Interesting timing, they have recently reached out to my $dayjob. We will be probably be running a workshop on our (massive) dataset with them. I'd like to evaluate the performance of a couple of analytical models we've manually built against whatever this model can do based on some prompts. Exciting times!

Strange that they do not compare it against TabFN, which is another foundation model for tabular data. (https://github.com/PriorLabs/TabPFN)

interesting! Super cool idea to augment software built with traditional DBs

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/

Jure Leskovec was my Professor at Stanford a few years back, cool to see he's behind this.

He seemed like a good guy and got the sense that he was destined to do something big

So suppose I've got a database of behavioral and neuroimaging data from a research study on autism. Is this something that can be used to predict diagnosis from the other data fields?

I feel like this is the next big thing for AI, having the ability to interact with any sort of structured dataset out of the box. Very cool project!

So can this be used to predict patterns for traffic, restaurant table availability, and your customers’ demand for things based on other customers?

A real-time in-context label generator. Nice...