Artificial intelligence (AI) already plays a major role in human economies and societies, and it will play an even bigger role in the coming years. To ponder the future of AI is thus to acknowledge that the future is AI. But how bright is that future? Or how dark?

Artificial intelligence is surrounded by fear and mystery because very few understand its inner workings. But it’s actually rather intuitive and far simpler than it seems.

Coupled with audio and vital-sign data, this deep-learning, wearable system could someday serve as a “social coach” for people with anxiety or Asperger’s

The webcast will take place on today from 9am-5:30pm EST and February 1st from 9am-5pm EST. Webcast participants are encouraged to submit questions for the presenters by e-mailing Michelle Schwalbe at mschwalbe@nas.edu who will read them out if time permits.

Given a still image, CSAIL deep-learning system generates videos that predict what will happen next in a scene.

Frank Chen, a partner at Andreessen Horowitz, the Silicon Valley venture capital and private equity firm, said, “It is absolutely non-controversial that deep learning is the most fundamental advance in AI research since the start [of A.I.] in 1956.”

John McCormac discusses his takeaways from the RSS 2016 Workshop ‘Are the Sceptics Right? Limits and Potentials of Deep Learning in Robotics’ and highlights interesting themes and topics from the discussion.

In episode eleven we chat with Neil Lawrence from the University of Sheffield. We talk about the problems of privacy in the age of machine learning, and the responsibilities that come with using machine learning tools and making data more open. We learn about the Markov decision process (and what happens when you use it in the real world and it becomes a partially observable Markov decision process) and take a listener question about finding insights into features in the black boxes of deep learning.

In Episode 10 we talk with David Blei of Columbia University. We talk about his work on latent dirichlet allocation, topic models, the PhD program in data that he’s helping to create at Columbia and why exploring data is inherently multidisciplinary. We learn about Markov Chain Monte Carlo and take a listener question about how machine learning can make humans more creative.

In episode nine we talk with George Dahl, of  the University of Toronto, about his work on the Merck molecular activity challenge on kaggle and speech recognition. George recently successfully defended his thesis at the end of March 2015. (Congrats George!) We learn about how networks and graphs can help us understand latent properties of relationships, and we take a listener question about just how you find the right algorithm to solve a problem (Spoiler: start simple.)

Robohub is excited to bring you our newest series – the Talking Machines podcast. Hosted by Katherine Gorman and Ryan Adams, Talking Machines is your window into the world of machine learning. You can catch new episodes every two weeks on Thursdays.

Today on Talking Machines we hear from Google researcher Ilya Sutskever about his work, how he became interested in machine learning, and why it takes a little bit of magical thinking.

Recently there has been a spate of articles in the mainstream press, and a spate of high profile people who are in tech but not AI, speculating about the dangers of malevolent AI being developed, and how we should be worried about that possibility. I say, relax. Chill. This all comes from some fundamental misunderstandings of the nature of the undeniable progress that is being made in AI.

In January, 2014, Google acquired London-based DeepMind Technologies for $643 million. Now it is adding to that purchase with two more companies, ten new hires and a substantial contribution to Oxford University.