9 ms·

This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. I'm no expert in the matter, but

This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. I'm no expert in the matter, but for "holistic" things (where there are a lot of cross-connections and inter-dependencies) it feels like a diffusion-based generative structure would be better-suited than next-token-prediction. I've felt this way about poetry-generation, and I feel like it might apply in these sorts of cases as well. Additionally, this is a highly-specialized field. From the conclusion of the article: > Overall we have some promising directions. Using LLMs for circuit board design looks a lot like using them for other complex tasks. They work well for pulling concrete data out of human-shaped data sources, they can do slightly more difficult tasks if they can solve that task by writing code, but eventually their capabilities break down in domains too far out of the training distribution. > We only tested the frontier models in this work, but I predict similar results from the open-source Llama or Mistral models. Some fine tuning on netlist creation would likely make the generation capabilities more useful. I agree with the authors here. While it's nice to imagine that AGI would be able to generalize skills to work competently in domain-specific tasks, I think this shows very clearly that we're not there yet, and if one wants to use LLMs in such an area, one would need to fine-tune for it. Would like to see round 2 of this made using a fine-tuning approach.

DHaldane 2y ago
My gut agrees with you that LLMs shouldn't do this well on a specialty domain. But I think there's also the bitter lesson to be learned here: many times people say LLMs won't do well on a task, they are often surprised either immediately or a few months later. Overall not sure what to expect, but fine tuning experiments would be interesting regardless.
- cjk2 2y ago
  I doubt it'd work any better. Most of EE time I have spent is swearing at stuff that looked like it'd work on paper but didn't due to various nuances. I have my own library of nuances but how would you even fine tune anything to understand the black box abstraction of an IC to work out if a nuance applies or not between it and a load or what a transmission line or edge would look like between the IC and the load? This is where understanding trumps generative AI instantly.
  - DHaldane 2y ago
    I doubt it too, but I notice that I keep underestimating the models. Do you have a challenge task I can try? What's the easiest thing I could get an LLM to do for circuit board design that would surprise you?
    - cjk2 2y ago
      Make two separate signals arrive at exactly the same time on two 50 ohm transmission lines that start and end next to each other and go around a right hand bend. At 3.8GHz. Edit: no VSWR constraint. Can add that later :) Edit 2: oh or design a board for a simple 100Mohm input instrumentation amplifier which knows what a guard ring is and how badly the solder mask will screw it up :)
      - bmicraft 2y ago
        It would seem to me that the majority of boards would be a lot more forgiving. Are you saying you wouldn't be impressed if it could do only say 70% of board designs completely?
        AdamH12113 2y ago
        Not the GP, but as an EE I can tell you that the majority of boards are not forgiving. One bad connection or one wrong component often means the circuit just doesn't work. One bad footprint often means the board is worthless. On top of that, making an AI that can regurgitate simple textbook circuits and connect them together in reasonable ways is only the first step towards a much more difficult goal. More subtle problems in electronics design are all about context-dependent interactions between systems.
        nurple 2y ago
        I hate that this is true. I think ML itself could be applied to the problem to help you catch mistakes in realtime, like language servers in software eng. I have experience building boards in Altium and found it rather enjoyable; my own knowledge was often a constraint as I started out, but once I got proficient it just seemed to flow out onto the canvas. There are some design considerations that would be awesome to farm out to genai, but I think we are far from that. Like stable-diffusion is to images, the source data for text-to-PCB would need to be well-labeled in addition to being correllated with the physical PCB features themselves. The part where I think we lose a lot of data in pursuit of something like this, is all of the research and integration work that went on behind everything that eventually got put into the schematic and then laid out on a board. I think it would be really difficult to "diffuse" a finished PCB from an RFQ-level description.
- sweezyjeezy 2y ago
  Some research to the contrary [1] - tldr is that they didn't find evidence that generative models really do zero shot well at all yet, if you show it something it literally hasn't seen before, it isn't "generally intelligent" enough to do it well. This isn't an issue for a lot of use-cases, but does seem to add some weight to the "giga-scale memorization" hypothesis. [1] https://arxiv.org/html/2404.04125v2 https://arxiv.org/html/2404.04125v2
- HanClinto 2y ago
  > But I think there's also the bitter lesson to be learned here: many times people say LLMs won't do well on a task, they are often surprised either immediately or a few months later. Heh. This is very true. I think perhaps the thing I'm most amazed by is that simple next-token prediction seems to work unreasonably well for a great many tasks. I just don't know how well that will scale into more complex tasks. With simple next-token prediction there is little mechanism for the model to iterate or to revise or refine as it goes. There have been some experiments with things like speculative generation (where multiple branches are evaluated in parallel) to give a bit of a lookahead effect and help avoid the LLM locking itself into dead-ends, but they don't seem super popular overall -- people just prefer to increase the power and accuracy of the base model and keep chugging forward. I can't help feeling like a fundamental shift something more akin to a diffusion-based approach would be helpful for such things. I just want some sort of mechanism where the model can "think" longer about harder problems. If you present a simple chess board to an LLM or a complex board to an LLM and ask it to generate the next move, it always responds in the same amount of time. That alone should tell us that LLMs are not intelligent, and they are not "thinking", and they will be insufficient for this going forward. I believe Yann LeCun is right -- simply scaling LLMs is not going to get us to AGI. We need a fundamental structural shift to something new, but until we stop seeing such insane advancements in the quality of generation with LLMs (looking at you, Claude!!), I don't think we will move beyond. We have to get bored with LLMs first.
  - pton_xd 2y ago
    > If you present a simple chess board to an LLM or a complex board to an LLM and ask it to generate the next move, it always responds in the same amount of time. Is that true, especially if you ask it to think step-by-step? I would think the model has certain associations for simple/common board states and different ones for complex/uncommon states, and when you ask it to think step-by-step it will explain the associations with a particular state. That "chattiness" may lead it to using more computation for complex boards.
    - HanClinto 2y ago
      > > If you present a simple chess board to an LLM or a complex board to an LLM and ask it to generate the next move, it always responds in the same amount of time. > Is that true, especially if you ask it to think step-by-step? That's fair -- there's a lot of room to grow in this area. If the LLM has been trained to operate with running internal-monologue, then I believe they will operate better. I think this definitely needs to be explored more -- from what little I understand of this research, the results are sporadically promising, but getting something like ReAct (or other, similar structures) to work consistently is something I don't think I've seen yet.
- anonymoushn 2y ago
  We have 0 y/o/y progress on Advent of Code, for example. Maybe we'll have some progress 6 months from now :) https://www.themotte.org/post/797/chatgpt-vs-advent-of-code https://www.themotte.org/post/797/chatgpt-vs-advent-of-code
  - DHaldane 2y ago
    Have you tried using more 4000x more samples? https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt https://redwoodresearch.substack.com/p/getting-50-sota-on-ar...
eimrine 2y ago
I like how you called it holistic, it is maybe the first time I see this word not in a "bad" context. What about the topic, it is impossible to synthesize STEM things not in the manner an engineer does this. I mean thou shalt to know some typical solutions and have all the calculations for all what's happening in the schematic being developed. Textbooks are not a joke and no matter who are you - a human or a device.
omgJustTest 2y ago
I asked this question of Duncan Dec 22! If you are interested I highly recommend this + your favorite llm. It does not do everything but is far superior to some highly expensive tools, in flexibility and repeatability. https://github.com/devbisme/skidl https://github.com/devbisme/skidl
- HanClinto 2y ago
  This tool looks really powerful, thanks for the link! One thing I've been personally really intrigued by is the possibility of using self-play and adversarial learning as a way to advance beyond our current stage of imitation-only LLMs. Having a strong rules-based framework to be able to be able to measure quality and correctness of solutions is necessary for any RL training setup to proceed. I think that skidl could be a really nice framework to be part of an RL-trained LLM's curriculum! I've written down a bunch of thoughts [1] on using games or code-generation in an adversarial training setup, but I could see circuit design being a good training ground as well! * [1] https://github.com/HanClinto/MENTAT https://github.com/HanClinto/MENTAT
  - deleted 2y ago
    [deleted]
hoosieree 2y ago
I agree diffusion makes more sense for optimizing code-like things. The tricky part is coming up with a reasonable set of "add noise" transformations.
- HanClinto 2y ago
  > The tricky part is coming up with a reasonable set of "add noise" transformations. Yes, as well as dealing with a variable-length window. When generating images with diffusion, one specifies the image ahead-of-time. When generating text with diffusion, it's a bit more open-ended. How long do we want this paragraph to go? Well, that depends on what goes into it -- so how do we adjust for that? Do we use a hierarchical tree-structure approach? Chunk it and do a chain of overlapping segments that are all of fixed-length (could possibly be combined with a transformer model)? Hard to say what would finally work in the end, but I think this is the sort of thing that YLC is talking about when he encourages students to look beyond LLMs. [1] * [1] https://x.com/ylecun/status/1793326904692428907 https://x.com/ylecun/status/1793326904692428907
surfingdino 2y ago
> This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. There is one posted on HN every week. How many more do we need to accept the fact this tech is not what it is sold at and we are bored waiting for it get good? I am not say "get better", because it keeps getting better, but somehow doesn't get good.
- makk 2y ago
  That’s a perception and the problem isn’t the AI it’s human nature: 1. every time AI is able to do a thing we move the goalposts and say, yeah, but it can’t do that other thing over there; 2. We are impatient, so our ability to get bored tends to outpace the rate of change.
  - slg 2y ago
    I don’t think the problem is moving the goalposts, but rather there are no actual goalposts. Advocates for this technology imply it can do anything either because they believe it will be true in the near future or they just want others to believe it for a wide range of reasons including to get rich of it. Therefore the general public has no real idea what the ideal use cases are for this technology in its current state so they keep asking it to do stuff it can’t do well. It is really no different than the blockchain in that regard.
    - surfingdino 2y ago
      One of the main issues I see amongst advocates of AI is that they cannot quantify the benefits and ignore provable failings of AI.
  - goatlover 2y ago
    The other side of this coin is everyone overhyping what AI can do, and when the inevitable criticism comes, they respond by claiming the goal posts are being moved. Perhaps, but you also told me it could do XYZ, when it can only do X and some Y, but not much Z, and it’s still not general intelligence in the he broad sense.
yousif_123123 2y ago
One downside for diffusion based systems (and I'm very noob in this) is that the model won't be able to see it's input and output in the same space, therefore wouldn't be able to do follow-up instructions to fix things or improve on it. Where as an LLM generating html could follow instructions to modify it as well. It's input and output are the same format.
- HanClinto 2y ago
  Oh? I would think that the input prompt to drive generation is not lost during generation iterations -- but I also don't know much about the architectural details.
deleted 2y ago
[deleted]

DHaldane 2y ago

My gut agrees with you that LLMs shouldn't do this well on a specialty domain. But I think there's also the bitter lesson to be learned here: many times people say LLMs won't do well on a task, they are often surprised either immediately or a few months later. Overall not sure what to expect, but fine tuning experiments would be interesting regardless.

cjk2 2y ago
I doubt it'd work any better. Most of EE time I have spent is swearing at stuff that looked like it'd work on paper but didn't due to various nuances. I have my own library of nuances but how would you even fine tune anything to understand the black box abstraction of an IC to work out if a nuance applies or not between it and a load or what a transmission line or edge would look like between the IC and the load? This is where understanding trumps generative AI instantly.
- DHaldane 2y ago
  I doubt it too, but I notice that I keep underestimating the models. Do you have a challenge task I can try? What's the easiest thing I could get an LLM to do for circuit board design that would surprise you?
  - cjk2 2y ago
    Make two separate signals arrive at exactly the same time on two 50 ohm transmission lines that start and end next to each other and go around a right hand bend. At 3.8GHz. Edit: no VSWR constraint. Can add that later :) Edit 2: oh or design a board for a simple 100Mohm input instrumentation amplifier which knows what a guard ring is and how badly the solder mask will screw it up :)
    - bmicraft 2y ago
      It would seem to me that the majority of boards would be a lot more forgiving. Are you saying you wouldn't be impressed if it could do only say 70% of board designs completely?
      - AdamH12113 2y ago
        Not the GP, but as an EE I can tell you that the majority of boards are not forgiving. One bad connection or one wrong component often means the circuit just doesn't work. One bad footprint often means the board is worthless. On top of that, making an AI that can regurgitate simple textbook circuits and connect them together in reasonable ways is only the first step towards a much more difficult goal. More subtle problems in electronics design are all about context-dependent interactions between systems.
        nurple 2y ago
        I hate that this is true. I think ML itself could be applied to the problem to help you catch mistakes in realtime, like language servers in software eng. I have experience building boards in Altium and found it rather enjoyable; my own knowledge was often a constraint as I started out, but once I got proficient it just seemed to flow out onto the canvas. There are some design considerations that would be awesome to farm out to genai, but I think we are far from that. Like stable-diffusion is to images, the source data for text-to-PCB would need to be well-labeled in addition to being correllated with the physical PCB features themselves. The part where I think we lose a lot of data in pursuit of something like this, is all of the research and integration work that went on behind everything that eventually got put into the schematic and then laid out on a board. I think it would be really difficult to "diffuse" a finished PCB from an RFQ-level description.
sweezyjeezy 2y ago
Some research to the contrary [1] - tldr is that they didn't find evidence that generative models really do zero shot well at all yet, if you show it something it literally hasn't seen before, it isn't "generally intelligent" enough to do it well. This isn't an issue for a lot of use-cases, but does seem to add some weight to the "giga-scale memorization" hypothesis. [1] https://arxiv.org/html/2404.04125v2 https://arxiv.org/html/2404.04125v2
HanClinto 2y ago
> But I think there's also the bitter lesson to be learned here: many times people say LLMs won't do well on a task, they are often surprised either immediately or a few months later. Heh. This is very true. I think perhaps the thing I'm most amazed by is that simple next-token prediction seems to work unreasonably well for a great many tasks. I just don't know how well that will scale into more complex tasks. With simple next-token prediction there is little mechanism for the model to iterate or to revise or refine as it goes. There have been some experiments with things like speculative generation (where multiple branches are evaluated in parallel) to give a bit of a lookahead effect and help avoid the LLM locking itself into dead-ends, but they don't seem super popular overall -- people just prefer to increase the power and accuracy of the base model and keep chugging forward. I can't help feeling like a fundamental shift something more akin to a diffusion-based approach would be helpful for such things. I just want some sort of mechanism where the model can "think" longer about harder problems. If you present a simple chess board to an LLM or a complex board to an LLM and ask it to generate the next move, it always responds in the same amount of time. That alone should tell us that LLMs are not intelligent, and they are not "thinking", and they will be insufficient for this going forward. I believe Yann LeCun is right -- simply scaling LLMs is not going to get us to AGI. We need a fundamental structural shift to something new, but until we stop seeing such insane advancements in the quality of generation with LLMs (looking at you, Claude!!), I don't think we will move beyond. We have to get bored with LLMs first.
- pton_xd 2y ago
  > If you present a simple chess board to an LLM or a complex board to an LLM and ask it to generate the next move, it always responds in the same amount of time. Is that true, especially if you ask it to think step-by-step? I would think the model has certain associations for simple/common board states and different ones for complex/uncommon states, and when you ask it to think step-by-step it will explain the associations with a particular state. That "chattiness" may lead it to using more computation for complex boards.
  - HanClinto 2y ago
    > > If you present a simple chess board to an LLM or a complex board to an LLM and ask it to generate the next move, it always responds in the same amount of time. > Is that true, especially if you ask it to think step-by-step? That's fair -- there's a lot of room to grow in this area. If the LLM has been trained to operate with running internal-monologue, then I believe they will operate better. I think this definitely needs to be explored more -- from what little I understand of this research, the results are sporadically promising, but getting something like ReAct (or other, similar structures) to work consistently is something I don't think I've seen yet.
anonymoushn 2y ago
We have 0 y/o/y progress on Advent of Code, for example. Maybe we'll have some progress 6 months from now :) https://www.themotte.org/post/797/chatgpt-vs-advent-of-code https://www.themotte.org/post/797/chatgpt-vs-advent-of-code
- DHaldane 2y ago
  Have you tried using more 4000x more samples? https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt https://redwoodresearch.substack.com/p/getting-50-sota-on-ar...

eimrine 2y ago

I like how you called it holistic, it is maybe the first time I see this word not in a "bad" context. What about the topic, it is impossible to synthesize STEM things not in the manner an engineer does this. I mean thou shalt to know some typical solutions and have all the calculations for all what's happening in the schematic being developed. Textbooks are not a joke and no matter who are you - a human or a device.

omgJustTest 2y ago

I asked this question of Duncan Dec 22! If you are interested I highly recommend this + your favorite llm. It does not do everything but is far superior to some highly expensive tools, in flexibility and repeatability. https://github.com/devbisme/skidl https://github.com/devbisme/skidl

HanClinto 2y ago
This tool looks really powerful, thanks for the link! One thing I've been personally really intrigued by is the possibility of using self-play and adversarial learning as a way to advance beyond our current stage of imitation-only LLMs. Having a strong rules-based framework to be able to be able to measure quality and correctness of solutions is necessary for any RL training setup to proceed. I think that skidl could be a really nice framework to be part of an RL-trained LLM's curriculum! I've written down a bunch of thoughts [1] on using games or code-generation in an adversarial training setup, but I could see circuit design being a good training ground as well! * [1] https://github.com/HanClinto/MENTAT https://github.com/HanClinto/MENTAT
- deleted 2y ago
  [deleted]

hoosieree 2y ago

I agree diffusion makes more sense for optimizing code-like things. The tricky part is coming up with a reasonable set of "add noise" transformations.

HanClinto 2y ago
> The tricky part is coming up with a reasonable set of "add noise" transformations. Yes, as well as dealing with a variable-length window. When generating images with diffusion, one specifies the image ahead-of-time. When generating text with diffusion, it's a bit more open-ended. How long do we want this paragraph to go? Well, that depends on what goes into it -- so how do we adjust for that? Do we use a hierarchical tree-structure approach? Chunk it and do a chain of overlapping segments that are all of fixed-length (could possibly be combined with a transformer model)? Hard to say what would finally work in the end, but I think this is the sort of thing that YLC is talking about when he encourages students to look beyond LLMs. [1] * [1] https://x.com/ylecun/status/1793326904692428907 https://x.com/ylecun/status/1793326904692428907

surfingdino 2y ago

> This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. There is one posted on HN every week. How many more do we need to accept the fact this tech is not what it is sold at and we are bored waiting for it get good? I am not say "get better", because it keeps getting better, but somehow doesn't get good.

makk 2y ago
That’s a perception and the problem isn’t the AI it’s human nature: 1. every time AI is able to do a thing we move the goalposts and say, yeah, but it can’t do that other thing over there; 2. We are impatient, so our ability to get bored tends to outpace the rate of change.
- slg 2y ago
  I don’t think the problem is moving the goalposts, but rather there are no actual goalposts. Advocates for this technology imply it can do anything either because they believe it will be true in the near future or they just want others to believe it for a wide range of reasons including to get rich of it. Therefore the general public has no real idea what the ideal use cases are for this technology in its current state so they keep asking it to do stuff it can’t do well. It is really no different than the blockchain in that regard.
  - surfingdino 2y ago
    One of the main issues I see amongst advocates of AI is that they cannot quantify the benefits and ignore provable failings of AI.
- goatlover 2y ago
  The other side of this coin is everyone overhyping what AI can do, and when the inevitable criticism comes, they respond by claiming the goal posts are being moved. Perhaps, but you also told me it could do XYZ, when it can only do X and some Y, but not much Z, and it’s still not general intelligence in the he broad sense.

yousif_123123 2y ago

One downside for diffusion based systems (and I'm very noob in this) is that the model won't be able to see it's input and output in the same space, therefore wouldn't be able to do follow-up instructions to fix things or improve on it. Where as an LLM generating html could follow instructions to modify it as well. It's input and output are the same format.

HanClinto 2y ago
Oh? I would think that the input prompt to drive generation is not lost during generation iterations -- but I also don't know much about the architectural details.

deleted 2y ago

[deleted]