I had some thoughts [1] around a concept similar to this a while ago, although it was much less refined. My thinking was around whether or not we could have a neural net remember a relational database schema, and be able to be queried for facts it knows, and facts it might predict.
This seems like a much more sensical (and actualised) stab at this kinda concept.
[1]: dancrimp.nz/2024/11/01/semantic-db/
He seemed like a good guy and got the sense that he was destined to do something big
I'm also guessing at some point he will probably read this comment, so hey Vid! See you at the next VRSA meetup!
However, the algo-trading crowd, will likely be very interested in this. They deal with structured data all day and it would surprise me if most of them don't already have things like this working in their networks. They seem to be very secretive, though, so we're not gonna hear much.
Every single credit card purchase gets classified by a model as fraud or ok. When you go to Netflix and see recommended movies, it's all predictions on structured data. Every single post in every social media feed is there because a model predicted you'd like it.
Realistically, it might be more like 10s of thousands or even hundreds of thousands of predictions that we engage with in a day.
If reality matches the benchmarks for this model, it can kick off a whole new category of models that can potentially be bigger than LLMs
This has more applications than you might first think.