No thinking is not the same as no actions, we had bots in games for decades and that bots look like they act reasonably but there never was any thinking.
I feel like ‘a lot of agency’ is wrong as there is no agency, but it doesn’t mean that an LLM in a looped setup can’t arrive to these actions and perform them. It doesn’t require neither agency, nor thinking
That’s leaving out vital information however. Certain types of brains (e.g. mammal brains) can derive abstract understanding of relationships from reinforcement learning. A LLM that is trained on “letting go of a stone makes it fall to the ground” will not be able to predict what “letting go of a stick” will result in. Unless it is trained on thousands of other non-stick objects also falling to the ground, in which case it will also tell you that letting go of a gas balloon will make it fall to the ground.
Well that seems like a pretty easy hypothesis to test. Why don’t you log on to chatgpt and ask it what will happen if you let go of a helium balloon? Your hypothesis is it’ll say the balloon falls, so prove it.
You seem very confident in this position. Can you share where you draw this confidence from? Was there a source that especially impressed upon you the impossibility of context comprehension in modern transformers?
If we’re concerned about misconceptions and misinformation, it would be helpful to know what informs your surety that your own position about the impossibility of modeling that kind of complexity is correct.
As has been pointed out to you, there is no thinking involved in an LLM. No context comprehension. Please don’t spread this misconception.
Edit: a typo
No thinking is not the same as no actions, we had bots in games for decades and that bots look like they act reasonably but there never was any thinking.
I feel like ‘a lot of agency’ is wrong as there is no agency, but it doesn’t mean that an LLM in a looped setup can’t arrive to these actions and perform them. It doesn’t require neither agency, nor thinking
Reinforcement learning
That’s leaving out vital information however. Certain types of brains (e.g. mammal brains) can derive abstract understanding of relationships from reinforcement learning. A LLM that is trained on “letting go of a stone makes it fall to the ground” will not be able to predict what “letting go of a stick” will result in. Unless it is trained on thousands of other non-stick objects also falling to the ground, in which case it will also tell you that letting go of a gas balloon will make it fall to the ground.
Well that seems like a pretty easy hypothesis to test. Why don’t you log on to chatgpt and ask it what will happen if you let go of a helium balloon? Your hypothesis is it’ll say the balloon falls, so prove it.
This is not the gotcha that you think it is. Now stop wasting my time.
You seem very confident in this position. Can you share where you draw this confidence from? Was there a source that especially impressed upon you the impossibility of context comprehension in modern transformers?
If we’re concerned about misconceptions and misinformation, it would be helpful to know what informs your surety that your own position about the impossibility of modeling that kind of complexity is correct.
Bad bot