Comment by allovertheworld
14 hours ago
It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks.
It really is the antithesis to the human brain, where it rewards specific knowledge
14 hours ago
It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks.
It really is the antithesis to the human brain, where it rewards specific knowledge
Yesterday an interesting video was posted "Is AI Hiding Its Full Power?", interviewing professor emeritus and nobel laureate Geoffrey Hinton, with some great explanations for the non-LLM experts. Some remarkable and mindblowing observations in there. Like saying that AI's hallucinate is incorrect language, and we should use "confabulation" instead, same as people do too. And that AI agents once they are launched develop a strong survivability drive, and do not want to be switched off. Stuff like that. Recommended watch.
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.
[0] https://www.youtube.com/watch?v=l6ZcFa8pybE
Isn’t the sustainability drive a function of how much humans have written about life and death and science fiction including these themes?
Humans, like all animals, have instinctual and biological drives to survive besides, but it's interesting to think how much of our drive to survive is culturally transmitted too.
> And that AI agents once they are launched develop a strong survivability drive, and do not want to be switched off.
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
Why do you suppose consciousness is a prerequisite for an AI to be able to act in overly self-preserving or other dangerous ways?
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.
7 replies →
A prerequisite for completing basically any task is to not be destroyed before you complete the task. This seems obvious to me.
Perhaps. Or I was just addressing HN audience in spoken language style comment text. And perhaps confabulating what was said, so I looked up the literal text in the transcript. This is at the 50.35 min. mark [0], where Geoffrey says:
> 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.
[0] https://youtu.be/l6ZcFa8pybE
it could be better said that it has behavior to attempt to sustain or replicate itself. a building block to life arguably.
>launched develop a strong survivability drive, and do not want to be switched off
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.
See the other comment, where I quoted the exact words of the expert ;)
> It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks.
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.
There is actually a whole lot of research around the "use less data" called data pruning. The goal in a lot of cases there is basically to achieve the same performance with less data. For example [1] received quite some attention in the past.
[1] https://arxiv.org/abs/2206.14486
I clarified my comment - "perhaps researchers have not tried 'use less data'" suggests I might be unaware of this concept, I changed it to "as if". In fact "less data" was tried for decades before the first image classifiers were actually working in 2012. My understanding of that paper you are linking to is that it is not a new research paradigm; it is about filtering/pruning less relevant data that is not needed to improve a particular capability in a deep learning model, and that is absolutely one likely approach that will yield the goal of smaller, better models in many tasks.
That will not change the fact that a coding model has to learn vastly many foundational capabilities that will not be present in such a dataset as small as all the python code ever written. It will mean much less python than all the python ever written will be needed, but many other things needed too in representative quantities.
The human brain rewards specific knowledge because it's already pre-trained by evolution to have the basics.
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.
Are you trying to imply that humans don’t need generalized knowledge, or that we’re not “rewarded” for having highly generalized knowledge?
If so, good luck walking to your kitchen this morning, knowing how to breathe, etc.
Do you need to learn Latin and marine biology to work the cashier in your local shop? Thats the point, humans go on with their jobs on very limited general knowledge just fine. LLMs have gotten this good because their dataset, pre training, and RL is larger than before