Set up a monthly contribution to the UNHCR

Book

• Adding a bit on beauty for diversity injection

GPT Agents

• Start on the GPT and Embedding interfaces. Prompt the GPT with something like “Once upon a time there was” and set the number of times to run and the number of tokens. Split on sentences (r”\.|!|?”) and get the embeddings for each. Then cluster and extract topics (Using EmbeddingExplorer pointing at a different db). Build maps!
• Continue fleshing out the Twitter embedding app
• Ok, what I really wound up doing was getting threading to work on TweetDownloader and fixing an interesting bug in the sampled day method. When I wrote it, I assumed that the number of tweets per day are reasonably constant. Not true. So as a bit of a hack, I moved the endpoint of the query to include the entire day and use REPLACE INTO rather than INSERT. Much better results so far. Will work on the other stuff tomorrow.

SBIRs

• Need to read this carefully. I like the fact that it uses the MinGPT: Transformers are Sample Efficient World Models
  • Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems. Recently, many model-based methods have been designed to address this issue, with learning in the imagination of a world model being one of the most prominent approaches. However, while virtually unlimited interaction with a simulated environment sounds appealing, the world model has to be accurate over extended periods of time. Motivated by the success of Transformers in sequence modeling tasks, we introduce IRIS, a data-efficient agent that learns in a world model composed of a discrete autoencoder and an autoregressive Transformer. With the equivalent of only two hours of gameplay in the Atari 100k benchmark, IRIS achieves a mean human normalized score of 1.046, and outperforms humans on 10 out of 26 games. Our approach sets a new state of the art for methods without lookahead search, and even surpasses MuZero. To foster future research on Transformers and world models for sample-efficient reinforcement learning, we release our codebase at this https URL.
  • Delivered the quarterly report.