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> such as datasets that are not properly integrated into the cloud I believe this is a core issue that needs to be addressed. I believe companies will need too
by stillpointlab 1y ago
> such as datasets that are not properly integrated into the cloud
I believe this is a core issue that needs to be addressed. I believe companies will need tools to make their data "AI ready" beyond things like RAG. I believe there needs to be a bridge between companies data-lakes and the LLM (or GenAI) systems. Instead of cutting people out of the loop (which a lot of systems seem to be attempting) I believe we need ways to expose the data in ways that allow rank-and-file employees to deploy the data effectively. Instead of threatening to replace the employees, which leads them to be intransigent in adoption, we should focus on empowering employees to use and shape the data.
Very interesting to see the Economist being so bullish on AI though.
- Marazan 1y agoThe Economist is filled with writers easily gulled by tech flim-flam. They went big on Cryptocurrency back in the day as well.
- rini17 1y agoGive rank-and-file employees access to all the data? LOL. Middle managers will never allow that and will shift blame to intrasingent employees. Of course Economist is pandering to that. LLMs are fundamentally very bad at compartmentalized access.
- stillpointlab 1y agoI didn't say all the data. In fact, what you are suggesting is exactly what I mean. The compartmentalized access is exactly the bridge we need. In fact, giving RBAC functionality to middle managers would be a key component to any strategy for AI deployment activity. You want traceability/auditability. Give the middle managers charts, logs, visibility into who is using what data with which LLMs. Give them levers to grant and deny access. Give them metrics on outcomes from the use of the data. This could eve even make legal happy. This missing layer will exist, its just a matter of time.