mgreg

I'm interested in their Planner -> Implementer -> Reviewer -> Verifier process they used for this transition to Rust. I've see similar but curious how they actually implemented this.

Curious how this could be applied to greenfield coding rather than just making a copy in a new language or performance optimizing.

This is quite the loss for OpenAI.

And no one seems to have heard what Ilya Sutskever's status is at OpenAI as well.

Unsurprising but disappointing none-the-less. Let’s just try to learn from it.

It’s popular in the AI space to claim altruism and openness; OpenAI, Anthropic and xAI (the new Musk one) all have a funky governance structure because they want to be a public good. The challenge is once any of these (or others) start to gain enough traction that they are seen as having a good chance at reaping billions in profits things change.

And it’s not just AI companies and this isn’t new. This is art of human nature and will always be.

We should be putting more emphasis and attention on truly open AI models (open training data, training source code & hyperparameters, model source code, weights) so the benefits of AI accrue to the public and not just a few companies.

[edit - eliminated specific company mentions]

I think this is built into Excel already.

In the Excel menu bar: Data -> Data from Picture

It's a great observation. People simply want their free stuff.

The potential challenge arises in the future. Today's models will probably look weak compared to models we'll have in 1, 3 or 10 years which means that today's models will likely be irrelevant in years hence. Every competitive "open" model today is tied closely to a controlling organization weather it's Meta, Mistral.AI, TII, 01.AI, etc.

If they simply choose not to publish the next iteration of their model and follow OpenAI's path that's the end of the line.

A truly open model could have some life beyond that of its original developer/organization. Of course it would still take great talent, updated datasets, and serious access to compute to keep a model moving forward and developing but if this is done in the "open" community then we'd have some guarantee for the future.

Imagine if Linux was actually owned by a for-profit corporation and they could simply choose not to release a future version AND it was not possible for another organization to fork and carry on "open" Linux?

Applying the term "open source" to AI models is a bit more nuanced than to software. Many consider reproducibility the bar to get over to earn the label "open source."

For an AI model that means the model itself, the dataset, and the training recipe (e.g. process, hyperparameters) often also released as source code. With that (and a lot of compute) you can train the model to get the weights.