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.
that’s quite dishonest because LLMs have had all manner of facts pre-trained on it with datacenters all over the world catering to it. If you think it can learn in the real world without many many iterations and it still needs pushing and proding on simple tasks that humans perform then I am not convinced.
It’s like saying a chess playing computer program like stockfish is a good indicator of intelligence because it knows to play chess but you forgot that the human chess players’ expertise was used to train it and understand what makes a good chess program.
That’s the thing with our terminology, we love to anthropomorphize things. It wasn’t a big problem before because most people had enough grasp on reality to understand that when a script makes :-) smile when the result is positive, or :-( smile otherwise, there is no actual mind behind it that can be happy or sad. But now the generator makes convincing enough sequence of words, so people went mad, and this cute terminology doesn’t work anymore.
Reinforcement learning
Bazzinga
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.
that’s quite dishonest because LLMs have had all manner of facts pre-trained on it with datacenters all over the world catering to it. If you think it can learn in the real world without many many iterations and it still needs pushing and proding on simple tasks that humans perform then I am not convinced.
It’s like saying a chess playing computer program like stockfish is a good indicator of intelligence because it knows to play chess but you forgot that the human chess players’ expertise was used to train it and understand what makes a good chess program.
That’s the thing with our terminology, we love to anthropomorphize things. It wasn’t a big problem before because most people had enough grasp on reality to understand that when a script makes :-) smile when the result is positive, or :-( smile otherwise, there is no actual mind behind it that can be happy or sad. But now the generator makes convincing enough sequence of words, so people went mad, and this cute terminology doesn’t work anymore.