Previous guests on our podcasts - from Tesla, Aurora, to Waymo - are building the brains of the cars and trucks of our future. This episode's guest, Professor Cathy Wu, is helping build the roadways of our future with machine-learning to predict the ideal infrastructure for the world's future mobility, the cost of building this infrastructure, and the age-old question, "How do we eliminate traffic jams?" Currently at MIT's Institute for Data, Systems, and Society (IDSS), Professor Cathy Wu (and previous student of Pieter Abbeel's) gives listeners an overview of the type of potential scenarios being modeled with machine-learning such as scenarios in which the road is filled with mixed-autonomy vehicles. What emergent behaviors might happen? As our mode for transportation and delivery evolve, what are the infrastructure or software solutions that can help ensure smooth travel and safe roadways? What are the policy considerations? Throughout the talk, Wu cites building reinforcement learning for her work and why it's the right fit her research, "Reinforcement learning is essentially this paradigm at the intersection of machine learning and also control, and it is essentially about how agents learn from experience and in particular through trial and error." What's in this episode: 00:00:00 - Introduction 00:02:09 - Her undergrad path to where she is now 00:06:30 - Why studying next-generation transportation models is important 00:10:03 - What does autonomy mean for traffic jams? 00:18:30 - Her motivation for her work 00:23:00 - Why use reinforcement learning? 00:29:27 - The future of our world's highways 00:34:21 - Advice for students Links: MIT's IDSS: https://idss.mit.edu/ Website: http://wucathy.com/ Recent interview: https://news.mit.edu/2021/qa-cathy-wu-developing-algorithms-safely-integrate-robots-our-world-1216 TEDxMIT talk: https://www.youtube.com/watch?v=mrk8I-9iLs4 SUBSCRIBE TODAY: Apple: https://apple.co/3vRqBlV Spotify: https://spoti.fi/3d0PEwf Amazon: https://amzn.to/2SWdZfP Google: https://bit.ly/3d52Uju Acast: https://bit.ly/3A0uqIO Host: Pieter Abbeel Executive Producers: Alice Patel & Henry Tobias Jones Production: Fresh Air Production

Analyzing a person’s blood (and the cells within it) is often used to diagnose many health conditions and illnesses, from infections to leukemia and bone marrow disorders. Generally it's a long and expensive process. You have to go to the doctor, have a sample taken, wait for a couple of days for a trained professional to analyze the blood, and finally get your diagnosis. Athelas is using machine learning to dramatically improve the speed and efficiency of testing blood cells. From a simple finger prick’s worth of blood, Athelas devices can monitor and help healthcare professionals to remotely care for patients. To date over 40,000 people have used Athelas devices to monitor a range of conditions including hypertension and diabetes. With a recent valuation of $1.5B, the company is poised for major impact. What's in this episode: 00:00:00 - Introduction 00:7:03 - Athelas' founding story 00:18:30 - Genetic differences among populations 00:21:14 - How does Athelas differ from Theranos? 00:27:07 - Real life use cases in action including Leukemia and Schizophrenia 00:32:25 - Patient stories related to detection and chronic care 00:34:10 - The story of Tanay founding his first company at 15 years old 00:37:59 - How the Athelas cofounders met, from science fair competitor to co-founder 00:39:16 - Tanay’s decision to quit Stanford to start his company 00:41:20 - YCombinator at 19 years old 00:44:00 - Advice for other founders 00:50:58 - Where is Athelas headed next? Links: Athelas website: https://www.athelas.com/ Bloomberg coverage of Athelas funding: https://www.bloomberg.com/news/videos/2022-02-01/health-startup-athelas-hits-1-5b-valuation-video Subscribe and listen to our podcast today: Apple: https://apple.co/3vRqBlV Spotify: https://spoti.fi/3d0PEwf Amazon: https://amzn.to/2SWdZfP Google: https://bit.ly/3d52Uju Acast: https://bit.ly/3A0uqIO Host: Pieter Abbeel Executive Producers: Alice Patel & Henry Tobias Jones Audio Production: Kieron Matthew Banerji. Video Production: Bo Obradovic.

*Warning* Explicit language used in the episode. Benedict Evans, long-time technology analyst and occasional VC at firms such as Andreessen Horowitz, is well known for his analysis of mobile, media and technology trends. He writes a popular weekly newsletter on the most important happenings in tech and he is also famous for his annual presentations that analyze macro and strategic trends in the tech industry. He’s a voice of reason trying to sort out the technology issues of the day. In this episode of The Robot Brains, he sits down with Pieter to discuss his opinion on the label of "AI", areas of business that ML is adding real value, and his predictions for the future. What's in this episode: 00:00:00 - Introduction 00:09:38 - The great unbundling 00:12:57 - How does AI play a role in this movement? 00:16:40 - The stage of AI becoming a product or a company 00:17:40 - Benedict’s time at Andreessen Horowitz 00:21:22 - Can scaling up current technologies lead to AGI? 00:31:25 - The vertical view of AI, logistics and e-commerce 00:36:40 - How important is efficiency for logistics? 00:40:04 - China's role as an AI powerhouse 00:46:46 - Government regulation of tech 00:51:50 - Benedict's view on AVs and transportation tech Links: Benedict's Twitter: https://twitter.com/benedictevans Benedict's newsletter: https://www.ben-evans.com/newsletter Benedict's annual reports: https://www.ben-evans.com/presentations Subscribe and listen to our podcast today: Apple: https://apple.co/3vRqBlV Spotify: https://spoti.fi/3d0PEwf Amazon: https://amzn.to/2SWdZfP Google: https://bit.ly/3d52Uju Acast: https://bit.ly/3A0uqIO Host: Pieter Abbeel Executive Producers: Alice Patel & Henry Tobias Jones Audio Production: Kieron Matthew Banerji. Video Production: Bo Obradovic.

While the world’s temperature rises, there are scores of scientists working around the globe to study causes and solutions. One scientist in particular, David Rolnick, has stood out as a pioneer of machine-learning in the fight against climate change. David successfully built a broader movement including others like Andrew Ng, Yoshua Bengio, Demis Hassabis, and Jennifer Chayes to champion the amazing possibilities that exist at the intersection of AI and the climate. He organized the first-ever ever AI event at the United Nations Climate Change Conference. He was named a top innovator by the MIT Technology Review -- all before the age of 30. He sits down with Pieter to discuss his landmark paper on the applications of ML to climate change looking at use cases like weather simulations, ecological monitoring, and predicting natural resource depletion. What's in this episode: 00:00:00 - Introduction 00:04:45 - Funding of David's work 00:05:30 - Different applications of ML for climate change 00:08:43 - How ML can speed up climate simulations 00:13:07 - How can one person make change? 00:15:22 - Ecological monitoring with ML 00:18:12 - The global perspective on climate change and AI 00:20:54 - Who decides what is studied? 00:23:00 - Will David stay in the ML field? 00:26:00 - Advice for students 00:32:49 - Can we be hopeful about the future of our climate? Links: David's Twitter: https://twitter.com/david_rolnick David's Website: https://t.co/Tyr5VPVpv8 2019 paper on tackling climate change: https://arxiv.org/abs/1906.05433 Subscribe and listen to our podcast today: Apple: https://apple.co/3vRqBlV Spotify: https://spoti.fi/3d0PEwf Amazon: https://amzn.to/2SWdZfP Google: https://bit.ly/3d52Uju Acast: https://bit.ly/3A0uqIO Host: Pieter Abbeel Executive Producers: Alice Patel & Henry Tobias Jones Audio Production: Kieron Matthew Banerji. Video Production: Bo Obradovic.

In the second episode of Season Two of The Robot Brains, Pieter Abbeel interviews the Chief Technology Officer of Etsy, Mike Fisher. Etsy is a global online marketplace, where people come together to make, sell, buy, and collect unique items. It was founded in 2005 as a simple listing site for DIY crafters and artists to sell their wares before blossoming into a widely-respected and mainstream online shopping site. It still maintains its whimsical brand despite its listing on the NASDAQ and in 2021, it generated an impressive $1.7B in revenue. The CEO shared with Forbes that he's determined to keep growing bigger. One of his strategies? Using AI and machine-learning to keep improving the 1:1 selling and buying experience its so well-known for. Mike Fisher, CTO, sat down with Pieter to share the transformation the site has gone through starting with rebuilding its search engine capabilities all the way to designing more intelligent algorithms to provide better and more accurate recommendations. With 100M items (equivalent to the inventory of 2500 grocery stores), the site processes a lot of data! Mike shares how Etsy manages it all and continues to innovate in the e-commerce space. What's in this episode: 00:00:00 - Introduction 00:02:18 - Mike's path from Army as an aviation officer to computer engineer at GE 00:05:01 - How Mike connected with Etsy 00:06:11 - What is Etsy? 00:09:53 - AI's role in the buyer journey 00:13:58 - How Etsy became a major mask seller overnight 00:25:37 - Computer vision at Etsy 00:29:10 - Lessons learned 00:35:38 - The future of online marketplaces 00:37:54 - Preparing for the holiday shopping season 00:39:49 - Etsy's Engineering culture 00:42:11 - What Mike applies to Etsy that he learned from flying helicopters Links: Forbes interview with Etsy CEO: https://bit.ly/33nLlcj Website: https://etsy.com Twitter: https://twitter.com/Etsy Subscribe and listen to our podcast today: Apple: https://apple.co/3vRqBlV Spotify: https://spoti.fi/3d0PEwf Amazon: https://amzn.to/2SWdZfP Google: https://bit.ly/3d52Uju Acast: https://bit.ly/3A0uqIO Host: Pieter Abbeel Executive Producers: Alice Patel & Henry Tobias Jones Audio Production: Kieron Matthew Banerji Video Production: Bo Obradovic

In Episode One of Season Two, Host Pieter Abbeel is joined by guest (and close collaborator) Sergey Levine, professor at UC Berkeley, EECS. Sergey discusses the early years of his career, how Andrew Ng influenced him to become interested in machine learning, his current projects, and his lab's recent accomplishments. The conversation concludes with Sergey's view on the dangers of machines not being intelligent enough and his advice for students seeking a career in robotic. What's in this episode: 00:00:00 Introduction 00:02:07 Sergey's PhD work 00:04:55 Andrew Ng's influence 00:05:46 Defining supervised learning and reinforcement learning (RL) 00:10:03 Sergey's predictions for RL in the future 00:11:29 Sergey's switch from graphics to robotics 00:13:58 Robots learning in the real world and Sergey's current research 00:32:48 How to keep collecting useful data 00:43:31 Offline model-based optimization 00:47:12 The concept of singularity 00:54:10 Managing AI responsibly Links: Sergey's Twitter: https://twitter.com/svlevine EECS lab: http://rail.eecs.berkeley.edu/ Sergey's Website: https://people.eecs.berkeley.edu/~svlevine/ Subscribe and listen to our podcast today: Apple: https://apple.co/3vRqBlV Spotify: https://spoti.fi/3d0PEwf Amazon: https://amzn.to/2SWdZfP Google: https://bit.ly/3d52Uju Acast: https://bit.ly/3A0uqIO Host: Pieter Abbeel Executive Producers: Alice Patel & Henry Tobias Jones Audio Production: Kieron Matthew Banerji Video Production: Bo Obradovic

The Robot Brains podcast hosted by Pieter Abbeel returns for a second season beginning in January 2022 and publishing weekly on Wednesdays. In this season, we'll talk to academic and business leaders building brand new robots and experiences using artificial intelligence and machine learning. With guests including Sergey Levine of UC Berkeley, Cathy Wu of MIT, and Gustav Söderström of Spotify, Pieter interviews a wide range of experts. Listeners will finish Season 2 with a better understanding of the exciting AI inventions happening in the world today and perhaps be inspired to build their own! Links: About Pieter Abbeel: https://en.wikipedia.org/wiki/Pieter_Abbeel The Robot Brains website: https://therobotbrains.ai The Robot Brains Twitter: https://twitter.com/therobotbrains The Robot Brains LinkedIn: https://www.linkedin.com/company/the-robot-brains-podcast Subscribe and listen to our podcast today: Apple: https://apple.co/3vRqBlV Spotify: https://spoti.fi/3d0PEwf Amazon: https://amzn.to/2SWdZfP Google: https://bit.ly/3d52Uju Acast: https://bit.ly/3A0uqIO Host: Pieter Abbeel Executive Producers: Alice Patel & Henry Tobias Jones Audio: Kieron Matthew Banerji Video: Bo Obradovic

One thing every AI researcher knows is that most foundational AI research is actually happening in open-source. Indeed, most AI researchers publish their research findings openly on arxiv, a freely accessible, open repository originally mostly used by physics researchers. And the code underlying many AI breakthroughs is often published on github, where it’s readily accessible to anyone. Historically these papers and codebases have originated from the leading universities and tech companies. But the open culture in the AI community means that aspiring researchers can study the latest AI breakthroughs and codebases, and in principle, start making their own contributions. It hasn't really been done before in a meaningful until Ross Wightman came along. As an independent researcher, Ross has grown into one of the most prominent contributors to AI research and codebases. In a previous life, an engineer at a Canadian unicorn start-up, his current full-time occupation is building new AI models that are freely available on Github for use by anyone and everyone. And he's essentially self-taught. He chats with Pieter about his inspirations, his beliefs, and his latest papers. What's in this episode: 00:00:00 - Introduction 00:02:07 - Ross' early days working with hardware 00:6:50 - Angel investing in Vancouver 00:9:25 - Self-taught AI and ML skills, Kaggle competitions 00:14:34 - PyTorch discussion 00:21:34 - Google's VisionTransformer, introduction to Timm 00:26:12 - Why Ross likes to remain independent, his day-to-day life 00:39:14 - Discussion of ResNet Strikes Back paper 00:41:41 - What matters for ResNet to do well 00:47:19 - How Ross combines many ideas at once 00:52:04 - What keeps Ross motivated 00:57:57 - The importance of open-source like Kaggle, HuggingFace, Eleuther Links: Ross' paper ResNet Strikes Back: https://arxiv.org/abs/2110.00476 Twitter: https://twitter.com/wightmanr Website: http://rwightman.com/ Github: https://github.com/rwightman Kaggle: https://www.kaggle.com/rwightman Hugging Face: https://huggingface.co/ Eleuther: https://www.eleuther.ai/ SUBSCRIBE TODAY: Apple: https://apple.co/3vRqBlV Spotify: https://spoti.fi/3d0PEwf Amazon: https://amzn.to/2SWdZfP Google: https://bit.ly/3d52Uju Acast: https://bit.ly/3A0uqIO Host: Pieter Abbeel Executive Producers: Alice Patel & Henry Tobias Jones Production: Fresh Air Production