[raydiatian]

I have been thinking for a few weeks now that we need another term for large language models trained on colossal datasets: AGK, artificially generally/globally knowledgeable. It can mimic a likeness of problem solving because the corpus it was trained on is full of problem/solution pairs in the abstract. But task it with any novel problem solving challenge outside of its training that is of sufficient complexity and it will balk, thereby precluding it from being AGI, because humans are by nature problem solvers.

Furthermore, I just don’t feel like the transformer architecture is suited for problem solving. Like I may just be a charlatan but self attention over the space of words does not seem like it’s going to be enough, and praying it falls out in emergent behavior if we can just add more parameters is… unscientific-ish? Now, if you could figure out a way to do self-attention over the space of concepts? Maybe you’ve got something.

I feel like AlphaGo ideas and some variation on MCTS is more likely to produce a solid problem solving architecture.

[famouswaffles]

Reading the paper it seems they are problems a lot of people would fail at it too, at least some of the time. LLMs are not superhuman in logical reasoning seems to be the conclusion more than anything.

[raydiatian]

What you’re saying gets to the core of why I would call it AGK and not AGI. Training a transformer on known answers to problems and then observing that it can successfully answer questions related to those problems is cheating.

I think the way that Ilya suggests that the “test for consciousness is to train a model with an absolute absence of any training example remotely referring to the notion of a self or of feeling, and then ask it questions about feeling. If the model can do it, congrats, you’ve discovered consciousness.” Similarly, if you train an architecture on exclusively the building blocks of a particular class of problem, and also avoid training it on any sort of problem where it could just reason by analogy and get a correct answer (isolating first principles thing as the only option), then if it can solve the problem you have a genuine problem solving architecture.

[famouswaffles]

Meh Intelligence is Intelligence.

It's not cheating for people so asserting that it's cheating for machines just seems like goal post shifting more than anything.

Like this idea to pass the machines through frankly ridiculous hoops that humans wouldn't even pass is just..ehh. you seen how children with no language development in childhood turn out ?

It just misses the point entirely.

It's like the user down the thread said. Some isolate groups will build asi while the rest of the world is bickering about philosophical zombies and consciousness.

[raydiatian]

> It's not cheating for people so asserting that it's cheating for machines just seems like goal post shifting more than anything.

I genuinely appreciate this argument, and was considering it myself. In which case, I’d almost argue that we “have” already achieved AGI, and maybe it’s just not that thrilling.

[famouswaffles]

If you define agi to be artificial and generally intelligent at the human level then yes we have.

It seems though that definitions of agi have since shifted to "better than human experts in all tasks" in which case no...not yet.

[raydiatian]

Isn’t that artificial super intelligence?

[outofpaper]

Often it can actually solve more complex problems but needs to have its "hand held". Essentially the model needs to be guided to/through problem solving techniques. We have to remember that LLM are literally inference engines. They default to providing us with probable results, probable responses. They can pe pulled away from these "knee jerk" responses.

[dragonwriter]

> Often it can actually solve more complex problems but needs to have its “hand held”. Essentially the model needs to be guided to/through problem solving techniques.

While I haven’t done experiments with it hooked up to enough resources to really solve problems autonomously, providing it access to lookup information (e.g., searching wikipedia) and do simply computation (e.g., send python expressions to be evaluated) it figures out a lot more than just the chat interface alone without resources, without hand holding. I think autonomously solving problems where the necessary information is in the universe covered by training data and accessible resources is not unrealistic.

[raydiatian]

Right but if it needs its hand held, that ends up being a transcription task rather than a logical reasoning task. Like if you _tell it_ the solution to a coding job in detail, it can build you the complex entity you’re looking for. But if you just say for instance “write me a Python script that generates random chord changes (ex A#dim to Gmaj9b5)”, first of all it will just dump code without asking for clarification on requirements, and second of all even if you do give it further clarification on requirements the code won’t work without you explaining in depth the algorithm.

Although, that’s just a personal anecdote.