IBM, Amazon, Facebook and Google are bent on building horizontal platforms around AI, but for Asia, the opportunity to build vertical solutions for startups is the forecasted trend. In an interview with EconomicTimes India, Sanat Rao, partner of IDG Venturs, a venture capital company, said "There is a really big opportunity in sectors such as healthcare and automobiles for AI. Concider a healthcare AI platform that can learn from thousands and even millions of images and can make more accurate diagnosis as it learns."

Emerging Seattle-based fintech startup, Kavout, just launched its new investment platform, driven by Artificial Intelligence (A.I.), for clients to find trading opportunities using tools powered by machine learning and big data, and with a beta-version of the platform made available to investors. As robo-advisors and automated trading products continue to emerge on the retail side, following the use of algorithmic trading and A.I.-driven technology by institutional firms, Kavout's announcement today appears to reflect the growing related interest from the market for such products. Kavout uses what it calls Kai, the core AI and machine learning process that fuels Kavout's main attributes and functions, including scanning historical SEC filings and stock quotes and examining millions of points of data ever second while analyzing stocks using an objective approach. There is also a Kai Score feature within the platform that provides a predictive analysis ranking reflecting data analytics processed for each security, and based on a stock's future performance related to its valuation, growth, momentum and other qualities. An excerpt of the platform as seen below, upon logging into the beta-version of Kavout, shows one of the Kai Score features. Commenting in an official corporate announcement, Kavout's CEO and co-founder Alex Lu added, "On Wall Street, you'll see that many hedge funds and investment banks are hiring top talent in artificial intelligence and machine learning."

Between the Terminator movies, Ava from'Ex Machina', Google's AlphaGo beating the world's best human Go players, machines mimicking Rembrandt's style to paint portraits, debates about morality and privacy, and Stephen Hawking's warnings about the consequences of intelligent machines manned by idiot humans; it's no wonder that Artificial Intelligence (AI) has already made people a little uncomfortable. In the marketing world, it's caused the customary outbreak of confusion. Thus resulting in a series of obtuse declarations like "AI will change everything. EVERYTHING!" that are typical of the industry. Any time anything new threatens to upset business as usual, comrades in the marketing and advertising industry especially lunge headlong into an existential crisis.

For more than 40 years, no single director has more defined what we think of when we think of the movies than Steven Spielberg. To date, his feature films have grossed 4.3 billion in North America and 9.2 billion worldwide, more than any other filmmaker in history by a comfortable margin. His movies have been nominated for 128 Academy Awards and won 32, and Spielberg personally has been nominated for 16 Oscars, winning three (Best Director for Saving Private Ryan, and Best Director and Best Picture for Schindler's List). And if that's not enough, Spielberg has also presided over at least two of the most transformative changes of the last 50 years in the movie industry: the creation of the summer blockbuster (with Jaws) and the proliferation of computer-generated imagery in visual effects (with Jurassic Park). To be sure, Spielberg has not done any of this alone. With George Lucas and Harrison Ford, he helped create Indiana Jones. With Tom Hanks, he established an ongoing creative partnership (and lifelong friendship). His longtime producer Kathleen Kennedy -- the woman currently shepherding the revival of Star Wars -- got her start as Spielberg's secretary. Just about every one of his films have been tightly edited by Michael Kahn and majestically scored by John Williams. And he's collaborated with a small stable of top-flight screenwriters, including David Koepp, Richard Curtis, Eric Roth, Lawrence Kasdan, Steven Zaillian, Tom Stoppard, Tony Kushner, Joel and Ethan Coen, and, on his newest film The BFG, Melissa Mathison. When we go to a Spielberg movie, we know we will see a film made with consummate craft and exhilarating visual style -- few directors know better how to harness the tools of pure cinema. But I would argue the artistic constant that has informed Spielberg's career and success more than any other has been his seemingly limitless capacity for empathy. "Movies are like a machine that generates empathy," the late Roger Ebert once said. "It lets you understand a bit more about different hopes, aspirations, dreams and fears. It helps us to identify with the people who are sharing this journey with us." Ebert might as well have been describing Spielberg's entire career, and I know that because, like a crazy person, I screened all 29 of Spielberg's theatrical feature films in chronological order, and then ranked them from worst to best. I also skipped 1983's Twilight Zone: The Movie, since Spielberg directed just one of five segments in the film.) By my count, only three of Spielberg's movies are irredeemably bad.

Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such as searching for Earth-like planets and solar twins, understanding the mechanisms that govern stellar evolution, and tracing the dynamics of our Galaxy. The volume of data that is becoming available, however, brings with it the need to process this information accurately and rapidly. While existing methods can constrain fundamental stellar parameters such as ages, masses, and radii from these observations, they require substantial computational efforts to do so. We develop a method based on machine learning for rapidly estimating fundamental parameters of main-sequence solar-like stars from classical and asteroseismic observations. We first demonstrate this method on a hare-and-hound exercise and then apply it to the Sun, 16 Cyg A & B, and 34 planet-hosting candidates that have been observed by the Kepler spacecraft. We find that our estimates and their associated uncertainties are comparable to the results of other methods, but with the additional benefit of being able to explore many more stellar parameters while using much less computation time. We furthermore use this method to present evidence for an empirical diffusion-mass relation. Our method is open source and freely available for the community to use. The source code for all analyses and for all figures appearing in this manuscript can be found electronically at https://github.com/earlbellinger/asteroseismology

Jordan is renowned in the scientific community as an expert and leading researcher in the fields of artificial intelligence and machine learning. "Jibo is breaking new ground by bringing a human element to the robot experience -- something I believe the world needs and will benefit from embracing," said Michael I. Jordan, advisory board member of Jibo Inc. "My background and research in AI is uniquely suited to help in advancing Jibo's learning capabilities and developing his role and relationships within the home environment." Currently the Pehong Chen distinguished professor in electrical engineering, computer science and statistics at the University of California, Berkeley, Jordan has developed a wide range of novel methods in machine learning, natural language processing and signal processing. Jibo Inc. will apply artificial intelligence and machine learning techniques to the field of social rapport and relationships. Jordan joins the advisory board comprised of 10 industry leaders in fields central to Jibo Inc.'s ongoing development including voice and natural language technologies, artificial intelligence, human factors, behavioral science and more.

Despite the tremendous progress we've made in the fields of robotics and artificial intelligence, building a robot that can convincingly emulate normal human behavior remains merely a fantasy at this stage. Some of them can speak and maybe even hold a conversation, but no robot comes close to being a real life'Ex Machina' yet. For the most part, the humanoid robots we've built so far come across as overly mechanical, unnervingly awkward and threateningly soulless… but somehow all of this creepiness makes them irresistibly fascinating. Tara the Android is perhaps the godmother of the robotic creepfest. While the video of this eerie singing mannequin was first uploaded back in 2009 and has since received over seven million views, little information is available about either Tara or her creator.

WASHINGTON – Coalition forces fighting the Islamic State group in northern Syria destroyed one of their own combat drones Tuesday after the unmanned aircraft crashed, the U.S. Air Force said. The MQ-9 Reaper -- a type of armed drone -- crashed while flying a combat mission after remote pilots lost "positive control" of the aircraft, the Air Force said in a statement. "The remotely piloted aircraft crash was not due to enemy fire. There are no reports of civilian injuries or damage to civilian property at the crash site," the statement reads. Coalition craft then destroyed what was left of the drone.

A U.S. military drone crashed in northern Syria while carrying out a combat mission against ISIS, the Air Force said Tuesday. According to a statement, the MQ-9 Reaper drone was not brought down by enemy fire and there were no reports of civilian injuries or damage in the crash. The statement added that aircraft of the U.S.-led coalition destroyed the crashed drone before it could fall into ISIS hands. An investigation will be conducted to determin the exact cause of the crash. The Obama administration has often used unmanned aircraft to target leaders of terror groups.

Jibo will be one of the first robots for the home that can actually see its users. The first wave of home robots is already upon us with the Amazon Echo, and the coming Google Home. While these barebones bots can take simple commands, they can't really add anything to the conversation or scoot around your home. That's what the makers of Jibo are trying to create--a robot that can initiate conversation to complete tasks in the home--and they're tapping some of the world's foremost A.I. researchers to do it. Today, Jibo Inc. announces that its adding veteran researcher Michael I. Jordan to its advisory board.