Check out our website: sophontai.com

Read our manifesto/announcement: tanishq.ai/blog/sophont

If you're interested in building & collaborating in this space, whether you're in genAI or medicine/pharma/life sciences, feel free to reach out at: [email protected]

researcher at Princeton University, and served as Head of NeuroAI at Stability AI. He led the team behind the MindEye publications, which achieved state-of-the-art reconstructions of seen images from brain activity.

With over five years of experience applying generative AI to medicine, I bring a wealth of expertise, having previously served as the Research Director at Stability AI and CEO of MedARC. My co-founder, Paul Scotti, has a decade of experience in computational neuroscience, was a postdoctoral ...

We are strong believers in open, transparent research, based on our proven track records in medical AI research and building open science online communities. We hope to continue this science-in-the-open approach to research at Sophont to build towards the future of healthcare.

Our vision is to merge highly performant, specialized medical foundation models into a single holistic, highly flexible medical-specific multimodal foundation model. We believe that this should be done open-source for maximum transparency and flexibility

These approaches succumb to the parable of the blind men and the elephant: The blind men are unimodal medical models and the patient is the elephant.

AI is clearly needed to enhance doctors’ ability to provide the best care.

However, currently deployed medical AI models are inflexible, rigid, suited for narrow tasks focused on individual data modalities.

Btw, I posted this 2 weeks ago on Twitter but forgot to post here, so doing it now. Twitter is probably going to always be the fastest place to get updates from me unfortunately 😅

Yeah, but compute scaling can mean lots of things including synthetic data for example

Artificial superintelligence

New blog post coming tomorrow on medical LLMs...

github.com/deepseek-ai/...

Flow matching is closely related to diffusion and rectified flows and Gaussian flow matching is equivalent to denoising diffusion.

PyTorch library: github.com/facebookrese...