Impossible Chess
bcat112a.itch.io2 pointsby not-a-cat1 comments
- 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.
I want to study how DL/LLM models reason, using ARC as a benchmark (https://arcprize.org/, https://arxiv.org/abs/1911.01547). Techniques I want to investigate include test time compute, active inference, meta-learning, self-play, or any technique that would be considered "system 2". - ML research engineer
Compensation will include flexible options scheme based on results.
Interest and knowhow in the following is a plus: Deep learning, transformer models, reasoning, world models, learning-to-learn, mechinterp, bayesian methods, probabilistic programming. - 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. - ML research engineer
Compensation will include flexible options scheme based on results.