Genpact (NYSE: G), a global leader in digitally-powered business process management and services, today launched its Neural Intelligence Platform that harnesses the power of artificial intelligence technologies, and brings middle and back office processing to the same level of digital automation as the front office to drive seamless customer journeys. Using a cognitive system powered by natural language processing, machine and deep learning techniques, the platform provides a versatile artificial intelligence-based capability that digitizes many business process solutions, including omni-channel management, contact center, account payable query management, and financial and accounting automation. The platform automates and improves processes, continually learns, and even can help anticipate customers' needs. Enterprises struggle to effectively integrate digital technologies into business processes that cut across the company to deliver the right customer experience. Analytics also must drive more upstream decisions around product design, customer sentiment, and distribution chain visibility.

Keep calm and carry on; artificial intelligence will not take all our jobs and achieve world domination, according to a report released by Forrester. Prominent figures including Elon Musk, co-chairman of OpenAI, and Professor Stephen Hawking have publicly warned people about how the advent of AI will cause an existential threat to humankind. The frenzy around AI has led to groups rallying for future safety measures. Google's DeepMind has even collaborated with Oxford University's Future of Humanity Institute to develop [PDF] an AI "panic button." Forrester, a technological research and advisory firm, believes the panic is exaggerated, however, and said: "Don't believe the hype – Google AlphaGo's gaming successes and IBM Watson will not usher in a dystopian triumph of machines over humans."

The world is quietly being reshaped by machine learning. We no longer need to teach computers how to perform complex tasks like image recognition or text translation: instead, we build systems that let them learn how to do it themselves. "It's not magic," says Greg Corrado, a senior research scientist at Google. The most powerful form of machine learning being used today, called "deep learning", builds a complex mathematical structure called a neural network based on vast quantities of data. Designed to be analogous to how a human brain works, neural networks themselves were first described in the 1930s.

Keep calm and carry on; artificial intelligence will not take all our jobs and achieve world domination, according to a report released by Forrester. Prominent figures including Elon Musk, co-chairman of OpenAI, and Professor Stephen Hawking have publicly warned people about how the advent of AI will cause an existential threat to humankind. The frenzy around AI has led to groups rallying for future safety measures. Google's DeepMind has even collaborated with Oxford University's Future of Humanity Institute to develop [PDF] an AI "panic button." Forrester, a technological research and advisory firm, believes the panic is exaggerated, however, and said: "Don't believe the hype – Google AlphaGo's gaming successes and IBM Watson will not usher in a dystopian triumph of machines over humans."

Tony Jebara is a Professor of Computer Science at Columbia University and Director of Machine Learning Research at Netflix. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in social networks, spatio-temporal data, vision and text. At the Deep Learning Summit in Boston, on April 2016, Tony presented'Double-Cover Inference in Deep Belief Networks'. I caught up with him to hear more about his work at Netflix and his thoughts on the recent advancements in deep learning. Tell us more about your work as Director of Machine Learning Research at Netflix.

Is there a nice deep learning library/wrapper like Keras which allows use for Torch? Also is there an easy way to convert between Theano, Tensorflow, and torch backends with Keras code?

June 27, Baltimore, MD - Youth Laboratories, the organizer of the first beauty contest judged by a panel of robots today announced the inclusion of AntiAgeist, an algorithm evaluating the difference between the chronological age of contest participants and the age predicted by a system of deep neural networks trained to predict human age. "We are very happy to have AntiAgeist on our jury of robot judges, since this is a rather novel idea of looking at beauty through the prism of how successfully the person is aging. We encourage teams from all over the world to submit algorithms and ideas on how machines can evaluate human beauty to the Beauty.AI contest. Best algorithms will get monetary prizes and will be promoted worldwide", said Anastasia Georgievskaya, general manager of Beauty.AI. Insilico Medicine specializes in drug discovery and biomarker development for a broad range of diseases with a mission to accelerate and improve lead generation and pre-clinical studies within biotechnology and pharmaceutical industries.

Apple (NASDAQ:AAPL) is years behind Google (NASDAQ:GOOG) (NASDAQ:GOOGL), Microsoft (NASDAQ:MSFT) and others in AI, full stop. Recently, rumors about a Siri software development kit have sparked a debate about Apple's plans to compete in machine learning. This comes after a series of announcements and product launches from various major technology companies about new language-driven devices and services, e.g. the Amazon (NASDAQ:AMZN) Echo or Google Home. Seeking Alpha contributor Mark Hibben has speculated about Apple's capability of closing that gap, which he believes is not very large. In this article, I will give a number of reasons why I believe he is wrong and why Apple is structurally not able to catch up anytime soon.

Mumbai: There is tremendous buzz around machine learning, broadly described as a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. However, despite an exponential increase in power, computers have typically proved incompetent at things that are really simple to human beings--like recognizing the dog in a picture containing a dog, or understanding speech. The trend, however, is changing. Consider'Deep Learning', which describes a collection of techniques that allow computational tasks that were previously thought impossible. Facebook Inc, for instance, uses it to identify faces, and when Google Inc recently announced that their algorithms could not only'see' a dog but also identify it as a Pomeranian, they heralded the maturity of Deep Learning techniques.

Using deep learning techniques, a group of researchers has trained a computer to recognise events in videos on YouTube -- even the ones the software has never seen before like riding a horse, baking cookies or eating at a restaurant. Researchers from Disney Research and Shanghai's Fudan University used both scene and object features from the video and enabled link between these visual elements and each type of event to be automatically determined by a machine-learning architecture known as neural network. "Notably, this approach not only works better than other methods in recognising events in videos, but is significantly better at identifying events that the computer programme has never or rarely encountered previously," said Leonid Sigal, senior research scientist at Disney Research. Automated techniques are essential for indexing, searching and analysing the incredible amount of video being created and uploaded daily to the Internet. "With multiple hours of video being uploaded to YouTube every second, there is no way to describe all of that content manually. If we don't know what's in all those videos, we can't find things we need and much of the videos' potential value is lost," noted Jessica Hodgins, vice president at Disney Research.