Vid is a good friend of mine and he's wicked smart and also a very solid guy I adore.

I'm also guessing at some point he will probably read this comment, so hey Vid! See you at the next VRSA meetup!

I remember Kumo was focusing on GNN when it was founded (Jure's strength back then). Looks like they are pivoting or have pivoted.

I'll suspect it'll be more like the next little thing. Most of don't interact that much with structured data, so the applications will be very specific.

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.

Does AI for relational data work the same way as token predictions does for LLM AI?

TabPFN is an amazing innovation. But there are some crucial differences in model capabilities that make it hard for a fair comparison.

TabPFN can only operate on a single small table. But real-world datasets are actually multi-table and to make accurate prediction you need to capture signal from multiple tables (for example, customers, products, purchases).

So, the comparison to TabPFN would be unfair as it would only use data from a single table and that would lead to bad performance of TabPFN.

Yes, I think this would work. For example, you'd organize the data into 3 tables: patients, behaviors and images. The patients table would have a partially filled-out "diagnosis" column. The model would then predict diagnosis of not-yet-diagnosed patients based on the patterns in data fields of previously diagnosed patients.

Hey! I'm one of the engineers who worked on this project.

These are all problems that KumoRFM is able to solve given that you have the right relational data of course! So e.g. for predicting restaurant table availability you would need at least an occupancy table which records how many seats were available historically and you can predict its future entries.

But you can also add more relevant data without joining into a single table, so you can add a restaurants table, a holiday-calendar table, weather patterns, etc. and KumoRFM should take it all into account when predicting.