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This looks extremely similar to what often happened with chat implementations in 3 and 3.5 era: GPT world generate it's answer and then go on and generate the n

This looks extremely similar to what often happened with chat implementations in 3 and 3.5 era: GPT world generate it's answer and then go on and generate the next input by the user as well.

Jensson 2y ago
It is a bit more uncanny when it uses your voice though.
EGreg 2y ago
I want to know, if LLMs were unleashed on simulating real-world people, how well could they predict everything they are going to say? In battles or business, how well would LLMs predict what people will do? Seems to me that a billion parameters can model decision making by a group of humans pretty well. Especially strategies to psych them out like anticipating everything they try ahead of time, and showing them it’s hopeless (demoralizing) and they may as well turn to using a random oracle than use their own strategies. Would any AI fans who don’t seem to mind anything AI does, be willing to volunteer for this experiment on the Internet?
- visarga 2y ago
  > how well could they predict everything they are going to say? Maybe they can predict preferences, not everything we are going to say
  - EGreg 2y ago
    Would you like to volunteer and test that theory? It’s stronger than a Turing test. You’d be competing against an AI to prove it’s really you and not an AI trained on what you have been posting. As determined by people who have been seeing you post this whole time. Or even more interestingly — after you hit send, we’ll compare to the 5 versions of what the AI predicted you’d say, and calculate the “loss”. If you lose, the AI can take over your account and use it to amass more karma for you (a measurable metric).
    - visarga 2y ago
      Sure, I am doing some work, collecting my writings, preparing them for LLM. For example I could select my own text as positive and a generic one as negative for RLHF. The resulting model should predict when I'd like a piece of text. But it would need retraining to track my changes over time.
causal 2y ago
Makes sense. With local models it's easy to make this happen with poorly defined stop tokens. The user's voice is vectorized somehow, and the model is predicting a series of vectors and won't have a sense of "self" to let it recognize it's own voice vs the user's.

This looks extremely similar to what often happened with chat implementations in 3 and 3.5 era: GPT world generate it's answer and then go on and generate the next input by the user as well.

Jensson 2y ago

It is a bit more uncanny when it uses your voice though.

EGreg 2y ago

I want to know, if LLMs were unleashed on simulating real-world people, how well could they predict everything they are going to say? In battles or business, how well would LLMs predict what people will do? Seems to me that a billion parameters can model decision making by a group of humans pretty well. Especially strategies to psych them out like anticipating everything they try ahead of time, and showing them it’s hopeless (demoralizing) and they may as well turn to using a random oracle than use their own strategies. Would any AI fans who don’t seem to mind anything AI does, be willing to volunteer for this experiment on the Internet?

visarga 2y ago
> how well could they predict everything they are going to say? Maybe they can predict preferences, not everything we are going to say
- EGreg 2y ago
  Would you like to volunteer and test that theory? It’s stronger than a Turing test. You’d be competing against an AI to prove it’s really you and not an AI trained on what you have been posting. As determined by people who have been seeing you post this whole time. Or even more interestingly — after you hit send, we’ll compare to the 5 versions of what the AI predicted you’d say, and calculate the “loss”. If you lose, the AI can take over your account and use it to amass more karma for you (a measurable metric).
  - visarga 2y ago
    Sure, I am doing some work, collecting my writings, preparing them for LLM. For example I could select my own text as positive and a generic one as negative for RLHF. The resulting model should predict when I'd like a piece of text. But it would need retraining to track my changes over time.

causal 2y ago

Makes sense. With local models it's easy to make this happen with poorly defined stop tokens. The user's voice is vectorized somehow, and the model is predicting a series of vectors and won't have a sense of "self" to let it recognize it's own voice vs the user's.