I had a pretty wild dream last night. I was working at Google building physical neural networks. I think we were precipitating them out of a metallic semiconductor solution. My sense is that it was something where the input buffer was the cathode and the output was the anode. The finished systems were placed in mineral oil tanks, so they were basically artificial brains in a bucket. They looked something like this:

Netron is a viewer for neural network, deep learning and machine learning models.

Sentence Transformers in the Hugging Face Hub

• Sentence Transformers is a framework for sentence, paragraph and image embeddings. This allows to derive semantically meaningful embeddings (1) which is useful for applications such as semantic search or multi-lingual zero shot classification. As part of Sentence Transformers v2 release, there are a lot of cool new features:

RAM ProMaster Lift Kit (2014-2020)

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GPT Agents

• Create a view for reviews and businesses. Done
• Search for types and start pulling out reviews + stars. Done. Here’s the estimate of the number of rows based on the number of rows it takes to get to 100 samples:

The same info as a chart:

• Once I figure that out start making training corpora. I think I’ll stick to those cuisines that have more than 100k estimated reviews – code is done, running the queries and creating the test/train corpora. I’m adding useful_votes, funny_votes, and cool_votes for some more ground truth numbers to look at. The format should work for Excel too, so the stats can be computed from there

Book

• Roll in Upendra’s changes
• Start updating Overleaf

SBIR

• 4:00 Tagup. Ping Andreea