I still see papers written on them occasionally. I have always wanted to implement one, but I've never had a use case. I think there are certain categories of problems where they excel, but in the real world, most of the time, there seems to be a better approach.

One real-world use case I saw was using genetic algorithms to design an automobile brake rotor to reduce heat (or increase heat dissipation). From what I remember of the presentation... Basically, they had a very large number of mathematical definable designs with many input variables. The interactions between these different variables were not necessarily clear. Elements of one of these designs might combine well with elements from a totally separate design. And the model to test them was computationally expensive.

They were able to use this genetic algorithm to design a rotor that, at least on the computer, was meaningfully better than their companies (and likely the industry's) state of the art.

Is it possible that they also switched from non-chinchilla-optimal davinci to chinchilla-optimal chatgpt? That is at least 4x smaller

Oh boy what I got away with. I have been using hundreds of thousands of tokens, augmenting parameters and only ever spent 20 bucks. I feel pretty lucky.

I've been tinkering with DaVinci but even with turbo/premium using gpt3.5turbo api requires a credit card added to the account. Excited to fool with it, however I typically use 2048-4000 tokens on DaVinci 3.

Speak for yourself.

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The fundamental problem with evolutionary strategies is that they are a freakin nightmare to evaluate. It's basically impossible to reason about their mathematical properties, experiments are noisy as hell, and how representative are the benchmark objective functions anyway? It's just really hard to do good science with those, which means it's hard to make concrete improvement. Sure, once upon a time they were the only choice for noisy, gradient free global optimization problems. But now we have Bayesian optimization.

Uhhhh

>I have no idea how OpenAI can make money on this.

Personally, I don't think they can. What is the main use case for chat bots? How many people are going to pay $20/month to talk to a chatbot? I mean, chatbots aren't exactly new... anybody who wanted to chat with one before ChatGPT could have and yet there wasn't an industry for it. Couple that with it not being possible to know whether its answers are fact or fiction and I just don't see the major value proposition.

I'm not overly concerned one way or another, I just don't think the business case is very strong.

I worked in adtech. It's often true.

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Try the SuperDataScience podcast with Jon Krohn. It's 90% ML. The host is quite a good explainer and he attracts fine guests (especially from industry).

One of my friend use genetic algorithms to do acoustic material research.

-totally not chatgpt

I think genetic algorithms may have a new role to play in problems involving inference / text generation / prompting with language models, even if they aren't used to train the models themselves.

For example, in our recent work on natural-language prompting, we use a genetic algorithm to generate prompts that are semantically coherent -- the genetic algorithm lets us make use of suggestions by a language model, for which gradients would be hard to obtain.

But does Bing believe in the sentience of Blake Lemoine...?

Machine learning street talk is great.

I use GA optimization for non-convex problems, mainly hyperparameter optimization. Sometimes it’s very effective but I’ve not found a way to know ahead of time if it will outperform other algorithms.

It mimics the function of part of our brain. It works like a language part of a dead brain. No consciousness.

Coming up with the statistically most probable next word is oversimplified. That is the training objective. The real process going on still needs to be investigated. The evolution of humans can also be viewed as maximizing our offspring . That does not mean humans are simple self-replicate meat balls.

I've never directly worked with either, but isn't RL agent-competitions approaches (i.e. simulating games between agents with different parameter values and iterating on this agents) a form of genetic algorithms?

It's also worth noting that this is exactly the type of problem that genetic algorithms were made for: no gradients, highly multimodal.

This is really interesting. Do you know of any approaches similar to this for learning hyperparameters layer structure, or complications such as inception networks or resnet?

yes

edit: it was also used to train Llama. there is no reason not to use it at this point, for both training and fine-tuning / inference

No. See this recent pub in Nature.

It's exciting to see that ChatGPT's cost is 1/10th that of GPT-3 API, which is a huge advantage for developers who are looking for high-quality language models at an affordable price. OpenAI's commitment to providing top-notch AI tools while keeping costs low is commendable and will undoubtedly attract more developers to the platform. It's clear that ChatGPT is a superior option for developers, and OpenAI's dedication to innovation and affordability is sure to make it a top choice for many in the AI community.

You're better qualified to know than nearly anyone who posts here, but is flash attention really all that's necessary to make that feasible?