I am starting a research lab funded by a hedge fund that I created 5 years ago. The hedge is doing well, and applies ML to quant finance. The research and trading activities should reinforce each other over time: the research publishes research and fosters in-house ML expertise. The trading applies it to quant finance and brings funding. The research entity is incorporated, funded, has an office and 0 employees currently. My vision for the research lab is the following: A bunch of phds in a room: create a good working environment, optimize for research output & runway. Take the 'bitter lesson' seriously: most of the successes in modern machine learning are due to deep learning and the ability to leverage vasts amounts of compute and data. So we make it a top priority to develop the infrastructure and expertise to be good at DL/LLMs/transformers. It doesn’t mean that we don’t explore new ideas. We want to learn to leverage large amounts of compute and be smart/efficient about it. If the trading works well, we can compete on an equal footing with the big labs over time in terms of amount spent in compute. For the research itself, I want to start with the ARC challenge.

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