with Brandon Ballinger (@bballinger), Mintu Turakhia (@leftbundle), Vijay Pande (@vijaypande), and Hanne Tidnam (@omnivorousread) There’s been a lot of talk about technology -- and AI, deep learning, and machine learning specifically -- finally reaching the healthcare sector. But AI in medicine isn’t actually new; it’s actually been there since the 1960s. And yet we didn’t see it effect a true change, or even become a real part our doctor’s offices -- let alone routine healthcare services. So: what's different now? And what does AI in medicine look like, practically speaking, whether it's ensuring the best data, versioning software for healthcare, or other aspects? In this episode of the a16z Podcast, Brandon Ballinger, CEO of Cardiogram; Mintu Turakhia, cardiologist at Stanford and Director of the Center for Digital Health; and general partner and head of a16z bio fund Vijay Pande in conversation with Hanne Tidnam discuss where will we start to see AI in healthcare first -- diagnosis, treatment, or system management -- to what it will take for it to succeed. Will we perhaps see a "levels" of AI framework for doctors as we have for autonomous cars?

with Frank Chen, Steven Sinofsky, and Sonal Chokshi There are many reasons why we’re in an “A.I. spring” after multiple “A.I. winters” — but how then do we tease apart what’s real vs. what’s hype when it comes to the (legitimate!) excitement about artificial intelligence and machine learning? Especially when it comes to the latest results of computers beating games, which not only captures our imaginations but has always played a critical role in advancing machine intelligence (whether it’s AI winning Texas Hold’em poker or beating the world human champ in the ancient Chinese game of Go). But on learning that Google DeepMind’s AlphaGo can master the game of Go without human knowledge — or more precisely: “based solely on reinforcement learning, without human data, guidance, or domain knowledge beyond game rules” — some people leap too far towards claims of artificial generalized intelligence. So where can we then generalize the findings of such work — unsupervised learning, self-play, etc. — to other specific domains? What does it mean for entrepreneurs building companies (and what investors look for)? And what does it mean for how we, as humans, learn… or rather, how computers can also learn from how we learn? Deal and research operating team head Frank Chen and a16z board partner Steven Sinofsky ponder all this and more, in conversation with Sonal Chokshi, in this episode of the a16z Podcast. We ended last time with the triumph of data over algorithms and begin this time with the triumph of algorithms over data … is this the end of big data?