[vkou]

> Anything closed source and having regulatory oversight is useless

Source isn't even the problem, unless you're a billionaire, you can't afford to train the model.

This isn't the wheel, the printing press, the PC, or the public internet, which created opportunities for everyone.

This is pay-to-play that is only affordable to the nation-state the mega-corporation, and the latter might let you play around as a digital sharecropper on their platform, until they cut you off, because they can make more money by having first-party ownership of whatever you built.

[bangkoksbest]

The model is the instance because the instance is what is being used at inference time. The training simply produces some set of weights.

Open source would mean someone could see and run the model code locally/independently to create an instance. For an LLM, this is much less insurmountable of an issue in terms of resources needed.

[_heimdall]

We should probably be calling for an open model or open data approach if that's what we think is best. Calling it open source leaves plenty of gray area in the definition.

For example, I run a few instances of open source software though my instances' databases are private.

[pixl97]

That is questionable.

We are still answering questions on LLM/AI scaling. Ya, you might have your cottage industry AI giving out answers on the trickle of information you feed it, but will that even be comparable to one being fed megawatts of power with terabytes of data per second flowing into its databanks?