Bigger models are more sample efficient, so it should need less data.

But - didn't the Chinchilla paper say bigger models need more data? Yes, but that's only true because right now compute is the limiting factor. They're intentionally trading off more data for less model size.

As computers get faster and models bigger, data will increasingly become the limiting factor, and people will trade off in the opposite direction instead.

I don't think this is a good test because these questions allow you to trade off knowledge for creativity, and LLMs have vast internet knowledge. It's easy to find listicles with creative uses for all of the objects in the test.

Now, this applies to human creativity too! If you ask me for an alternative use for a pair of jeans, I might say that you could cut them up and braid them into a rug. This isn't my creative idea; I just happen to know there's a hobbyist community that does that.

I think in order to test creativity you need constraints. It's not enough to find uses for jeans, you need to find uses for jeans that solve a specific problem.

Oh, definitely. I just checked ChatGPT and it's both aware of the existence of the test and can generate example question/answer pairs. This is a general problem when applying human psychology tests to LLMs.

It does help that this test is open-ended and has no right answer. You can always come up with new objects to ask about.

gpt2-medium is only 355M parameters so don't expect quality.

Even the fullsize GPT-2 is smaller than the smallest variants of most modern language models.

>They seem to refer to this model as text-only, contradicting to the known fact that GPT-4 is multi-modal.

I noticed this in the original paper as well.

This probably means that they implemented multimodality the same way Palm-E did; starting with a pretrained LLM.

I expect it's more likely that people will run their own chatbots with proprietary content. (Even if just built on top of the GPT API)

For example you might have a news chatbot that knows the news and has up-to-date information not available to ChatGPT. And you'd pay a monthly subscription to the news company for it, not to OpenAI.

Google search uses BERT, you're just calling a language model via an API.

Right, but you're still loading the full GPT4 to do that.

The idea is that domain-specific chatbots might have better performance at a given model size. You can see this with StableDiffusion models, the ones trained on just a few styles have much higher quality than the base model - but only for those styles.

This is basically the idea behind mixture of experts.

We're really running out of acronyms at this point.

Doesn't look like they sell in individual quantities right now but I welcome any competition in the space!

Llamma.cpp uses the neural engine, so does StableDiffusion. And the speed is not that far off from VRAM, actually.

>Memory bandwidth is increased to 800GB/s, more than 10x the latest PC desktop chip, and M1 Ultra can be configured with 128GB of unified memory.

By comparison, the Nvidia 4090 is clocking in at ~1000GB/s

Apple is clearly positioning their devices for AI.

I mean of course not, nobody would make such a game right now because there are no >24GB cards to run it on.

Right. And even once you have enough VRAM, memory bandwidth limits the speed more than tensor core bandwidth.

They could pack more tensor cores in there if they wanted to, they just wouldn't be able to fill them with data fast enough.

Just price. They have the same amount of VRAM. The 4090 is faster of course.

I'm hoping that non-Vonn-Neumann chips will scale up in the next few years. There's some you can buy today but they're small:

>NDP200 is designed natively run deep neural networks (DNN) on a variety of architectures, such as CNN, RNN, and fully connected networks, and it performs vision processing with highly accurate inference at under 1mW.

>Up to 896k neural parameters in 8bit mode, 1.6M parameters in 4bit mode, and 7M+ In 1bit mode

An arduino idles at about 10mw, for comparison.

The idea is that if you're not shuffling the entire network weights across the memory bus every inference cycle, you save ludicrous amounts of time and energy. Someday, we'll use this kind of tech to run LLMs on our phones.

Honestly, they already cost more than I can afford to spend on a side project.

I'm just gonna have to wait and hope that AMD gets their act together on AI support.

Data is really hard to get because of privacy regulations too.

There are millions of brain MRI scans sitting in hospital databases but nobody can use them without individually asking each patient. Most published datasets are only a couple dozen scans, and plenty are N=1.

I'm gonna end up buying a bunch of 24GB 3090s at this rate.

I haven't heard of anybody running LLama as a paid API service. I think doing so might violate the license terms against commercial use.

>(or any other) model

OpenAI has a ChatGPT API that costs pennies per request. Anthropic also recently announced one for their Claude language model but I have not tried it.

Computers have gotten literally 100 million times faster within my lifetime. I'm not even that old!

Oh, it is clearly structured. Words and phrases and sentences are all forms of structure and we're using them right now.

What it doesn't have is formal structure; it cannot be fully defined by any set of rules. This is why you can't build a rules-based parser that understands english and have to use an 800GB language model instead.

>shared across essentially every language and dialect

Noam Chomsky thinks this, but the idea of a universal grammar is controversial in modern linguistics.

So why do you think it is a structured formal thing?

...for a toy-sized 250M parameter language model, yes.

That's why it's a natural language instead of a formal language.

The brain doesn't have any built-in knowledge about language, but it has an advantage; it's trying to communicate with other brains.

It is fundamentally impossible to understand human language without understanding how humans think. Language isn't a structured formal thing, it's more like the fuzzy interactions of two neural networks.

Humans already know how other humans think - plus they have a shared world environment to ground the symbols in. LLMs have to learn to approximate both of those.