This year we’ve seen remarkable advances in agentic AI, including systems that conduct deep research, operate computers, complete substantial software engineering tasks, and tackle a range of other complex, multi-step goals. In each case, the industry relied on careful vertical integration: tools and agents were  

Recently, we released a paper (Working with AI: Measuring the Occupational Implications of Generative AI) that studied what occupations might find AI chatbots useful, and to what degree. The paper sparked significant discussion, which is no surprise since people care deeply about the future of AI and jobs–that’s part of why we think it’s important  

[THEME MUSIC FADES]  The book passage I read at the top is from the epilogue, and I think it’s a truly fitting closing sentiment for the conclusion of this podcast series—because it calls back to the very beginning. As I’ve mentioned before, Carey, Zak, and I wrote The AI Revolution in Medicine as a guide to help answer these big questions, particularly as they pertain to medicine. You know, we wrote the book to empower people to make a choice […]

A new research framework helps AI agents explore three-dimensional spaces they can’t directly detect. Called MindJourney, the approach addresses a key limitation in vision-language models (VLMs), which give AI agents their ability to interpret and describe visual scenes.   While VLMs are strong at identifying objects in static  

Training AI models requires choosing an optimizer and for nearly a decade, Adam( (opens in new tab)–W) (opens in new tab) has been the optimizer of choice. Given that durability and success, it was  

[THEME MUSIC FADES]  The book passage I read at the top is from Chapter 7, “The Ultimate Paperwork Shredder.” Public health officials and healthcare system leaders influence the well-being and health of people at the population level. They help shape people’s perceptions and responses to public health emergencies, as well as to chronic disease. They help determine the type, quality, and availability of treatment. All this is critical for maintaining good public health, as well as aligning better health and […]

Unlocking self-adaptive cognitive behavior that is more controllable and explainable than reasoning models in challenging scientific domains Long-running LLM agents equipped with strong reasoning, planning, and execution skills have the potential to transform scientific discovery with high-impact advancements, such as developing new materials or pharmaceuticals. As these agents become more autonomous, ensuring effective human oversight and clear accountability becomes increasingly important, presenting challenges that  

Today, we are excited to introduce an autonomous AI agent that can analyze and classify software without assistance, a step forward in cybersecurity and malware detection. The prototype, Project Ire, automates what is considered the gold  

Many applications of language models (LMs) involve generating content based on source material, such as answering questions, summarizing information, and drafting documents. A critical challenge for these applications is that LMs may produce content that is not supported by the source text – a phenomenon known as “closed-domain hallucination.”1 Existing methods for detecting closed-domain hallucination typically compare a given LM output  

[THEME MUSIC FADES]   The book passage I read at the top is from Chapter 4, “Trust but Verify.” In it, we explore how AI systems like GPT-4 should be evaluated for performance, safety, and reliability and compare this to how humans are both trained and assessed for readiness to deliver healthcare.  In previous conversations with guests, we’ve spoken a lot about AI in the clinic as well as in labs and companies developing AI-driven tools. We’ve also talked about AI […]