“AI is one of the most important things humanity is working on. It’s more profound than electricity or fire.” – Sundar Pichai, CEO of Google. Listen as experts discuss the implications for effective machine learning and exchange best practices for applying AI across industries. Moderator: Mike Reiner (OpenOcean) Cassie Kozyrkov (Google) Claire Novorol (Ada Digital Health Ltd) Mark Chung (Verdigris Technologies) Rebecca Clyde (Botco.ai) Tech Open Air is Europe’s leading technology and innovation festival. Our mission is to connect, engage and inspire through transformative discourse, knowledge exchange and collaboration. Connect with us and stay up to date about our next events: http://www.toaberlin.com http://www.fb.com/techopenair http://www.twitter.com/toaberlin http://instagram.com/toaberlin
Discover the latest innovation and the positive impact of Artificial Intelligence technologies. The Applied AI Conference is a must-attend event for people who are working, researching, building, and investing in Applied Artificial Intelligence technologies and products. Keynote – Quantum AI: The Next Frontier Dr. Colin P. Williams, Director of Strategy & Business Development, D-Wave Systems, Inc.
Artificial Intelligence Conquering the next frontier of the digital world … discussion in the end whether the AI is really the future to humanity ….. machine learning that is done by mimicking the human brain’s multilevel functions …
Machine learning is moving toward an important advance: the development of flexible systems that can learn to perform multiple tasks and then use what they have learned to solve new problems on their own. While substantial progress has been made, significant improvements to hardware, software and system design remain to be made before truly flexible systems are developed. Two important contributors to the field of artificial intelligence and machine learning – Jeff Dean, head of Google AI and the co-founder of Google Brain, and Chris Ré, associate professor of computer science at Stanford – discussed the future of flexible machine learning at a recent session of the AI Salon, hosted by the Stanford AI Lab and the Stanford Institute for Human-Centered Artificial Intelligence. The hour-long discussion highlighted the following takeaways: •The use of specialized processing chips has already contributed to advances in machine learning, but some of those devices are beginning to reach a performance plateau, said Ré. Improvements are still possible, but designing hardware tailored to artificial intelligence projects is difficult because the field is evolving so quickly. Designing learning models that can more efficiently utilize the computing systems they run on will solve at least some of the performance issues, the researchers said. •With privacy a key requirement, Google has advanced an approach called “federated learning,” which enables mobile phones to do a better job predicting what words users are attempting to input to their phone without sending the data to the cloud, Dean said. In the future, [More]
Roboticist Andrea Thomaz gives us an under-the-hood peak at what is coming in the field of Robotics where robots will eventually be able to function and collaborate with humans as naturally as humans do with each other in social situations. Andrea Thomaz knows robots. Her work developing robots that don’t need programming to perform every task and that can collaborate side by side with humans as assistants, domestic help, etc. has been recognized by the National Science Foundation and the Office of Naval Research. Her work with social robots “Simon” and “Curi” has been featured in the New York Times and on NOVA Science Now. In 2012, Thomaz was named to Popular Science Magazine’s “Brilliant 10” and she was named an “Innovator under 35” by MIT Technology Review in 2009. She is an Associate Professor of Interactive Computing at the Georgia Tech and director of the Socially Intelligent Machines lab. She earned a B.S. in Electrical and Computer Engineering from the University of Texas at Austin in 1999, and Sc.M. and Ph.D. degrees from MIT in 2002 and 2006. Thomaz has published in the areas of Artificial Intelligence, Robotics, and Human-Robot Interaction. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Human bias is a notorious hindrance to effective risk management, and humans have long relied on machines to help them compensate for these errors. In particular, algorithmic, rules-based decision-making has done much to limit human bias in risk analysis. Machine learning is now lifting this work to the next level by uncovering trends and correlations heretofore unknown, and by enabling real-time access to high-frequency data. CEOs from innovative big data companies will reveal some of the most exciting new discoveries and applications of this sophisticated technology. Moderator Staci Warden Executive Director, Center for Financial Markets, Milken Institute Speakers Eduardo Cabrera Chief Cybersecurity Officer, Trend Micro Stuart Jones, Jr. CEO, Sigma Ratings Inc. Mark Rosenberg CEO and Co-Founder, GeoQuant Inc. Stephen Scott Founder and CEO, Starling Trust Sciences #MIGlobal http://www.milkeninstitute.org/events/conferences/global-conference/2018/