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The branding on this is a bit much, it’s not an operating system. However LLMs are the real deal, some papers claim they are achieving SOTA or significant break

The branding on this is a bit much, it’s not an operating system. However LLMs are the real deal, some papers claim they are achieving SOTA or significant breakthroughs in various domains. Surely they’re computationally intensive, but if you have read the most of the papers out of Berkeley, Microsoft, Meta, Google/DeepMind/Waymo… I think you’d have an change in opinion.

deleted 3y ago
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TerrifiedMouse 3y ago
> However LLMs are the real deal Weird thing is it was designed to model language. It’s surprising that it returns sound answers as often as it does. But that’s also kind of the problem, it’s “surprising”, i.e. we don’t really know what happened. You wouldn’t fly on a jetliner that’s “surprising it flies without disintegrating midair”.
- beoberha 3y ago
  Unless you’re using an LLM to fly a plane, your analogy is a woefully bad comparison.
  - TerrifiedMouse 3y ago
    I guess my point is, if we don’t understand it, we don’t know its failure scenarios.
    - omarfarooq 3y ago
      There's an LLM for that.
    - jackgolding 3y ago
      This is the case with all neural nets/black box AI models and a lot are used in various industries.
    - tayo42 3y ago
      What things are not understood about transformers?
      - aik 3y ago
        All the uses
- s17n 3y ago
  People were pretty surprised by the Wright flyer. Confidence is built by experience, not theoretical understanding.
  - ravetcofx 3y ago
    Science being iterative, they definitely weren't the first to fly, and not even in a heavier than aircraft, what they did acheive was the first time the pilot had 3-axis control, and was the first powered heavier than air manned flight.
- unblough 3y ago
  > Weird thing is it was designed to model language. It’s surprising that it returns sound answers as often as it does. Is this surprising? Can you point to researchers in the field being “surprised” by LLMs returning sound answers? > “surprising”, i.e. we don’t really know what happened. This ie reads like a sort of popsci conclusion. We know exactly what happened. We programmed it to perform these calculations. It’s actually rather straightforward elementary mathematics. But, what happens is so many interdependent calculations grow the complexity of the problem until we are unable to hold it in it our minds, and to analyze its decisions computationally necessitates similar levels of computation for each decision being made as what was used to compute the weights. As for its effectiveness, familiarity with the field of computational complexity points to high dimensional polynomial optimization problems being broadly universal solvers.
  - TerrifiedMouse 3y ago
    > Is this surprising? Can you point to researchers in the field being “surprised” by LLMs returning sound answers? It's surprising because it wasn't the intent of LLMs. LLMs are just predictive models that guess the most likely next word. Having the results make sense was never a priority. Early version, GPT1/2, all return mostly complete nonsense. It was only with GPT3 when the model got large enough that it started returning results that are convincing and might even make sense often enough. Even more mind boggling is the fact that randomness is part of its algorithm, i.e. temperature, and that without it the output is kind of meh.
    - unblough 3y ago
      > It's surprising because it wasn't the intent of LLMs. LLMs are just predictive models that guess the most likely next word. Having the results make sense was never a priority. If you took the same amount of data for the GPT3+ but scrambled it's tokenization before training THEN I would agree with you that its current behaviour is surprising, but the model was fed data that has large swaths that are literal question and answer constructions. It's over fitting behavior is largely why it's parent company is facing so much legal backlash. > Even more mind boggling is the fact that randomness is part of its algorithm The randomness is for token choice rather than any training time tunable so fails to support the "i.e. we don’t really know what happened" sentiment. We do know, we told it to flip a coin, and it did. > i.e. temperature, and that without it the output is kind of meh. Both without it and with it. You can turn up the temperature and get bad results as well as you can turn it down and get bad results. If adding a single additional dimension to the polynomial of the solution space turned a nondeterministic problem into a deterministic one, then yes, I would agree with you, that would be surprising.
- xcv123 3y ago
  > Weird thing is it was designed to model language. Not exactly. They are designed to perform natural language processing (NLP) tasks. That includes understanding language and answering questions.
mistermann 3y ago
Depends what "operating system" means. I'd say things like democracy, marketing/journalism/propaganda, etc are operating systems of some sort, in that they perform orchestration of humans, modify reality, etc. Lack of memory is a big handicap for LLM's if they want to play in that league.

deleted 3y ago

[deleted]

TerrifiedMouse 3y ago

> However LLMs are the real deal Weird thing is it was designed to model language. It’s surprising that it returns sound answers as often as it does. But that’s also kind of the problem, it’s “surprising”, i.e. we don’t really know what happened. You wouldn’t fly on a jetliner that’s “surprising it flies without disintegrating midair”.

beoberha 3y ago
Unless you’re using an LLM to fly a plane, your analogy is a woefully bad comparison.
- TerrifiedMouse 3y ago
  I guess my point is, if we don’t understand it, we don’t know its failure scenarios.
  - omarfarooq 3y ago
    There's an LLM for that.
  - jackgolding 3y ago
    This is the case with all neural nets/black box AI models and a lot are used in various industries.
  - tayo42 3y ago
    What things are not understood about transformers?
    - aik 3y ago
      All the uses
s17n 3y ago
People were pretty surprised by the Wright flyer. Confidence is built by experience, not theoretical understanding.
- ravetcofx 3y ago
  Science being iterative, they definitely weren't the first to fly, and not even in a heavier than aircraft, what they did acheive was the first time the pilot had 3-axis control, and was the first powered heavier than air manned flight.
unblough 3y ago
> Weird thing is it was designed to model language. It’s surprising that it returns sound answers as often as it does. Is this surprising? Can you point to researchers in the field being “surprised” by LLMs returning sound answers? > “surprising”, i.e. we don’t really know what happened. This ie reads like a sort of popsci conclusion. We know exactly what happened. We programmed it to perform these calculations. It’s actually rather straightforward elementary mathematics. But, what happens is so many interdependent calculations grow the complexity of the problem until we are unable to hold it in it our minds, and to analyze its decisions computationally necessitates similar levels of computation for each decision being made as what was used to compute the weights. As for its effectiveness, familiarity with the field of computational complexity points to high dimensional polynomial optimization problems being broadly universal solvers.
- TerrifiedMouse 3y ago
  > Is this surprising? Can you point to researchers in the field being “surprised” by LLMs returning sound answers? It's surprising because it wasn't the intent of LLMs. LLMs are just predictive models that guess the most likely next word. Having the results make sense was never a priority. Early version, GPT1/2, all return mostly complete nonsense. It was only with GPT3 when the model got large enough that it started returning results that are convincing and might even make sense often enough. Even more mind boggling is the fact that randomness is part of its algorithm, i.e. temperature, and that without it the output is kind of meh.
  - unblough 3y ago
    > It's surprising because it wasn't the intent of LLMs. LLMs are just predictive models that guess the most likely next word. Having the results make sense was never a priority. If you took the same amount of data for the GPT3+ but scrambled it's tokenization before training THEN I would agree with you that its current behaviour is surprising, but the model was fed data that has large swaths that are literal question and answer constructions. It's over fitting behavior is largely why it's parent company is facing so much legal backlash. > Even more mind boggling is the fact that randomness is part of its algorithm The randomness is for token choice rather than any training time tunable so fails to support the "i.e. we don’t really know what happened" sentiment. We do know, we told it to flip a coin, and it did. > i.e. temperature, and that without it the output is kind of meh. Both without it and with it. You can turn up the temperature and get bad results as well as you can turn it down and get bad results. If adding a single additional dimension to the polynomial of the solution space turned a nondeterministic problem into a deterministic one, then yes, I would agree with you, that would be surprising.
xcv123 3y ago
> Weird thing is it was designed to model language. Not exactly. They are designed to perform natural language processing (NLP) tasks. That includes understanding language and answering questions.

mistermann 3y ago

Depends what "operating system" means. I'd say things like democracy, marketing/journalism/propaganda, etc are operating systems of some sort, in that they perform orchestration of humans, modify reality, etc. Lack of memory is a big handicap for LLM's if they want to play in that league.