When President Barack Obama agreed to guest-edit the November issue of WIRED, he selected MIT Media Lab Director Joi Ito for an exchange of ideas about artificial intelligence (AI). Their recent interview at the White House is featured in the latest online issue of WIRED, published on Oct. 12.
The one-on-one conversation, moderated by WIRED Editor-in-Chief Scott Dadich, ran the gamut of topics at the intersection of societal needs, ethics, and technology — from cybersecurity to self-driving cars; from the roles of government, industry, and academia to the lack of diversity in tech; from “moonshot” motivations to innovation at the margins; and from neurodiversity to Star Trek. All this was covered in the context of AI and extended intelligence (EI), which uses machine learning to augment human capabilities.
Two years after the small London-based startup DeepMind published their pioneering work on “Playing Atari with Deep Reinforcement Learning”, they have become one of the leaders in the chase for Artificial General Intelligence. In our previous article, we took a thorough peek inside their technology. In our latest research, we ask: what happens when multiple AI agents are competing or collaborating in the same environment?
The UK Royal Society would like to hear your thoughts on machine learning – you are invited to answer some or all of the questions set out in a Call for Evidence here. The closing date for evidence is 3 January.
In episode 23 we talk with David Mimno of Cornell University about his work in the digital humanities, and explore what machine learning can tell us about lady zombie ghosts and huge bodies of literature. Ryan introduces us to probabilistic programming and we take a listener question about knowledge transfer between math and machine learning.
Brain-Machine Interfaces (BMIs) — where brain waves captured by electrodes on the skin are used to control external devices such as a robotic prosthetic — are a promising tool for helping people who have lost motor control due to injury or illness. However, learning to operate a BMI can be very time consuming. In a paper published in Nature Scientific Reports, a group from CNBI, EPFL and NCCR Robotics show how their new feedback system can speed up the training process by detecting error messages from the brain and adapting accordingly.