Deep Learning Summit
San Francisco

Ian Goodfellow is a Senior Research Scientist on the Google Brain team. He studies new methods for improving neural networks. Recent publications include: Adversarial Autoencoders; Net2Net: Accelerating Learning via Knowledge Transfer; and Explaining and Harnessing Adversarial Examples.

They are a dream of researchers but perhaps a nightmare for highly skilled computer programmers: artificially intelligent machines that can build other artificially intelligent machines.

A Brief Overview of Deep Learning from Google's Ilya Sutskever. What’s so special about deep learning? Why does it work now, and how does it differ from neural networks of old? How will it impact your industry? Hear more from Ilya at the summit.

Sergey Levine, Assistant Professor at UC Berkeley has been listed as one of the 35 Innovators Under 35 by MIT Technology Review for his work creating a robot that supervises its own learning. “It’s reverse-engineering its own behavior,” Levine said.

Improving neural networks not through teaching, but through evolution—is revealing its potential. Five new papers from Uber in San Francisco, California, demonstrate the power of so-called neuroevolution to play video games, solve mazes, and even make a simulated robot walk.

Find out more on how AI4ALL is educating the next generation of AI technologists, thinkers, and leaders

Rushin Shah left his post as a senior machine learning manager on Apple’s virtual assistant to join Facebook’s Applied Machine Learning team, where he’ll be working on natural language and dialog understanding.

Modern bots try to trick users into interacting with them as if they’re human in other ways: by bantering and using humor, speaking (or writing) conversationally, and learning to parse free-form questions and answers. “This creates a perception that if you say anything to this bot, it should respond to it,” said Nikhil Mane, Autodesk.

Over time, most AI applications will need to access multiple AI models to automate a process. PipelineAI aims to reduce the cost of creating those AI models by making it less expensive for developers to determine which AI framework will work best during the life cycle of their AI application.

You’ve probably seen lots of robots here on TechCrunch. You’ve seen Atlas, the robot that can walk just like a human. You’ve seen Small Dog, the quadruped bot that can scale hills and climb stairs. But have you ever seen a robot that just makes you say “aaaaaaaaaaaaaaaaaaawwwww”? Meet Mira — an adorable little mini-bot built by Pixar technical director Alonso Martinez with the help of Aaron Nathan

Northern Illinois University’s David Gunkel, in his recent book, asks provocative questions about the accountability and social standing of robots in the future.

Karol's research interests lie in active state estimation, control generation and machine learning for robotics. Karol investigates interactive perception, by which robots use their manipulation capabilities to gain the most useful perceptual information to model the world and inform intelligent decision making.

Aira has launched an app that uses AI to empower the blind community by connecting individuals with trained professionals who remotely provide visual assistance.

Folia Water is supported by 500 Startups, an early-stage venture fund and seed-stage accelerator that is based in Silicon Valley, but makes investments all over the world based on its mission to promote entrepreneurs from diverse backgrounds. Its 500 Seed Program brings startup founders together for four months, where they work on areas such as business strategy, user testing, and growth.

Researchers such as Abhishek Gupta are trying to help Montreal lead the world in ensuring AI is developed responsibly. His bi-monthly "AI ethics meet-up" brings together people from around the city who want to influence the way researchers are thinking about machine-learning.

A number of startups are now thinking about ways to minimize the amount of training needed for AI. Samsung Next is pushing this trend forward with the launch of a venture team called Q Fund. The team appears to be looking for companies that focus less on specific applications of AI (say, farming or algorithmic trading) and more on developing new ways of making decisions with computers.

Amy isn’t a real person but a software agent that exists somewhere in the cloud & communicates with your contacts and e-mail, helping set up meetings and appointments. The software is being developed by x.ai, which hopes to create something that seems virtually indistinguishable from a real human.

Hear from Catherine Lu, Principal at Spike Ventures, on the 3 major categories of AI companies. Catherine breaks down the three major categories as data science consulting firms, AI platform companies, and vertical AI companies. Read on to find out more.

Jeremy A. Marvel is a research scientist and project leader at the U.S. National Institute of Standards and Technology (NIST), a non-regulatory branch of the U.S. Department of Commerce. Prior to NIST, Dr. Marvel was a research scientist at the Institute for Research in Engineering and Applied Physics at the University of Maryland, College Park, MD.