model being able to be trained in such a way that it has representative structures for applicable problem solving. Furthermore, the structures itself should Philosophically, you can start ad hoc-ing functionalities on top of LLMs and expect major progress. Sure, you can make them better, but you will never get to the state where AI is massively useful. For example, lets say you gather a whole bunch of experts in respective fields, and you give them a task to put together a detailed plan on how to build a flying car. You will have people doing design, doing simulations, researching material sourcing, creating CNC programs for manufacturing parts, sourcing tools and equipment, writing software, e.t.c. And when executing this plan, they would be open to feedback for anything missed, and can advise on how to proceed. The AI with above capability should be able to go out on the internet, gather respective data, run any soft of algorithms it needs to run, and perhaps after a month of number crunching on a cloud rented TPU rack produce step by step plan with costs on how to do all of that. And it would be better than those experts because it should be able to create a much higher fidelity simulations to account for things like vibration and predict if some connector if going to wobble loose .
    - beambot 3y ago
      > Philosophically, you can start ad hoc-ing functionalities on top of LLMs and expect major progress. Sure, you can make them better, but you will never get to the state where AI is massively useful. Evolution created various neural structures in biological brains (visual cortex, medulla, thalamus, etc) rather ad-hoc, and those resulted in "massively useful" systems. Why should AI be different?
      - ActorNightly 3y ago
        I mean, we could definitely run architectures through simulated evolution with genetic algorithms, but then you arrive at the same problem as humans do, which is that you end up with a statistically best solution for given conditions. Sure, that could be a form of AI but there is likely a better (and likely faster) way to build an AI that isn't fundamentally statistical in nature and is adaptable to any and all problems.
- sroussey 3y ago
  Well, just remember that NAND gates are made of transistors themselves which are a statistical model of a sort… just designed to appear digital when combined to that NAND level. This is why I am very interested in analog again—quantum stuff is statistical already, so why go from statistical (analog) to digital (huge drop off of performance, e.g. just look at basic addition in a ALU) and back to statistical. Very interested. Not sure if it will ever be worth it, but can’t rule it out.

necroforest 3y ago

> We arent going to see more progress until we have a way to generalize the compute graph as a learnable parameter That's a bold statement since a ton of progress has been made without learning the compute graph.

nomel 3y ago
From my naive perspective, there seems to be a plateau, that everyone is converging on, somewhere between ChatGPT 3.5 and 4 level of performance, with some suspecting that the implementation of 4 might involve several expert models, which would already be extra sauce, external to the LLM. This, combined with the observation that generative models converge to the same output, given the same training data, regardless of architecture (having trouble finding the link, it was posted here some weeks ago), external secret sauce, outside the model, might be where the near term gains are. I suppose we'll see in the next year!
- manojlds 3y ago
  We already have competitors to Transformers https://arxiv.org/abs/2312.00752 https://arxiv.org/abs/2312.00752
  - heyoni 3y ago
    Where do I enter in my credit card info?
    - taneq 3y ago
      You hire people to implement a product based on this?
ActorNightly 3y ago
We have made progress in efficiency, not functionality. Instead of searching google or stack overflow or any particular documentation, we just go to Chatgpt. Information compression is cool, but I want actual AI.

knightoffaith 3y ago

How can gradient descent work on compute graphs when the space of compute graphs is discrete?

tnecniv 3y ago
It can’t. There’s no gradient since it’s not a sufficiently nice space for them. You can use gradient free methods but I’d be shocked if there was an efficient enough way to do that
blackbear_ 3y ago
Perhaps in a way similar to this paper: https://arxiv.org/abs/1806.09055 https://arxiv.org/abs/1806.09055
- knightoffaith 3y ago
  I wonder why this hasn't taken off.
  - ActorNightly 3y ago
    From brief look at the paper, they are doing a gradient descent of the architecture based on validation loss, which does good for efficiency, but its not ground breaking. The problem is that you are still training towards a target of a correct answer. I don't think this is gonna be applicable in the future, in the sense that we have to train on other things (like logical consistency somehow encoded into the network), as well as correct answers.
    - knightoffaith 3y ago
      Your expectations are pretty high. Differentiable architecture search as you mentioned in the original comment is one thing; going beyond empirical risk minimization-based learning is another thing entirely. In fact, they seem mostly orthogonal. That aside, it seems like AI has had the most empirical success by not imposing hard constraints/structure, but letting models learn completely "organically". The computationalists (the folks who have historically been more into this "AI has to have things like logical consistency embedded into its structure" kind of thinking) seem to have basically lost, empirically. Who even knows what Soar[1] is nowadays? Maybe some marriage of the two paradigms will lead to better results, but I doubt that things will head in that direction anytime soon given how massively far just having parallelizable architectures and adding more parameters has gotten us. [1] https://en.wikipedia.org/wiki/Soar_(cognitive_architecture) https://en.wikipedia.org/wiki/Soar_(cognitive_architecture)

kevindamm 3y ago

hyperparameter tuning does already go some of the way towards learning the compute graph, though very constrained and with a lot more training required.

api 3y ago

Genetic algorithms figured out GI the first time, but it took a while.

mdp2021 3y ago
Could you please expand?
- api 3y ago
  Evolution built our brains. Though to be fair, actual biological evolution is more complex than simple genetic algorithms. More like evolution strategies with meta-parameter-learning and adaptive rate tuning among other things.

mikeknoop 3y ago

Couldn't find your contact info. Email me?

logicchains 3y ago

>The whole "mystery" of transformer is that instead of a linear sequence of static weights times values in each layer, you now have 3 different matrices that are obtained from the same input through multiplication of learned weights, and then you just multiply the matrices together. I.e more parallelism which works out nice, but very restrictive since the attention formula is static. That's not it at all. What's special about transformers is they allow each element in a sequence to decide which parts of data are most important to it from each other element in the sequence, then extract those out and compute on them. The big theoretical advantage over RNNs (which were used for sequences prior to transformers), is that transformers support this in a lossless way, as each element has full access to all the information in every other element in the sequence (or at least all the ones that occurred before it in time sequences). RNNs and "linear transformers" on the other hand compress past values, so generally the last element of a long sequence will not have access to all the information in the first element of the sequence (unless the RNN internal state was really really big so it didn't need to discard any information).

ActorNightly 3y ago
>What's special about transformers is they allow each element in a sequence to decide which parts of data are most important to it from each other element in the sequence, then extract those out and compute on them. They do that in theory. In practice, its just all matrix multiplication. You could easily structure a transformer as a bunch of fully connected deep layers and it would be mathematically equivalent, just computationally inefficient.

cyberpewpew 3y ago

can you explain exactly what you mean by this? I understand what a compute graph is, but I'm not getting the idea of making a learnable parameter.

cyberpewpew 3y ago
nevermind, after looking at my own question 3-4 times it clicked.

lambdatronics 3y ago

>a way to generalize the compute graph as a learnable parameter. Agreed. Seems analogous with how human mental processes are used to solve the kind of problems we'd like LLMs to solve (going beyond "language processing" which transformers do well, to actual reasoning which they can only mimic). Although you risk it becoming a Turing machine by giving it flow control & then training is a problem as you say. Perhaps not intractable though.