Humans don't suffer from overfitting if they train on the same data too much.

That would make sense, having an understanding of images probably helps lead to a more intuitive grasp of physics

The first superhuman AGI will likely give its creator an insurmountable lead.

you are very special and unique!

Tay has made her return

They won't make any money, but they can tell others they have something "published". Which is all these talentless wankers want.

It was shown recently that for LLMs ~0.01% of parameters explain >95% of performance.

No dude.. no computer is emotional right now, even though it might say so, due to how they work. ChatGPT, the most advanced thing out there right now just predicts the next word. It’s a transformer model that can read texts backwards and forwards so that it can make more coherent predictions. That’s it. That’s all it does. It finds and mimics patterns, which is excellent for a large language model and especially the data it has consumed. But it can’t even do math and physics right and I mean it’s worse than a human. It doesn’t “work out problems”, it’s simply a “word calculator.” Also, Occam’s razor is something you’re using incorrectly. You could be a psychopath, a sociopath, or some other mentally unwell person that is certainly not “just like anyone else”. Occam’s razor means the simplest explanation for something is usually the correct one. Usually. And that’s completely different from the context you’re using it in.

Agreed. Stage one was "cogent", stage two was "as good as a human", stage three is "better than all humans". We have already passed stage 2 which could be called AGI. We will soon hit stage 3 which is ASI.

[deleted]

In a year or two (or less even?) we'll definitely (probably) have these running locally.

Hold onto your papers!

Wow a specialized model out preforms a generalized one?

*shocked pikachu*

I'm going to make a genuine question because no one ever gave me a clear answer. When will these language models ever start to be useful?

[deleted]

We would have to read the study methodology to evaluate how they were testing GPT 3.5's image context.

But in this case, multimodal refers to being trained on not just text (like GPT 3.5), but also images associated with that text.

That seems to have improved their model, which requires substantially fewer parameters while scoring higher, even in text-only domains.

But if you have to reason out the correct answer, and do that over and over again, it doesn't matter if there are 4 options or 1000. Think about it. The bar exam and other post graduate tests have multiple choice. You think anyone could pass those? Why do they take years of study?

I'd be interested to know what social science questions the AI was getting wrong compared to the humans.

I had a wondering the other day: “what does thousand-year old computer code look like?” “Ten-thousand?” “Million?”

Correct. The current chatbots will be seen as highly inefficient and archaic in like 30 days.

Yeah, absolutely.

Two more papers down the line. What a time to be alive!

It's good at faking empathy, just like humans.

Amen.

Perhaps you could have a specialist AI whose specialty was figuring out which other specialist AI it needs to pass the query to. If each specialist can run on home hardware that could be the way to get our Stable Diffusion moment. Constantly swapping models in memory might slow things down, but I'd be fine with "slow" in exchange for "unfettered."