Summary: <a href="http://www.gayleallen.net/wp-content/uploads/2017/01/Blog-Post-Hod-and-Melba.png"></a>Self-driving cars are just around the corner. Are you ready?<br> With the advent of machine learning and related tech, autonomous cars are more technologically mature than most of us think. Yet old-school policies and regulations are lagging behind, making it difficult for large scale adoption to take place. Essentially, driverless tech has become a people, rather than a technology, problem.<br> To help us sort out the complicated landscape on our horizon, <a href="http://hodlipson.com/">Hod Lipson</a> and <a href="http://melbakurman.com/">Melba Kurman </a>wrote the book, <a href="https://www.amazon.com/Driverless-Intelligent-Cars-Ahead-Press/dp/0262035227">Driverless: Intelligent Cars and the Road Ahead</a>. Lipson, a roboticist at Columbia University who specializes in artificial intelligence and digital manufacturing, and Kurman, an expert on the impact of technology on the economy and our daily lives, lay out the advances in technology that got us here and the benefits and challenges that lie ahead. <br> Highlights from our interview include:<br> <br> The staggering number of lives self-driving cars will save<br> How the maturity of driverless tech has outpaced updates to policies and regulations<br> How traditional models of car insurance do not hold up to what autonomous cars require<br> How a safety standard comparing driverless tech to humans is key <br> How driverless tech can reduce noise and idling pollution<br> Ways parking spaces and garages can be repurposed with fewer cars on the road<br> The fact that city planners are focusing on public transportation and neglecting driverless tech and its impact on transportation budgets<br> The important safety challenge of an incremental versus an all-out shift to driverless tech<br> How driverless tech is now able to out-perceive humans at the wheel<br> The role DARPA played in advancing driverless accelerating driverless tech<br> How a shift from rules-based to machine learning birthed driverless car tech<br> How sensors and software feed information to driverless cars<br> How a combination of sensors and software help driverless tech overcome individual vulnerabilities in tech<br> How gaming software held the key to advancing driverless tech<br> The role ImageNet played in advancing image perception needed for driverless cars<br> The fact that deep learning includes machines learning what we may not have words for<br> Why we need to be talking about the impact of driverless tech on jobs<br> How driverless tech can reduce isolation and increase mobility for the elderly and visually impaired<br> How networked driverless cars can amass thousands of lifetimes of experience very quickly as they learn from one another in ways humans cannot<br> How the shift to self-driving cars is less about the tech and more about the human issues of policies and regulations<br> How driverless tech will usher in new businesses we cannot even imagine or predict<br> <br> Links to Topics Mentioned in this Podcast<br> <a href="http://www.creativemachineslab.com/">Creative Machines Lab</a> at <a href="http://www.columbia.edu/">Columbia University</a><br> <a href="https://en.wikipedia.org/wiki/DARPA_Grand_Challenge">DARPA Grand Challenge</a><br> <a href="https://www.amazon.com/Grid-Fraying-Between-Americans-Energy/dp/1608196100">The Grid by Gretchen Bakke</a><br> <a href="https://en.wikipedia.org/wiki/Lidar">Lidar</a><br> <a href="https://en.wikipedia.org/wiki/Graphics_processing_unit">GPUs</a><br> <a href="http://image-net.org/">ImageNet</a><br> <a href="https://en.wikipedia.org/wiki/Deep_learning">Deep learning</a><br> <a href="https://en.wikipedia.org/wiki/Qualia">Qualia</a><br> If you enjoy the podcast, <a href="https://en.wikipedia.org/wiki/David_Steindl-Rast">please rate and review it on iTunes</a> – your ratings make a...
