I am the co-founder and CEO of Adaptive ML, where we are building AI models that can learn from production feedback. Our motto: if you can measure it, you can optimize it. Our technology draws inspiration from RLHF, RLAIF, RLEF, and recommender systems. We have a strong focus not only on technical excellency, but on building a delightful product experience, with real-world impact. You can reach me at julien@lolo.science.
🔥 Latest news
- 🔥 I am organizing the 2nd edition of the ICML workshop on Efficient Systems for Foundation Models! Join us in Vienna to discuss the nitty gritty details of training and inference of large models: gpusgobrrr.com.
- 🚀 I have started a company! Check-out Adaptive ML.
Interests
- 🦾 Building adaptive models. As humans, we learn from every interaction with our peers and our environment, constantly redefining ourselves. I am interested in how we can bring this adaptivity to large language models. I believe the next generation of post-training workflows won't rely on unwieldy data annotation contracts, but will instead directly leverage user/environment interactions and synthetic data.
- 📈 Challenges in scaling. Scaling has been the main driver of progress in machine learning for the past few years: I am interested in how we can keep that engine churning. Specifically, I am interested in challenges brought forth by ML becoming a so-called big science, with novel research directions at the crossroads of large-scale engineering and pure research.
- 🧠 Philosophy of mind. I am interested in how LLMs can gain human-like functions. This goes from deliberate reasoning and planning, to the acquisition of a theory of mind and its relation with works such as Julian Jaynes' bicameral mind. I am also interested in tool use, and how LLMs can learn to interact with their environment.
During my Ph.D., I also explored alternatives to backpropagation, using optical co-processors to train neural networks, and trained state-of-the-art open-source LLMs.
Fantastic networks and where to find me
- 📟 Blue-bird-thing: twitter.com/slippylolo;
- 📚 Super-duper serious scribbles: scholar.google.com;
- 🤖 Coder-Tinder: github.com/slippylolo;
- 💼 Professionnal-make-believe: linkedin.com/in/julien-launay;
- 🏞️ The Gram: instagram.com/slippylolo.