Artificial intelligence has become a larger part of our everyday lives in recent years. It’s being used in medical devices, smart-home systems, and video games—to say nothing of robots and autonomous cars. And AI has even started to do what many people have feared: outsmart humans. In its June special report, The Institute dives into today’s AI applications as well as its latest development: deep learning, in which machines teach themselves, then make decisions on their own.
Here to answer questions about the future of AI are three experts in the field. Submit your questions in the comments section below, or email us: Institute@ieee.org. The experts will answer a selection of questions, and we’ll publish their responses on 30 June.
Fellow Li Deng is chief AI scientist at the Microsoft Applications and Services Group and research manager at Microsoft Research. He pioneered research in the application of deep-learning speech recognition. With colleagues at Microsoft, he explored multimodal intelligence involving images and natural language for computers to communicate like humans. He received the IEEE Signal Processing Society’s 2015 Technical Achievement Award for contributions to deep learning and to automatic speech recognition.
Member John C. Havens is the author of Heartificial Intelligence: Embracing Our Humanity to Maximize Machines. He is the executive director of the Global Initiative for Ethical Considerations in the Design of Autonomous Systems, led by the IEEE Standards Association. He has written about AI for Mashable, TechCrunch, and other news outlets and is a TEDx speaker.
Fellow Fatih Porikli is the computer vision group leader for Data61 at NICTA, Australia’s Information and Communications Technology Research Centre of Excellence, in Canberra. He is a professor of engineering at the Australian National University, also in Canberra. He has made significant contributions to object and motion detection, object tracking, and video analytics. In 2014, he helped launch the annual IEEE DeepVision workshop, where people brainstorm theories and processes for deep-learning architectures.