XResearch04:10Jason Wei@_jasonwei@lindugong health is one of the best applications of AICollected by WispRead in full↗
XResearch02:52Jason Wei@_jasonweiIn our health workstream at Meta we did a project where we wanted to see what research papers medical experts could write using a Muse Wonder coding scaffold that could access a database of 5M electronic health records we licensed from a vendor. The goal is that with 5M patients in this database, AI could do research to answer arbitrary questions like “did people who take statin have higher rates of diabetes later in life”? The thing we were betting on here is on what happens when you put human experts, a strong coding model, and access to data all together We worked with a small group of medical experts to help them use this AI tool and the punchline is that they all found that this setup enabled a much faster pace of health research. They generated ten manuscripts in around two weeks, with one researcher (Paddy Ssentongo) generating three. Throughout the process, they collaborated with each other to review manuscripts and write skill files to help our coding scaffold improve at doing research. The medical experts liked how quickly they could test different hypotheses and choose the best ones to continue working on Health research is pretty unique in that it is super important to be able to interpret noisy data and have taste to know which variables are important, which was still a significant value add from human researchers. Given that medicine is one of the oldest fields in human history and it is ultimately about humans, keeping a substantial human component in this type of research is probably important. In the near future I see AI primarily as a tool to facilitate human-led research. With the right scaffold I believe health researchers can be 10x or even 100x more productive using AI tools that have appropriate access to the wealth of data that the world has collected about human health One way to see this work is through the broader lens of AI for science. You can think of science as two parts: (1) collecting raw data about the world, and (2) synthesizing that raw data into understanding. In this project we already had existing data about the world and we aimed to accelerate part (2) using AI. Eventually, I imagine a world where we have such great AI tools that the time to synthesize raw data into understanding is driven towards zero. In that world, science will be substantially faster and most of the work will be focused on collecting the right raw data via physical experiments We did this in a pretty short amount of time (which was the point) and I think the future of scientific research will be more faster and more exciting than everCollected by WispRead in full↗
XResearch05:05Jason Wei@_jasonwei@engyziedan thanks and happy to partner on the data for this work!Collected by WispRead in full↗
XResearch03:42Jason Wei@_jasonwei@_memoryleak Fun working together on this Ashish!Collected by WispRead in full↗
XResearch04:47Jason Wei@_jasonwei@alexandr_wang Love this quote about Muse Wonder: "What I appreciate about Muse Wonder is that it isn't sycophantic. Some other models I've used will make an idea sound great in the moment, and only later do I realize it didn't hold up. Muse Wonder is willing to call me out and steer me toward a better approach, often before I've spotted the problem myself. That saves a lot of time, and it's a big part of why I trust it." — Jason Rothschild, MD, Musculoskeletal radiologistCollected by WispRead in full↗
XResearch03:31Jason Wei@_jasonwei@kritika_singh24 So great to have you on the team Kritika, great work!Collected by WispRead in full↗
XResearch01:20Jason Wei@_jasonweimuse wonder is out! scaling prevails… it has been so fun to be part of the powerful yet nimble ship of Meta, where in just over a year, much of the company pivoted to contribute to the model, ranging from the infra engineers who started building tools for experiment management to the business development teams that totally transitioned to help hire human experts for data review and try out our model for health use cases as well! great for multimodal, you will be able to feel the scale 📈Collected by WispRead in full↗
XResearch01:06Jason Wei@_jasonweivery cool result showing how wet lab data enables a specialized model to beat gpt-6 astra at a task at the frontier of science! in general scaling is great and obviously i am a believer in it, but probably the more we approach the frontier of science, the more specialized data matters and that gives task-specific models a chance. this specialized data is usually private and is probably a real moat it should be in principle true that a task specific model will probably do better at scientific discovery just because it can use more of its parameters for the task you care aboutCollected by WispRead in full↗