Prediction: It’s shit
Yeah - this seems to be IBM’s next step in making RHEL a gateway to IBM’s AI trap.
I thought IBM was still stuck with Watson. Have they moved on?
Not very RHELAIable, you say?
This is the best summary I could come up with:
Red Hat Summit 2024 is underway in Denver, Colorado…
Red Hat announced the developer preview today of Red Hat Enterprise Linux AI (RHEL AI).
RHEL AI is based on the InstructLab open source project and combines open source-licensed Granite large language models from IBM Research and InstructLab model alignment tools, based on the LAB (Large-scale Alignment for chatBots) methodology, in an optimized, bootable RHEL image to simplify server deployments.
The main objective of RHEL AI and the InstructLab project is to empower domain experts to contribute directly to Large Language Models with knowledge and skills.
This allows domain experts to more efficiently build AI-infused applications (such as chatbots).
… At general availability (GA), Red Hat Enterprise Linux AI Subscriptions will include enterprise support, a complete product life cycle starting with the Granite 7B model and software, and IP indemnification by Red Hat."
The original article contains 229 words, the summary contains 142 words. Saved 38%. I’m a bot and I’m open source!
Well that sure sounds like a bunch of marketing gobbledygook.
Exactly.
AI is essentially an algorithm that looks at a set of data which it “trains” on and then uses that predictive model to reproduce a facsimile of an answer. An analogy would be if you took a parrot and only sever said compliments to it you would get a parrot that says compliments except it doesn’t actually understand what its saying just that it should. Knowing that, what they’re saying is that they want your data to train on so here’s some crap you don’t want or need in the hopes you think its cool or useful.
If enough people give up their data they could probably make a model that is actually useful, at which time they’ll turn it into a paid product to replace the people who were naive enough to provide their data for training.
