Spark AI Summit is the premier global event for the data and machine learning community to discuss the latest advances in open-source technologies such as Apache Spark, Delta Lake, MLflow, Koalas and TensorFlow as well as best practices for deploying AI in the real world. In addition to over 100 exciting breakout sessions, this year's Spark AI Summit in Amsterdam 15-17 October will feature keynotes from some of the leading thinkers and innovators in AI. We're pleased to announce Katie Bouman's keynote address at this year's Spark AI Summit Europe. Katie was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics, and she received her Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory in EECS. Currently, she specializes in using emerging computational methods to push the boundaries of imaging.

"When you start looking at connecting points, those intersections across the different siloed data collections and siloed departments, you might just be amazed at what you could discover." Most businesses have siloed departments that are pursuing digital transformation independently, creating "islands of innovation" throughout the enterprise. This episode discusses how to unify these efforts into a cohesive strategy to develop an intelligent enterprise. What could your business do if you had total control over your data? In NASA's case, a data transformation is what made the Hubble telescope and countless other amazing discoveries possible. Our guest this episode, Kirk Borne, was there when it happened. He began his career as an astrophysicist, combining datasets from multiple fields in unprecedented ways. Now Kirk's work is more down to earth. In this episode, he shares how businesses can "democratize data" across the enterprise. And he explains how data transparency plus intelligent analytics can lead to new efficiencies, better customer experiences, even entire new business models. "The key is this concept of the culture of experimentation, that you allow people to experiment with data." Kirk Borne is a Principal Data Scientist and Executive Advisor at Booz Allen Hamilton.

VentureBeat is pleased to announce its first ever Women in AI Awards at Transform 2019, honoring the changemakers in the field of artificial intelligence -- women leaders who are paving the way in rethinking process, policy, technology, and education as AI advances. These are women who demonstrate a commitment to changing the status quo as technology continues to disrupt established norms. This award will honor a woman who demonstrates exemplary leadership and progress in responsible AI. This award (two winners) will honor two women who have started companies that show significant promise in AI. Consideration will include business traction and positive impact of the technology in the AI space.

Auggie takes Her one step further. In the popular 2013 Spike Jonze film, the main character was a lonely man who fell in love with an artificially intelligent voice. In Auggie, the main character has a family and the artificially intelligent being has a physical form. But very similar issues arise. Co-written and directed by Matt Kane, Auggie stars legendary character actor and voice actor Richard Kind as Felix, a man who is given a very special gift at his retirement party.

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.