Universal Medical Intelligence: OpenAI's Plan to Elevate Human Health, with Karan Singhal
Karan Singhal, Head of Health AI at OpenAI, explains how ChatGPT Health is achieving attending-physician-level performance and already serving hundreds of millions of users. He details how OpenAI works with over 250 doctors, built the 49,000-criteria HealthBench evaluation, and ran one of the first randomized trials of AI copilots in clinical care. The conversation explores privacy and safety safeguards, medical multimodality, N-of-1 treatment plans, and how AI could become a standard part of global medical practice.
Nathan uses Granola to uncover blind spots in conversations and AI research. Try it at granola.ai/tcr with code TCR — and if you’re already using it, test his blind spot recipe here: https://bit.ly/granolablindspot
LINKS:
modeling human wellness
Sponsors:
Claude:
Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro’s full capabilities at https://claude.ai/tcr
Serval:
Serval uses AI-powered automations to cut IT help desk tickets by more than 50%, freeing your team from repetitive tasks like password resets and onboarding. Book your free pilot and guarantee 50% help desk automation by week 4 at https://serval.com/cognitive
Framer:
Framer is an enterprise-grade website builder that lets business teams design, launch, and optimize their.com with AI-powered wireframing, real-time collaboration, and built-in analytics. Start building for free and get 30% off a Framer Pro annual plan at https://framer.com/cognitive
Tasklet:
Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai
CHAPTERS:
(00:00) About the Episode
(06:11) Cancer story and mission
(11:46) Designing safe health AI (Part 1)
(17:49) Sponsors: Claude | Serval
(21:09) Designing safe health AI (Part 2)
(26:48) Uncertainty, HealthBench and robustness (Part 1)
(30:23) Sponsors: Framer | Tasklet
(32:50) Uncertainty, HealthBench and robustness (Part 2)
(38:11) Chain-of-thought and evaluation
(46:49) Real-world performance and frontiers
(55:35) Multimodal data and science
(01:05:36) Personalization, privacy and monitoring
(01:15:47) Models, data and incentives
(01:29:31) Doctor adoption and workflows
(01:38:13) Scalable oversight and alignment
(01:51:06) Move 37 and future
(02:00:50) Episode Outro
(02:03:06) Outro
PRODUCED BY:
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Autoformalization and Verifiable Superintelligence with Christian Szegedy - #745
In this episode, Christian Szegedy, Chief Scientist at Morph Labs, joins us to discuss how the application of formal mathematics and reasoning enables the creation of more robust and safer AI systems. A pioneer behind concepts like the Inception architecture and adversarial examples, Christian now focuses on autoformalization—the AI-driven process of translating mathematical concepts from their human-readable form into rigorously formal, machine-verifiable logic. We explore the critical distinction between the informal reasoning of current LLMs, which can be prone to errors and subversion, and the provably correct reasoning enabled by formal systems. Christian outlines how this approach provides a robust path toward AI safety and also creates the high-quality, verifiable data needed to train models capable of surpassing human scientists in specialized domains. We also delve into his predictions for achieving this superintelligence and his ultimate vision for AI as a tool that helps humanity understand itself.
The complete show notes for this episode can be found at https://twimlai.com/go/745.
The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal
What if AI could revolutionize healthcare with advanced language learning models? Sarah and Elad welcome Karan Singhal, Staff Software Engineer at Google Research, who specializes in medical AI and the development of MedPaLM2. On this episode, Karan emphasizes the importance of safety in medical AI applications and how language models like MedPaLM2 have the potential to augment scientific workflows and transform the standard of care.
Other topics include the best workflows for AI integration, the potential impact of AI on drug discoveries, how AI can serve as a physician's assistant, and how privacy-preserving machine learning and federated learning can protect patient data, while pushing the boundaries of medical innovation.
No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.
Show Links:
May 10, 2023: PaLM 2 Announcement
April 13, 2023: A Responsible Path to Generative AI in Healthcare
March 31, 2023: Scientific American article on Med-PaLM
February 28, 2023: The Economist article on Med-PaLM
KaranSinghal.com
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @thekaransinghal
Show Notes:
[00:22] - Google's Medical AI Development
[08:57] - Medical Language Model and MedPaLM 2 Improvements
[18:18] - Safety, cost/benefit decisions, drug discovery, health information, AI applications, and AI as a physician's assistant.
[24:51] - Privacy Concerns - HIPAA's implications, privacy-preserving machine learning, and advances in GPT-4 and MedPOM2.
[37:43] - Large Language Models in Healthcare and short/long term use.
Understanding AI’s Impact on Social Disparities with Vinodkumar Prabhakaran - #617
Today we’re joined by Vinodkumar Prabhakaran, a Senior Research Scientist at Google Research. In our conversation with Vinod, we discuss his two main areas of research, using ML, specifically NLP, to explore these social disparities, and how these same social disparities are captured and propagated within machine learning tools. We explore a few specific projects, the first using NLP to analyze interactions between police officers and community members, determining factors like level of respect or politeness and how they play out across a spectrum of community members. We also discuss his work on understanding how bias creeps into the pipeline of building ML models, whether it be from the data or the person building the model. Finally, for those working with human annotators, Vinod shares his thoughts on how to incorporate principles of fairness to help build more robust models.
The complete show notes for this episode can be found at https://twimlai.com/go/617.
Protein Annotation at Google
Max Bileschi, a software engineer at Google Research, talks about his team's application of convolutional neural networks to predict the function of amino acid sequences in a protein. Eye on AI is sponsored by Clear.ML.
#50 Christian Szegedy - Formal Reasoning, Program Synthesis
Dr. Christian Szegedy from Google Research is a deep learning heavyweight. He invented adversarial examples, one of the first object detection algorithms, the inceptionnet architecture, and co-invented batchnorm. He thinks that if you bet on computers and software in 1990 you would have been as right as if you bet on AI now. But he thinks that we have been programming computers the same way since the 1950s and there has been a huge stagnation ever since. Mathematics is the process of taking a fuzzy thought and formalising it. But could we automate that? Could we create a system which will act like a super human mathematician but you can talk to it in natural language? This is what Christian calls autoformalisation. Christian thinks that automating many of the things we do in mathematics is the first step towards software synthesis and building human-level AGI. Mathematics ability is the litmus test for general reasoning ability. Christian has a fascinating take on transformers too.
With Yannic Lightspeed Kilcher and Dr. Mathew Salvaris
Whimsical Canvas with Tim's Notes:
https://whimsical.com/mar-26th-christian-szegedy-CpgGhnEYDBrDMFoATU6XYC
YouTube version (with detailed table of contents) https://youtu.be/ehNGGYFO6ms
Why AI Innovation and Social Impact Go Hand in Hand with Milind Tambe - #422
In this special #TWIMLfest Keynote episode, we’re joined by Milind Tambe, Director of AI for Social Good at Google Research India, and Director of the Center for Research in Computation and Society (CRCS) at Harvard University.
In our conversation, we explore Milind’s various research interests, most of which fall under the umbrella of AI for Social Impact, including his work in public health, both stateside and abroad, his conservation work in South Asia and Africa, and his thoughts on the ways that those interested in social impact can get involved.
The complete show notes for this episode can be found at twimlai.com/go/422.