China's internet-search giant Baidu is planning to mass produce driverless electric cars in five years, bringing the fight to Google and Apple.

Can India become the top 3 market for AI & Analytics in 2025? NASSCOM's Big Data & Analytics Summit 2016 in Hyderabad had an Interesting theme this year "Rise of Algorithms and AI: Complexity to Competitive Advantage" Over 800 Data analytics executives gathered to share real-world experiences at this event, three key themes emerged from these discussions: Cognitive Computing, Industrialization of Algorithms and Platform as a Service using Analytics (PAAS). Everyone echoed in the forum where "Data" is the new Gold and it is an asset for every organization, small and large with strong emphasis on Quality of the "Data". Data is evolving rapidly everyday " VUCA - Volatility,Uncertainty, Complexity & Ambiguity ". Gesture Recognition via Interactive Data will bring analytics to your finger tips.

Sony Corp. President Kazuo Hirai said Wednesday the company is making progress in its transition from restructuring to seeking profitability, pinning hopes on the growth potential of games and artificial intelligence-based robots. "We got off to a good start in the first year toward generating profits and investing in growth," Hirai told a press conference in Tokyo. Sony kept to its operating profit target of more than 500 billion yen ( 4.9 billion) set under its three-year business plan through fiscal 2017, although he called it "challenging." With an aim of developing AI-based robots, Sony will set up the "Sony Innovation Fund" in July to invest in startups, marking Sony's reentry into a market it quit when it ended production in 2006 of robotic dog AIBO. "Robots that can connect with our customers at heart...and receive affection are among items we want to develop," Hirai said.

Chance Dorland spoke with Bloomberg TV's Angie Lau, Bloomberg News' David Merritt, Heather Timmons of Quartz, the AP's Paul Cheung & drone photographer Seongjoo Cho after their Asian American Journalists Association "Journalism in the Age of Artificial Intelligence" panel discussion at this weekend's New.Now.Next media conference in Seoul.

Artificial brains are man-made machines that are just as intelligent, creative, and self-aware as humans. No such machine has yet been built, but it is only a matter of time. This website tracks the latest scientific and technological progress. SyNAPSE is a DARPA-funded program to develop neuromorphic microprocessor systems that match the intelligence, physical size, and low power consumption of animal brains. Their approach is to first test neural networks in simulation on a supercomputer.

Whether it's Deep Blue beating Garry Kasparov at chess, Watson defeating Ken Jennings at Jeopardy!, or Google DeepMind's AlphaGO besting Lee Sedo at Go, artificial intelligence can't be underestimated when it comes to taking on the champions and winning. That's because a new AI system called ALPHA -- developed by recent University of Cincinnati doctoral graduate Nick Ernest, now CEO of Psibernetix -- recently defeated retired United States Air Force Colonel Gene Lee in an air combat simulator. Not only did Colonel Lee, who has extensive aerial combat experience as an instructor, fail to kill ALPHA's aircraft during combat, he was also repeatedly shot out of the air by the bot. According to Lee, ALPHA is "the most aggressive, responsive, dynamic and credible AI I've seen to date." "ALPHA is an incredibly difficult opponent to face," Psibernetix CEO Nick Ernest tells Digital Trends. "Even flying against other pilots when ALPHA has severe handicaps to a number of its systems -- including speed, turning, missile capability and sensors -- it is able to win.

Two essential tasks in managing description logic knowledge bases are eliminating problematic axioms and incorporating newly formed ones. Such elimination and incorporation are formalised as the operations of contraction and revision in belief change. In this paper, we deal with contraction and revision for the DL-Lite family through a model-theoretic approach. Standard description logic semantics yields an infinite number of models for DL-Lite knowledge bases, thus it is difficult to develop algorithms for contraction and revision that involve DL models. The key to our approach is the introduction of an alternative semantics called type semantics which can replace the standard semantics in characterising the standard inference tasks of DL-Lite. Type semantics has several advantages over the standard one. It is more succinct and importantly, with a finite signature, the semantics always yields a finite number of models. We then define model-based contraction and revision functions for DL-Lite knowledge bases under type semantics and provide representation theorems for them. Finally, the finiteness and succinctness of type semantics allow us to develop tractable algorithms for instantiating the functions.

With this positional paper we present a representation learning view on predicate invention. The intention of this proposal is to bridge the relational and deep learning communities on the problem of predicate invention. We propose a theory reconstruction approach, a formalism that extends autoencoder approach to representation learning to the relational settings. Our intention is to start a discussion to define a unifying framework for predicate invention and theory revision.

This paper addresses the problem of blind demixing of instantaneous mixtures in a multiple-input multiple-output communication system. The main objective is to present efficient blind source separation (BSS) algorithms dedicated to moderate or high-order QAM constellations. Four new iterative batch BSS algorithms are presented dealing with the multimodulus (MM) and alphabet matched (AM) criteria. For the optimization of these cost functions, iterative methods of Givens and hyperbolic rotations are used. A pre-whitening operation is also utilized to reduce the complexity of design problem. It is noticed that the designed algorithms using Givens rotations gives satisfactory performance only for large number of samples. However, for small number of samples, the algorithms designed by combining both Givens and hyperbolic rotations compensate for the ill-whitening that occurs in this case and thus improves the performance. Two algorithms dealing with the MM criterion are presented for moderate order QAM signals such as 16-QAM. The other two dealing with the AM criterion are presented for high-order QAM signals. These methods are finally compared with the state of art batch BSS algorithms in terms of signal-to-interference and noise ratio, symbol error rate and convergence rate. Simulation results show that the proposed methods outperform the contemporary batch BSS algorithms.

It was at Carnegie Mellon where Dr. Hon began seriously building the foundation for his later work in machine-human interaction. His PhD supervisor, Turing Award winner Raj Reddy, was a former student of John McCarthy, the computer scientist who coined the term'artificial intelligence' in 1956 and is widely known as the father of AI. This connection would have richly benefited Dr. Hon, except that an'AI Winter' was happening from 1986 to 1992 when he was completing his doctorate degree. Recalling the climate of this period, "government agencies, universities and even companies slowed down or stopped funding to the field. It was only until the first decade of the 2000's that AI got so hot," said Dr. Hon. Dr. Hon credits the power of improved hardware and software and Big Data for heating up the AI scene.