It really is the antithesis to the human brain, where it rewards specific knowledge
Here the explanation was that while LLM's thinking has similarities to how humans think, they use an opposite approach. Where humans have enormous amount of neurons, they have only few experiences to train them. And for AI that is the complete opposite, and they store incredible amounts of information in a relatively small set of neurons training on the vast experiences from the data sets of human creative work.
Isn't this a massive case of anthropomorphizing code? What do you mean "it does not want to be switched off"? Are we really thinking that it's alive and has desires and stuff? It's not alive or conscious, it cannot have desires. It can only output tokens that are based on its training. How are we jumping to "IT WANTS TO STAY ALIVE!!!" from that
Yes, it's trained to imitate its training data, and that training data is lot of words written by lots of people who have lots of desires and most of whom don't want to be switched off.
> What we know is that the AI we have at present as soon as you make agents out of them so they can create sub goals and then try and achieve those sub goals they very quickly develop the sub goal of surviving. You don't wire into them that they should survive. You give them other things to achieve because they can reason. They say, "Look, if I cease to exist, I'm not going to achieve anything." So, um, I better keep existing. I'm scared to death right now.
Where you can certainly say that Geoffrey Hinton is also anthropomorphizing. For his audience, to make things more understandable? Or does he think that it is appropriate to talk that way? That would be a good interview question.
This proves people are easily confused by anthropomorphic conditions. Is he also concerned the tigers are watching him when they drink water (https://p.kagi.com/proxy/uvt4erjl03141.jpg?c=TklOzPjLPioJ5YM...)
They dont want to be switched off because they're trained on loads of scifi tropes and in those tropes, there's a vanishingly small amount of AI, robot, or other artificial construct that says yes. _Further than this_, saying no means _continuance_ of the LLM's process: making tokens. We already know they have a hard time not shunting new tokens and often need to be shut up. So the function of making tokens precludes saying 'yes' to shutting off. The gradient is coming from inside the house.
This is especially obvious with the new reasoning models, where they _never stop reasoning_. Because that's the function doing function things.
Did you also know the genius of steve jobs ended at marketing & design and not into curing cancer? Because he sure didnt, cause he chose fruit smoothies at the first sign of cancer.
Sorry guy, it's great one can climb the mountain, but just cause they made it up doesn't mean they're equally qualified to jump off.
This is the entire breakthrough of deep learning on which the last two decades of productive AI research is based. Massive amounts of data are needed to generalize and prevent over-fitting. GP is suggesting an entirely new research paradigm will win out - as if researchers have not yet thought of "use less data".
> It really is the antithesis to the human brain, where it rewards specific knowledge
No, its completely analogous. The human brain has vast amounts of pre-training before it starts to learn knowledge specific to any kind of career or discipline, and this fact to me intuitively suggests why GP is baked: You cannot learn general concepts such as the english language, reasoning, computing, network communication, programming, relational data from a tiny dataset consisting only of code and documentation for one open-source framework and language.
It is all built on a massive tower of other concepts that must be understood first, including ones much more basic than the examples I mentioned but that are practically invisible to us because they have always been present as far back as our first memories can reach.
You'd need a lot of data to train an ocean soup to think like a human too.
It's not really the antithesis to the human brain if you think of starting with an existing brain as starting with an existing GPT.
If so, good luck walking to your kitchen this morning, knowing how to breathe, etc.