Facebook's hexapod, Daisy, learning to walk On the rooftop of the building that houses the Facebook AI Research (FAIR) lab in Mountain View, California, there is a bootcamp for robots where the sun beams down on Daisy, a hexapod who is learning how to walk on a dirt jogging path. Her foot has become stuck in mulch as she struggles to wrestle free. A team of Facebook AI researchers eagerly look on, watching to see what she will do next as she moves forward with the curiosity and experimentation of a toddler. One flight down, Daisy's counterpart Pluto, a red arm robot, is learning how to reach for an object in its playpen. Facebook is leading an effort to teach robots how to think for themselves and develop human-like intuition that will enable them to navigate unknown circumstances.

Insilico Medicine, a global leader in artificial intelligence for drug discovery, announced the publication of a paper titled, "Deep learning enables rapid identification of potent DDR1 kinase inhibitors," in Nature Biotechnology. The paper describes a timed challenge, where the new artificial intelligence system called Generative Tensorial Reinforcement Learning (GENTRL) designed six novel inhibitors of DDR1, a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice. The traditional drug discovery starts with the testing of thousands of small molecules in order to get to just a few lead-like molecules and only about one in ten of these molecules pass clinical trials in human patients.

This Christmas, my friend's grandmother finally found out what her grandson had been working on for years. He's a data scientist raised on English with a bit of Spanish that gets dusted off on occasional family occasions. His grandmother speaks only Spanish. "Before today, my grandmother had no idea what I actually do for a living." The sci-fi-fuelled rumors of what data scientists work on -- especially if we specialize in AI -- attract a whiff of the ridiculous, so many of us find ourselves constantly having to explain our life choices.

Today's leading minds talk AI with host Byron Reese Episode 94 of Voices in AI features Byron speaking with fellow futurist and author Amy Webb on the nature of artificial intelligence and the morality and ethics tied to its study. Listen to this episode or read the full transcript at www.VoicesinAI.com Byron Reese: This is Voices in AI brought to you by Gigaom, and I'm Byron Reese. My guest is Amy Webb. She is a quantitative futurist.

Today, many companies now process huge amounts of data using artificial intelligence (AI). If the data that fuels AI algorithms is unrepresentative of society, however, these programs essentially learn and adopt our biases. More organisations are now opting to employ algorithmic decision-making in order to reduce bias and improve operations. Nevertheless, it is possible for these algorithms to share many of the same vulnerabilities found in a human decision-making process. Indeed, the interim report Bias in Algorithmic Decision Making released in July this year supports this.

A team of Italian mathematicians, including one who is also a neuroscientist from the Champalimaud Centre for the Unknown (CCU), in Lisbon, Portugal, has shown that artificial vision machines can learn to recognize complex images spectacularly faster by using a mathematical theory that was developed 25 years ago by one of this new study's co-authors. Their results have been published in the journal Nature Machine Intelligence. During the last decades, machine vision performance has exploded. For example, these artificial systems can now learn to recognise virtually any human face - or to identify any individual fish moving in a tank, in the midst of a large number of other almost identical fish which are also moving. The machines we're talking about are, in fact, electronic models of networks of biological neurons, and their aim is to simulate the functioning of our brain, which is as good as it gets at performing these visual tasks - and this, without any conscious effort on our part.

Disclaimer: I would like it to be made very clear that I am absolutely 100% open to the idea that I am wrong about anything in this post. I don't only accept but explicitly request arguments that could convince me I am wrong on any of these issues. If you think I am wrong about anything here, and have an argument that might convince me, please get in touch and present your argument. I am happy to say "oops" and retract any opinions presented here and change my course of action. As the saying goes: "When the facts change, I change my mind. I plan on releasing it on the 1st of July. Before criticizing my decision to do so, please read my arguments below. If you still think I'm wrong, contact me on Twitter @NPCollapse or by email (thecurioushacker@outlook.com) and convince me. For code and technical details, see this post. UPDATE: My mind has been changed, and I plan on not releasing. See my update post here that explains my reasoning. UPDATE 2: This post is now part 1 in a series of ...

This is a clip from a conversation with Yann LeCun on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2NJiCov If you enjoy these, consider subscribing, sharing, and commenting below. Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning.

In Grenoble, France, one company is aiming to make an impact in the field which is so visibly dominated by multi-billion dollar corporations. We caught up with the company's Business Unit Director responsible for introducing their products to the Automotive market, Stéphane Cordova, to find out more, ahead of their attendance at AutoSens Detroit in May. The company's approach to "Supercomputing on a chip" has evolved from a the business origins providing components and software services to data centres, where high speed and reliability as well as low power consumption and significantly reduced heat generation were all key factors in processor component design. What helped you decide to commit to exhibiting at AutoSens again? Kalray's technology will be at the heart of autonomous driving.

Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founding father of convolutional neural networks, in particular their early application to optical character recognition. This conversation is part of the Artificial Intelligence podcast. OUTLINE: 0:00 - Introduction 1:11 - HAL 9000 and Space Odyssey 2001 7:49 - The surprising thing about deep learning 10:40 - What is learning?