potatoman22

I really liked this, thank you

I think a good "traditional" approach would look like a BM25 algorithm over an index of trigger phrases for each category, sitting behind a majority-vote classifier. The "fine tuning" would be done by reindexing the data, generating different/new phrases, and tuning the classification threshold.

I think that's to demonstrate its speed

Adding this to my homelab, thanks for the web browser option. How does your son use the backing tracks? Does he mute bass/drum, or play along with them?

I have about 60 hours of my own playing in midi data. I wonder if I could fine-tune this model on that?

It's not specific to SQLlite per se, but that's what most dashboard builders are

Generative models have been used in healthcare for a while for things like drug design and data generation. Not to mention all the algorithms (and probably ML) used in generating results for MRI and CT scans. I don't think this is that crazy provided they can prove it's effective.

I'd wager that for 99.9% of "Apple Intelligence" tasks, Google's models perform just as well as other frontier labs. Google also has done more work on getting LLMs running on edge devices compared to anthropic and openAI.

The source also says > The new architecture centers on Apple Foundation Models co-developed with Google, which Apple says are adapted to run both on-device and on servers through its existing Private Cloud Compute infrastructure

Which could mean Google and Apple have trained some custom models, probably the on-device ones, specifically tailored towards Apple's hardware.

Picking an equal weight fund is closer to picking specific stocks than investing in the S&P500 imo

I wonder why they didn't use a Bonsai model as the text encoder

Google should know why a human accepted the automated suggestion, or if and why there wasn't any human oversight in the first place.