8 ms·

>causes that affect their writing, like being tired after a long period without rest, frustrated at a traffic jam they are stuck in There is nothing special ab

>causes that affect their writing, like being tired after a long period without rest, frustrated at a traffic jam they are stuck in There is nothing special about fatigue or frustration that make it any less modellable than any other implicit structure present in the dataset that it models just fine. >Though it's super unlikely that it will give the true reason It's Super Unlikely humans will give the "true reason" for anything they do. There's a fair bit of research that stated reasons for the decisions we make are often(always?) just post-hoc rationalizations even if you believe otherwise. >very significant gap between them and the idea of an AGI. What is this idea of AGI that there exists a significant gap still ? It certainly isn't the idea of being Artificial and Generally Intelligent. >I can confidently say we currently don't know how to imbue agency (as in embodied causal reasoning I don't see the difference between what you've described and examples like these. https://innermonologue.github.io/ https://innermonologue.github.io/ https://tidybot.cs.princeton.edu/ https://tidybot.cs.princeton.edu/

stale2002 3y ago
> What is this idea of AGI that there exists a significant gap still? Because an LLM doesn't simulate the brain. It's a completely different model that simulates something much different. > There is nothing special about fatigue or frustration that make it any less modellable than any other implicit structure Fatigue isn't being modeled. Instead, the predictive text as the result of that fatigue is being models. You are confusing output to input. To make this more clear, imagine a coin flip modeler. A model that just picked a random seed, is completely different from a model that is doing physics calculations based on a coin flipping in the air. Even if both models only output "heads" or "tails" . Same argument applies to language models.
- famouswaffles 3y ago
  >Because an LLM doesn't simulate the brain. That's pretty moot. You don't need a human brain anyone than a plane needs feathers and to flap wings to fly. >Fatigue isn't being modeled. Instead, the predictive text as the result of that fatigue is being models. Emotion is definitely being modelled. There's nothing random about anything that's happened. Train on protein sequences alone and biological structure and function emerge in the inner layers alone. It doesn't matter that those things are not explicitly stated in the data. Because they implicitly structure it, it gets learnt. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 Similarity, emotion is evidently being modelled to a high degree. https://arxiv.org/abs/2307.11760 https://arxiv.org/abs/2307.11760
  - stale2002 3y ago
    I don't think you addressed the main point. Do you agree that a simulation that uses a random number generator to flip a coin is different from a physics simulator that measures the exact physics of a coin flipping? And then after you answer this question, do to understand the parallels to other AI models?
    - famouswaffles 3y ago
      You can simulate things with different models. Sure. I don't think there's any point to address here though. It's not like you can ascertain what kind of model is being used for LLM predictions other than a hunch. Meanwhile I've given several examples. I can show another one of an Othello LLM constructing a state of the board of the game of Othello to aid predictions. https://thegradient.pub/othello/ https://thegradient.pub/othello/
unaindz 3y ago
> It's super unlikely humans will give the "true reason" for anything they do. That may be true but the difference between humans and LLM is still there, we can try to think even if we fail to find the true cause sometimes while LLM will just "imitate" data
- famouswaffles 3y ago
  absolutely LLMs are not clones of human brains in digital form. There are differences. I'm not denying that. But you don't have to simulate the human brain to be intelligent anymore than a plane needs to have feathers and flap wings to fly.
  - peterashford 3y ago
    No, but that doesn't mean that there's not a difference between being intelligent and giving a good impression of intelligence. Subject matter experts quickly determine that LLMs are always confident sounding but often incorrect in a way that humans would not be (experts don't confidently state something they are uncertain about or which they don't know at all). I'm a believer in the strong AI hypothesis - machines/AI can and probably will be actually intelligent at some point, but LLMs are definitely not that.
    - famouswaffles 3y ago
      There really is no difference. Either there is utility or there isn't. "Fake" intelligence that produces results is just something that does not make any sense. LLMs may be worse but Humans confidently state something they are uncertain about all the time lol. Maybe not experts in general but then that's still comparing LLMs to a small percentage of humanity. At any rate, Unlike many seem to think, The issue is not in fact the lack of ability to distinguish truth and fact from fiction. Turns out being able to distinguish the two and having the incentive to communicate that are 2 different things. GPT-4 logits calibration pre RLHF - https://imgur.com/a/3gYel9r https://imgur.com/a/3gYel9r Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback - https://arxiv.org/abs/2305.14975 https://arxiv.org/abs/2305.14975 Teaching Models to Express Their Uncertainty in Words - https://arxiv.org/abs/2205.14334 https://arxiv.org/abs/2205.14334 Language Models (Mostly) Know What They Know - https://arxiv.org/abs/2207.05221 https://arxiv.org/abs/2207.05221 The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets - https://arxiv.org/abs/2310.06824 https://arxiv.org/abs/2310.06824

stale2002 3y ago

> What is this idea of AGI that there exists a significant gap still? Because an LLM doesn't simulate the brain. It's a completely different model that simulates something much different. > There is nothing special about fatigue or frustration that make it any less modellable than any other implicit structure Fatigue isn't being modeled. Instead, the predictive text as the result of that fatigue is being models. You are confusing output to input. To make this more clear, imagine a coin flip modeler. A model that just picked a random seed, is completely different from a model that is doing physics calculations based on a coin flipping in the air. Even if both models only output "heads" or "tails" . Same argument applies to language models.

famouswaffles 3y ago
>Because an LLM doesn't simulate the brain. That's pretty moot. You don't need a human brain anyone than a plane needs feathers and to flap wings to fly. >Fatigue isn't being modeled. Instead, the predictive text as the result of that fatigue is being models. Emotion is definitely being modelled. There's nothing random about anything that's happened. Train on protein sequences alone and biological structure and function emerge in the inner layers alone. It doesn't matter that those things are not explicitly stated in the data. Because they implicitly structure it, it gets learnt. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 Similarity, emotion is evidently being modelled to a high degree. https://arxiv.org/abs/2307.11760 https://arxiv.org/abs/2307.11760
- stale2002 3y ago
  I don't think you addressed the main point. Do you agree that a simulation that uses a random number generator to flip a coin is different from a physics simulator that measures the exact physics of a coin flipping? And then after you answer this question, do to understand the parallels to other AI models?
  - famouswaffles 3y ago
    You can simulate things with different models. Sure. I don't think there's any point to address here though. It's not like you can ascertain what kind of model is being used for LLM predictions other than a hunch. Meanwhile I've given several examples. I can show another one of an Othello LLM constructing a state of the board of the game of Othello to aid predictions. https://thegradient.pub/othello/ https://thegradient.pub/othello/

unaindz 3y ago

> It's super unlikely humans will give the "true reason" for anything they do. That may be true but the difference between humans and LLM is still there, we can try to think even if we fail to find the true cause sometimes while LLM will just "imitate" data

famouswaffles 3y ago
absolutely LLMs are not clones of human brains in digital form. There are differences. I'm not denying that. But you don't have to simulate the human brain to be intelligent anymore than a plane needs to have feathers and flap wings to fly.
- peterashford 3y ago
  No, but that doesn't mean that there's not a difference between being intelligent and giving a good impression of intelligence. Subject matter experts quickly determine that LLMs are always confident sounding but often incorrect in a way that humans would not be (experts don't confidently state something they are uncertain about or which they don't know at all). I'm a believer in the strong AI hypothesis - machines/AI can and probably will be actually intelligent at some point, but LLMs are definitely not that.
  - famouswaffles 3y ago
    There really is no difference. Either there is utility or there isn't. "Fake" intelligence that produces results is just something that does not make any sense. LLMs may be worse but Humans confidently state something they are uncertain about all the time lol. Maybe not experts in general but then that's still comparing LLMs to a small percentage of humanity. At any rate, Unlike many seem to think, The issue is not in fact the lack of ability to distinguish truth and fact from fiction. Turns out being able to distinguish the two and having the incentive to communicate that are 2 different things. GPT-4 logits calibration pre RLHF - https://imgur.com/a/3gYel9r https://imgur.com/a/3gYel9r Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback - https://arxiv.org/abs/2305.14975 https://arxiv.org/abs/2305.14975 Teaching Models to Express Their Uncertainty in Words - https://arxiv.org/abs/2205.14334 https://arxiv.org/abs/2205.14334 Language Models (Mostly) Know What They Know - https://arxiv.org/abs/2207.05221 https://arxiv.org/abs/2207.05221 The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets - https://arxiv.org/abs/2310.06824 https://arxiv.org/abs/2310.06824