Hacker News Reader

Comment by mlyle | Hacker News Reader

I can remember to do something tomorrow after doing many things in-between.

I can reason about something and then combine it with something I reasoned about at a different time.

I can learn new tasks.

I can pick a goal of my own choosing and then still be working towards it intermittently weeks later.

The examples we have now of GPT LLM cannot do these things. Doing those things may be a small change, or may not be tractable for these architectures to do at all... but it's probably in-between: hard but can be "tacked on."

View on HN

Former neuroscientist here.

Our brain actually uses many different functions for all of these things. Intelligence is incredibly complex.

But also, you don't need all of these to have real intelligence. People can problem solve without memory, since those are different things. People can intelligently problem-solve without a task.

And working towards long-term goals is something we actually take decades to learn. And many fail there as well.

I wouldn't be surprised if, just like in our brain, we'll start adding other modalities that improve memory, planning, etc etc. Seems that they started doing this with the vision update in GPT-4.

I wouldn't be surprised if these LLMs really become the backbone of the AGI. But this is science– You don't really know what'll work until you do it.

> I wouldn't be surprised if these LLMs really become the backbone of the AGI. But this is science– You don't really know what'll work until you do it.

Yes-- this is pretty much what I believe. And there's considerable uncertainty in how close AGI is (and how cheap it will be once it arrives).

It could be tomorrow and cheap. I hope not, because I'm really uncertain if we can deal with it (even if the AI is relatively well aligned).

That just proves we real-time fine tuning of the neuron weights. It is computationally intensive but not fundamentally different. A million token context would look close to long short-term memory and frequent fine-tuning will be akin to long-term memory.

I most probably am anthropomorphizing completely wrong. But point is humans may not be any more creative than an LLM, just that we have better computation and inputs. Maybe creativity is akin to LLMs hallucinations.

Real-time fine tuning would be one approach that probably helps with some things (improving performance at a task based on feedback) but is probably not well suited for others (remembering analogous situations, setting goals; it's not really clear how one fine-tunes a context window into persistence in an LLM). There's also the concern that right now we seem to need many, many more examples in training data than humans get for the machine to get passably good at similar tasks.

I would also say that I believe that long-term goal oriented behavior isn't something that's well represented in the training data. We have stories about it, sometimes, but there's a need to map self-state to these stories to learn anything about what we should do next from them.

I feel like LLMs are much smarter than we are in thinking "per symbol", but we have facilities for iteration and metacognition and saving state that let us have an advantage. I think that we need to find clever, minimal ways to build these "looping" contexts.

> I most probably am anthropomorphizing completely wrong. But point is humans may not be any more creative than an LLM, just that we have better computation and inputs.

I think creativity is made of 2 parts - generating novel ideas, and filtering bad ideas. For the second part we need good feedback. Humans and LLMs are just as good at novel ideation, but humans have the advantage on feedback. We have a body, access to the real world, access to other humans and plenty of tools.

This is not something an android robot couldn't eventually have, and on top of that AIs got the advantage of learning from massive data. They surpass humans when they can leverage it - see AlphaFold, for example.

Are there theoretical models that use real time weights? Every intro to deep learning focuses on stochastic gradient descent for neural network weights; as a layperson I'm curious about what online algorithms would be like instead.

Reply on news.ycombinator.com