A bunch of startups is using tech to make everyday tasks much easier. One can book flights and buy mobile phone or insurance plans, simply by using a messaging app and chatting with a robot. Remember JARVIS, 'Iron Man' Tony Stark's intelligent assistant who not only controls everything in Stark's house but was also his closest friend. Some thing similar, far from that powerful yet, is available now to more ordinary folks. They are called chatbots, computer programs that can have automated text conversations with users using artificial intelligence (AI) and natural language processing.

A group of researchers at Google appear to have taken a leaf out of HBO comedy Silicon Valley's book for its latest project. The team has developed a way to use neural networks that mimic the workings of the human brain to compress images more efficiently than traditional methods. The researchers trained an AI system (built using Google's TensorFlow, which the company open sourced last year) to learn how compression works using 6 million photos for reference. It broke these images into 32 x 32 pixel pieces and selected 100 pieces with the least effective compression to learn from; the idea is that training with these difficult bits would make it a cakewalk to handle the rest of the image. The AI then predicts how a image would look after it's compressed and generates that result.

Two of today's major business and intellectual trends offer complementary insights about the challenge of making forecasts in a complex and rapidly changing world. Forty years of behavioral science research into the psychology of probabilistic reasoning have revealed the surprising extent to which people routinely base judgments and forecasts on systematically biased mental heuristics rather than careful assessments of evidence. These findings have fundamental implications for decision making, ranging from the quotidian (scouting baseball players and underwriting insurance contracts) to the strategic (estimating the time, expense, and likely success of a project or business initiative) to the existential (estimating security and terrorism risks). The bottom line: Unaided judgment is an unreliable guide to action. Consider psychologist Philip Tetlock's celebrated multiyear study concluding that even top journalists, historians, and political experts do little better than random chance at forecasting such political events as revolutions and regime changes.1 The second trend is the increasing ubiquity of data-driven decision making and artificial intelligence applications. Once again, an important lesson comes from behavioral science: A body of research dating back to the 1950s has established that even simple predictive models outperform human experts' ability to make predictions and forecasts. This implies that judiciously constructed predictive models can augment human intelligence by helping humans avoid common cognitive traps.

The video analytics industry is typically split into two distinct camps: (1) systems designed around rules and user-specified rules or models and (2) autonomous systems designed around machine learning. Supervised learning systems require heavy training and feedback to achieve the desired output, where unsupervised learning systems train themselves from the input data and require minimal human input. The video analytic solutions we saw in the market a decade ago seem rudimentary compared to today's offerings; partly due to the technology catching up with early promises and partly due to the industry's understanding and level-setting of expectations from the initial splash of analytics hyped as a panacea and the future of security. However, some of the extreme claims such as its ability to replace trained human operators, eliminate the need for well-designed camera placement, completely eliminate false positives, and determine a person's intent ahead of an action have proven to be more hype than reality for many end users.

The industry has come a long way from analog CCTV video surveillance systems. The days of a few low-resolution cameras being monitored by a security guard at a desk are becoming rarer in mid-sized to enterprise organizations. Putting the cameras on an enterprise network and treating the video like any other data gives us endless possibilities of what we can do with this powerful and complex information. We witnessed the rise of video content analysis (VCA) technology, or video analytics, in the early 2000s in response to the growth of cameras and general surveillance, spurred on by the emergence of IP cameras, the falling costs of data storage and IT infrastructure, a reactive security posture to a changing threat landscape, and the quick realization that traditional monitoring approaches couldn't keep pace with the growth in video data. The video analytics industry is typically split into two distinct camps: (1) systems designed around rules and user-specified rules or models and (2) autonomous systems designed around machine learning.

When we see two people meet, we can often predict what happens next: A handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. This week researchers from MIT's Computer Science and Artificial Intelligence Laboratory(CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before.

Machine learning and AI seem to be the way of the future. These techniques can help software developers create powerful applications that crunch data, analyze trends, and offer solutions that a developer may not even consider. But getting into machine learning is no easy task. You need to have a background in programming and your algorithm skills need to be at least somewhat competent. Python Machine Learning is one detailed book that covers machine learning from the angle of 3rd party Python libraries.

As a boy growing up in Wales, Dr. Steve Jones was fascinated by solving puzzles and finding solutions to challenging problems. As head of biometrics for the BC Cancer Agency, Dr. Jones arrives at his lab in the morning intent on saving another life. He uses information from the DNA sequencing of tumours followed by computational analysis to find the precise drug to attack and destroy that specific tumour. It's a high-tech approach that helps find solutions in some of the hardest cases. "We've done a number of analyses where the outcome has been able to improve the drug choice and help the patient," says Dr. "If we can introduce machine learning," he says, referring to artificial intelligence, "we can do this even better."

NEC Corporation and the University of Tokyo recently announced the commencement of a comprehensive industry-academia alliance for strengthening innovation in Japan. This alliance differs from others in that it has been created under an organization-to-organization agreement to promote comprehensive collaboration, including the sharing of visions and issues from the fundamental research phase to the commercialization of research results phase, the consideration of social acceptance of research results, and the development of human resources for the future. Among the first actions by this industry-academia alliance for innovation will be considering the magnitude of the impact of AI on society, to conclude the NEC/University of Tokyo Partnership Agreement for Future AI Research and Education in the field of Strategic Artificial Intelligence (AI), and to commence specific activities. Conventional industry-academia alliances have only engaged in limited, small-scale joint research related to individual technologies. However, in order to strengthen Japan's innovation and promote meaningful social changes, it will be imperative to carry out investigations on implications for ethics and legal systems with a focus on commercialization, as well as to create new collaborative alliances that are large in scale and cover broad areas, including human resource development.

PsiberLogic is a completely free, open-source fuzzy logic controller package for Python 3. Psibernetix proudly supports the amazing Python community, and is happy to contribute to Python's open-source movement. This package is for anyone seeking a high-performance, python3-callable package for creating fuzzy logic controllers. Details on ALPHA – a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.