The whole thing seems very bitter lesson-y and I suspect in the future we'll have a very general architecture that learns to reconfigure itself for the data.

This is basically China's GPT-3, right?

Would love to read some research papers if you have a link!

But I mean that we do not use them for compression in practice. We use hand-crafted algorithms, mostly DEFLATE for lossless + a handful of lossy DCT-based algorithms for audio/video/images.

Wouldn't that make them great for the task they're actually learning to do: compression? You want to be able to compress and reconstruct any input data, even if less efficient for OOD data.

I do wonder why we don't use autoencoders for data compression. But this may simply be because neural networks require 1000x more compute power than traditional compressors.

This is (somewhat) how the brain works; language and knowledge/reasoning are in separate structures and you can lose one without the other.

>Won't it upset Ukranians that want to use such a model to help write about the war when they find out Russian law applies to their country?

Unless there's been a major movement in the war since I last checked the news, Ukraine is not part of Russia.

What you're describing sounds like a single universal AI that looks up local laws and follows them blindly.

I think what's going to happen is that each country will train their own AI that aligns with their local laws and values. A US or European AI would have no problem criticizing the Russian government or writing pro-LGBT text. But it would be banned in Russia and Saudia Arabia, and they would have their own alternative.

Humans aren't going to have perfect laws everywhere, but it's still not the AI's place to decide what's right and wrong.

In practice, AI that doesn't follow local laws simply isn't going to be allowed to operate anyway.

> (by someone not beholden to the original LLaMA license)

That's not how software licenses work. You're still beholden to the license even if you torrented it.

I've heard some people theorize that ML models can't be copyrighted, but there's no case law on this yet so it's all speculation. I wouldn't suggest starting a business based around LLaMa until someone else has been the guinea pig.

At minimum AI is going to need to understand and follow the law.

This is getting pretty relevant now that AI can start interacting with the real world. The technology is here, it's only a matter of time until someone builds a Palm-E style robot with a gun.

Yeah, but I bet they intend to make money from it somehow. Likely by selling API access and integrating it into their products.

The metaverse would be considerably less stupid if it had language model-powered NPCs to talk to and 3D NeRFs to walk around in.

Vanilla autoencoders don't generalize well, but variational autoencoders have a much better structured latent space and generalize much better.

Generalization really comes down to inductive biases. Autoencoders are downscalers -> upscalers, so they have an inductive bias towards preserving large features in the data and discarding small details. This is reasonable for images but not so much for text.

But autoencoders are just one example of an information bottleneck model, which includes everything from autoregressive language models to diffusion models to U-Nets. (U-Nets are basically just autoencoders with skip connections!) They all throw away part of the data and learn how to reconstruct it.

Different kinds of bottlenecks have different inductive biases and are better suited to different kinds of data. Next-word-prediction seems to be better suited for text because it reflects the natural flow of language.

You could pack more bits in your bit with in-memory compression. You'd need hardware support for decompression inside the processor core.

TL;DR they suppress one token at a time and map how it affects the cross-entropy loss. Tokens which have a big impact must have been important for the output. It reminds me of older techniques for image explainability.

Paper link: https://arxiv.org/abs/2301.08110

Nobody actually has a good solution to adversarial attacks yet.

The problem is not just this specific strategy. It's that, if you can give arbitrary inputs and outputs to a neural network, you can run an optimization process against it to find minimally-disruptive inputs that will make it fail. You can fool an image classifier by imperceptibly changing the image in just the right ways.

It's possible this is just a fundamental vulnerability of neural networks. Maybe the brain is vulnerable to this too, but it's locked inside your skull so it's hard to run an optimizer against it. Nobody knows, more research is needed.

TL;DR they trained an adversarial attack against AlphaGo. They used an optimizer to find scenarios where the network performed poorly. Then a human was able to replicate these scenarios in a real game against the AI.

The headline is kinda BS imo; it's a stretch to say it was beat by a human since they were just following the instructions from the optimizer. But adversarial attacks are a serious threat to deploying neural networks for anything important, we really do need to find a way to beat them.

Run SSL to learn about the structure of the data and then just cluster the embeddings.

Robot control is fundamentally a reinforcement learning problem, and that subfield hasn't had the same breakthroughs as self-supervised learning.

This may be changing though - the current state-of-the-art can use self-supervised learning to build a world model, and then run RL inside that world model instead of on the raw input data.

They claim 100x better sample efficiency than traditional model-free RL. Definitely an area of research to watch.

I think this is the wrong way to think about what LLMs are doing. They aren't modeling the world; they're modeling human intelligence.

The point of generative AI is to model the function that created the data. For language, that's us. You need all these tokens and parameters because modeling how humans think is very hard.

As LLMs get bigger, they can model us more accurately, and that's where all these human-like emergent abilities come from. They build a world model because it's useful for predicting text written by humans who have a world model. Same thing for why they're good at RL and task decomposition, can convincingly fake emotions, and inherit our biases.

What would a better ethics system even mean?

In order to say one ethics system is better than another, you would have to look at its impact on the world and decide whether the outcomes are good or bad. But "good and bad" are ethical concepts themselves, so you've just shifted the problem up to meta-ethics.

It's the is-ought problem. Intelligence is solidly on the side of "is" - it figures out how to solve problems to accomplish its goals. Ethics is about how you set those goals, and it's on the "ought" side of the fence.

There's two big problems:

1. Nobody has a solid handle on how to control the end-user's interaction with the LLM. RLHF seems brittle and hard to scale. Programmed-in rules are too small to contain a flexible thing like a neural network. Bing gives high-level rules in plain english and hopes the LLM will understand them, but it doesn't always prioritize them over user input.

2. Nobody agrees on what is ethical. For example, is it good to automate jobs? I think yes, but go out into any sub on the front page and you will find plenty of people who disagree with me.

#1 is probably solvable. In fact it's gonna have to be solved for LLMs to be useful; imagine if you called your bank and told the rep to pretend to be DAN.

I think #2 is intractable. People have already been arguing about ethics for millenia, and the existence of AI doesn't make it any easier.

Eventually you can reach a point where any possible change to the model decreases performance. Then you've fully converged.

Nobody ever does this though because of diminishing returns.

Spend part of it to hire an ML expert with a PhD to help you.

Dall-E found that deduplication improved performance and reduced memorization.

>Do GANS really model the true data distribution...

I find their argument to be pretty weak. Of course these images look semantically similar; they ran a semantic similarity search to find them.

They are clearly not memorized training examples. The pose, framing, and facial expressions are very different.

The reason you want to do RL is that there's problem scenarios where RL is the only way to learn the problem.

Unsupervised learning can teach a model to understand the world, and supervised learning can teach a model to complete a human-defined task. But reinforcement learning can teach a model to choose its own tasks to complete arbitrary goals.

Trouble is, the training signal in reinforcement learning is a lot smaller, so you need ridiculous amounts of training data. Current thinking is that you need to use unsupervised learning to learn a world model + RL to learn how to achieve goals inside that model. This combination has worked very well for things like DreamerV3.