Yoshua Bengio: Yoshua BengioOCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadiancognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.

One afternoon in the summer of 2018, Bob Gramling dropped by the small suite that serves as his lab in the basement of the University of Vermont's medical school. There, in a grey lounge chair, an undergrad research assistant named Brigitte Durieux was doing her summer job, earphones plugged into a laptop. Then he saw her tears. Bob doesn't balk at tears. As a palliative care doctor, he has been at thousands of bedsides and had thousands of conversations, often wrenchingly difficult ones, about dying. But in 2007, when his father was dying of Alzheimer's, Bob was struck by his own sensitivity to every word choice of the doctors and nurses, even though he was medically trained. "If we [doctors] are feeling that vulnerable, and we theoretically have access to all the information we would want, it was a reminder to me of how vulnerable people without those types of resources are," he says. He began to do research into how dying patients, family members, and doctors talk in these moments about end of treatment, pain management, and imminent death. Six years later, he received over $1 million from the American Cancer Society to undertake what became the most extensive study of palliative care conversations in the US.

Among them is that systems of governance are not immortal and that democracies can devolve into autocracy. As institutions decay and social norms fray, democratic processes and practices are prone to apathy, demagoguery and disintegration. One scholar ringing the loudest alarm bell--or perhaps death knell--is David Runciman. He is a professor of politics at Cambridge University and the author of "How Democracy Ends". His replies are followed by an excerpt from the book. Upgrade your inbox and get our Daily Dispatch and Editor's Picks.

Deep Learning enables rapid identification of potent DDR1 Kinase Inhibitors. Insilico Medicine, a global leader in artificial intelligence for drug discovery, today 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.

One of the biggest legal problems protecting AI users in the coming years will be accountability – dealing with the opacity of the black box and explaining decisions made by machine thinking. Understanding the logic behind an AI finding is not an issue where AI is assisting in spotting real-world risks that affect individuals – such as the current use of AI in radiology, where failure to use AI radiology analysis may soon be considered malpractice. As long as the AI is accurate and productive in showing where cancer may exist, we don't care how the machine picked that specific spot on the x-ray, we are just happy to have another tool that helps save lives. But where the AI proposes treatments or outcomes, your clients – healthcare and otherwise – will need to be ready to defend those decisions. This means an entirely different baseline organization and feature set for than the AI currently envisioned or in use.

This is Pharma's AlphaGo moment when the potential for AI to radically transform the normal operating procedures and business models of the entire industry becomes tangibly obvious to the public. In the case of the AI industry, this happened in 2015, when AI company DeepMind succeeded in developing the first AI capable of beating a human Go champion in Go. This study by Insilico Medicine may be an analogous game-changing moment for Pharma. While it typically takes 2-3 years to go from initial drug discovery to preclinical validation, Insilico Medicine has done this in less than 2 months end-to-end. This is 15 times faster than Pharma companies capable of conducting the most efficient R&D processes. In a landmark study published in Nature Biotechnology on September 2, 2019, Insilico Medicine showed that they generated and validated a novel small molecule in just 46 days, and designed the drug from scratch based on specified molecular properties in just 21 days.

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.