zlacker

>>vforgi+(OP)
Imagine thinking that regression based function approximators are capable of anything other than fitting the data you give it. Then imagine willfully hyping up and scaring people who don't understand, and because it can predict words you take advantage of the human tendency to anthropomorphize, so it follows that it is something capable of generalized and adaptable intelligence.

Shame on all of the people involved in this: the people in these companies, the journalists who shovel shit (hope they get replaced real soon), researchers who should know better, and dementia ridden legislators.

So utterly predictable and slimy. All of those who are so gravely concerned about "alignment" in this context, give yourselves a pat on the back for hyping up science fiction stories and enabling regulatory capture.

>>srslac+I7
What do you think about the papers showing mathematical proofs that GNNs (i.e. GATs/transformers) are dynamic programmers and therefore perform algorithmic reasoning?

The fact that these systems can extrapolate well beyond their training data by learning algorithms is quite different than what has come before, and anyone stating that they "simply" predict next token is severely shortsighted. Things don't have to be 'brain-like' to be useful, or to have capabilities of reasoning, but we have evidence that these systems have aligned well with reasoning tasks, perform well at causal reasoning, and we also have mathematical proofs that show how.

So I don't understand your sentiment.

>>chaxor+hB
To be fair LLMs are predicting the next token. It's just that to get better and better predictions it needs to understand some level of reasoning and math. However it feels to me that a lot of this reasoning is brute forced from the training data. Like chatgpt gets some things wrong when adding two very large numbers. If it really knew the algorithm for adding two numbers it shouldn't be making them in the first place. I guess same goes for issues like hallucinations. We can keep pushing the envelope using this technique but I'm sure we will hit a limit somewhere

>>rdedev+3F
Of course it predict the next token. Every single person on earth knows that so it's not worth repeating at all.

As for the fact that it gets things wrong sometimes - sure, this doesn't say it actually learned every algorithm (in whichever model you may be thinking about). But the nice thing is that we now have this proof via category theory, and it allows us to both frame and understand what has occurred, and to consider how to align the systems to learn algorithms better.

>>chaxor+GG
The fact that it sometimes fails simple algorithms for large numbers but shows good performance in other complex algorithms with simple inputs seems to me that something on a fundamental level is still insufficient