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On the path from scientific discovery to new drug, the clinical trial is a huge — and critical — hurdle. Clinical trials are themselves experiments, and to make sure that they are doing the best possible job at deter...
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In this episode of our show 16 Minutes -- where we talk about the headlines, and where we are on the long arc of tech trends -- we cover the news around Google DeepMind's AlphaFold system for predicting the 3-D structure of proteins outperforming 100 teams across 20 countries in the 14th Community Wide Assessment on the CASP (Critical Assessment of Structure Prediction) challenge. The challenge, which takes place every other year (over several months) tracks progress, key metrics, and state-of-the-art on predictive techniques for protein folding.
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a16z Journal Club covers recent advances from the scientific literature; this inaugural episode for bio covers 2 two different topics: (1) identifying new antibiotics through a novel machine-learning based approach; and (2) characterizing the novel coronavirus causing the COVID-19 pandemic.
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Data has long been lauded as a competitive moat for companies, and that narrative’s been further hyped with the recent wave of AI startups. Network effects have been similarly promoted as a defensible force in building...
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Data, data, everywhere, nor any drop to drink. Or so would say Coleridge, if he were a big company CEO trying to use A.I. today — because even when you have a ton of data, there’s not always enough signal to ...
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When you have “a really hot, frothy space” like AI, even the most basic questions — like what is it good for, how do you make sure your data is in shape, and so on — aren’t answered. This is...
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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 Winarksy 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?
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We’ve already talked quite a bit about the Algorithms, Machines, and People lab at U.C. Berkeley (AMPLab) — all about making sense of big data — so what happens when the entire world moves towards artif...