Importance of Understanding Data for Model Building

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Understanding the data is crucial for effective model building. Close engagement with the data enables a co-development process of data sets and models, rather than just a model training problem. Organizations can improve model training by evaluating loss per region on a data set to increase exposure on specific domains. This approach involves online training and monitoring the models' evolution on specific subsets of the data.

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We can’t think of a more Latent-Space-y way to end 2023 than with a mega episode featuring many old and new friends recapping their biggest news, achievements, and themes and memes of the year!

We previously covered the Best Papers of NeurIPS 2023, but the other part of NeurIPS being an industry friendly conference is all the startups that show up to hire and promote their latest and greatest products and papers! As a startup-friendly podcast, we of course were ready with our mics to talk to everyone we could track down.

In lieu of an extended preamble, we encourage you to listen and click through all the interviews and show notes, all of which have been curated to match the references mentioned in the episode.

Timestamps & Show Notes

* [00:01:26] Jonathan Frankle - Chief Scientist, MosaicML/Databricks

* see also the Mosaic/MPT-7B episode

* $1.3B MosaicML x Databricks acquisition

* [00:22:11] Lin Qiao - CEO, Fireworks AI

* Fireworks Mixtral

* [00:38:24] Aman Sanger - CEO, Anysphere (Cursor)

* see also the Cursor episode

* $8m seed from OpenAI

* Tweet: Request-level memory-based KV caching

* Tweet: GPT-4 grading and Trueskill ratings for rerankers

* [00:51:14] Aravind Srinivas - CEO, Perplexity