4 ms·

Computationally what’s the difference between a human and an LLM besides scale and speed? Trained humans are still using heuristics and shortcuts. Just because

Computationally what’s the difference between a human and an LLM besides scale and speed? Trained humans are still using heuristics and shortcuts. Just because these are subconscious and only possessed by talented, trained professionals doesn’t make the skill computationally special.

Joel_Mckay 25d ago
LLM do not think because they are not real "AI", but it does copy the linear patterns people exhibit if statistically salient within the granularity of the higher dimensional vector search space proximity. Watermarking does skew the compaction slightly, but not far from resulting output patterns. =3 https://en.wikipedia.org/wiki/The_Subservient_Chicken https://en.wikipedia.org/wiki/The_Subservient_Chicken
- adrianvi 25d ago
  I do think that slightly smarter LLMs and a good (although not perfect) autorouting algorithm could solve 99% of hobbyist's projects and simple industry boards, due to the repetition of those patterns in real life. The LLM would learn the heiristics (example: data lines first, power lines later, etc.) and would request the autorouter to do that routing, then take the image output and request a different part (depth-first). If later no routing is posible with that configuration, after some retries it could try another way. It wouldn't solve complex boards, and engineers are always needed for short comings (and even if not, for research), but I wouldn't say this is something different than poetry, music or pixel art, LLMs can imitate although most of the times in a uncanny way.
  - Joel_Mckay 24d ago
    Every trace on a PCB is a traveling salesman problem with ballooning complexity. https://en.wikipedia.org/wiki/Travelling_salesman_problem https://en.wikipedia.org/wiki/Travelling_salesman_problem While physics informed models do exist, they are still going to burn a lot of compute to generate failure modes people didn't know were possible. =3 https://www.youtube.com/watch?v=T4Upf_B9RLQ https://www.youtube.com/watch?v=T4Upf_B9RLQ
    - adrianvi 24d ago
      Yes, the optimal solution won't be achievable, it will burn lots of compute and we need better simulations so that less errors are made (and will be made). Still, if LLMs are capable of writing working code, art and solving math problems, they qre definitely capable of doing suboptimal routing (with some algorithmic aid)
      - Joel_Mckay 24d ago
        >if LLMs are capable of That is the leap in logic, as an LLM doesn't think/create/feel or understand. It simply copies billions of patterns, and finds the closest fit. > writing working code, You mean a compacted collage of stolen codified work from real people. It will require a continuous parasitic relationship of user data to remain coherent. Given the firms have already stolen everything, it is unlikely to improve much as weights are refined. > art Again, a statistical output of salient feature clusters mimicking a humans output is not a creative process. For example, a glass-blower only sees the glowing art-form in its true state before it is annealed into its final form. Or a painter undergoes a creative process to find meaning, and abandons the work when there is nothing left to add. Emotional projection onto an algorithmic output is simply recognizing the millions of peoples aesthetic choices stolen, and compacted with some degree of lost granularity. It is peoples art forms, but no creative thought or intent behind the results. > solving math problems, LLM are very good at context search, but require persistent data streams to mine for weighting relevant vector proximity. Again, everything that could be stolen, has already been scraped. Only user input data remains in a sea of slop, and the process is degenerative given most human chat-users appear to be losing 17% cognitive function. As people discover the intelligence campaign against users is not in their interest.. paying companies to rob you and your friends makes less sense. Being a sentient turnip, I am probably unaware of such things. ymmv =3
        adrianvi 24d ago
        > That is a leap in logic It is not, it doesn't matter if a brain's neurons or a prediction algorithm managed to get the solution if it has done so in a (sort of) reliable way. https://en.wikipedia.org/wiki/Duck_test https://en.wikipedia.org/wiki/Duck_test Errors are made by both machines and humans, so that's not a differenting factor. What is clear is that the human element will be more valued on art as time goes on.
- CamperBob2 25d ago
  What were the chicken's thoughts on the Jacobian conjecture?
  - Joel_Mckay 24d ago
    Finding a consistent pattern by conflating most papers, resolving a similar isomorphic vector search proximity result from an entire field of research. LLM are very good at brute force context search spaces, and people see patterns in nonsense even when it is nebulous. Similar to the Newton's Apple story people are fond of telling each other. =3 https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia
    - CamperBob2 24d ago
      Explain how this is different from what humans do. We all just muddle through life, predicting the next token. That's it. That's all there is. And it's enough.
      - Joel_Mckay 24d ago
        >Explain how this is different from what humans do. The same reason every human understands how Einstein brushed his teeth in the morning. Best regards =3
        CamperBob2 24d ago
        That logic is so goofy, I don't even think the Greeks had a name for it.

Joel_Mckay 25d ago

LLM do not think because they are not real "AI", but it does copy the linear patterns people exhibit if statistically salient within the granularity of the higher dimensional vector search space proximity. Watermarking does skew the compaction slightly, but not far from resulting output patterns. =3 https://en.wikipedia.org/wiki/The_Subservient_Chicken https://en.wikipedia.org/wiki/The_Subservient_Chicken

adrianvi 25d ago
I do think that slightly smarter LLMs and a good (although not perfect) autorouting algorithm could solve 99% of hobbyist's projects and simple industry boards, due to the repetition of those patterns in real life. The LLM would learn the heiristics (example: data lines first, power lines later, etc.) and would request the autorouter to do that routing, then take the image output and request a different part (depth-first). If later no routing is posible with that configuration, after some retries it could try another way. It wouldn't solve complex boards, and engineers are always needed for short comings (and even if not, for research), but I wouldn't say this is something different than poetry, music or pixel art, LLMs can imitate although most of the times in a uncanny way.
- Joel_Mckay 24d ago
  Every trace on a PCB is a traveling salesman problem with ballooning complexity. https://en.wikipedia.org/wiki/Travelling_salesman_problem https://en.wikipedia.org/wiki/Travelling_salesman_problem While physics informed models do exist, they are still going to burn a lot of compute to generate failure modes people didn't know were possible. =3 https://www.youtube.com/watch?v=T4Upf_B9RLQ https://www.youtube.com/watch?v=T4Upf_B9RLQ
  - adrianvi 24d ago
    Yes, the optimal solution won't be achievable, it will burn lots of compute and we need better simulations so that less errors are made (and will be made). Still, if LLMs are capable of writing working code, art and solving math problems, they qre definitely capable of doing suboptimal routing (with some algorithmic aid)
    - Joel_Mckay 24d ago
      >if LLMs are capable of That is the leap in logic, as an LLM doesn't think/create/feel or understand. It simply copies billions of patterns, and finds the closest fit. > writing working code, You mean a compacted collage of stolen codified work from real people. It will require a continuous parasitic relationship of user data to remain coherent. Given the firms have already stolen everything, it is unlikely to improve much as weights are refined. > art Again, a statistical output of salient feature clusters mimicking a humans output is not a creative process. For example, a glass-blower only sees the glowing art-form in its true state before it is annealed into its final form. Or a painter undergoes a creative process to find meaning, and abandons the work when there is nothing left to add. Emotional projection onto an algorithmic output is simply recognizing the millions of peoples aesthetic choices stolen, and compacted with some degree of lost granularity. It is peoples art forms, but no creative thought or intent behind the results. > solving math problems, LLM are very good at context search, but require persistent data streams to mine for weighting relevant vector proximity. Again, everything that could be stolen, has already been scraped. Only user input data remains in a sea of slop, and the process is degenerative given most human chat-users appear to be losing 17% cognitive function. As people discover the intelligence campaign against users is not in their interest.. paying companies to rob you and your friends makes less sense. Being a sentient turnip, I am probably unaware of such things. ymmv =3
      - adrianvi 24d ago
        > That is a leap in logic It is not, it doesn't matter if a brain's neurons or a prediction algorithm managed to get the solution if it has done so in a (sort of) reliable way. https://en.wikipedia.org/wiki/Duck_test https://en.wikipedia.org/wiki/Duck_test Errors are made by both machines and humans, so that's not a differenting factor. What is clear is that the human element will be more valued on art as time goes on.
CamperBob2 25d ago
What were the chicken's thoughts on the Jacobian conjecture?
- Joel_Mckay 24d ago
  Finding a consistent pattern by conflating most papers, resolving a similar isomorphic vector search proximity result from an entire field of research. LLM are very good at brute force context search spaces, and people see patterns in nonsense even when it is nebulous. Similar to the Newton's Apple story people are fond of telling each other. =3 https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia
  - CamperBob2 24d ago
    Explain how this is different from what humans do. We all just muddle through life, predicting the next token. That's it. That's all there is. And it's enough.
    - Joel_Mckay 24d ago
      >Explain how this is different from what humans do. The same reason every human understands how Einstein brushed his teeth in the morning. Best regards =3
      - CamperBob2 24d ago
        That logic is so goofy, I don't even think the Greeks had a name for it.