Robots are taking over retail, well not quite. There has been much talk about robots taking on human roles within retail. Whilst this seems like an incredible innovation and still very futuristic, IBM Watson, the supercomputing power behind Pepper, the high profile robot with linguistic talents is one of several technologies such as advanced voice-recognition, data analytics, cognitive systems and automation that are going to transform our experience of shopping. What can be a painful drag, will, thanks to these advances, become more convenient and satisfying, whichever way we shop. Get right to the point When you go online to shop for a new coat the power of machine learning and artificial intelligence means you won't immediately be confronted by a bewildering range that takes hours to trawl through and ends up making you unhappy with your indecisiveness.

Is creativity in the marketing industry on a slow death march? Go to any marketing conference or seminar these days and you are likely to hear someone lament declining creative standards and the loss of'brand magic'. Financial pressures have put a squeeze on marketers' ability to unleash their creative instincts, but of greater concern is the role of technology and automation. The growing influence of programmatic media buying, machine learning and artificial intelligence (AI) have changed the way marketers perform their jobs, potentially putting creativity and independent thought at risk. HSBC's former head of marketing in EMEA Philip Mehl hit on this point in a recent Marketing Week roundtable when he declared: "Marketing used to be a creative challenge but it's a data challenge now."

Detecting cancer sooner rather than later can help prevent worst outcomes in many cases. Yet diagnosing different types of cancers in their earliest stages is not often possible. It requires years of training to gain the expertise and experience to do well in detecting cancerous tumor. Even with extensive training, some symptoms may be hard to distinguish from the signs of other similar diseases. For example, agreement in diagnosis for some forms of breast cancer can be as low as 48% and this percentage of disagreement is not surprising given the massive amount of information needed to review in order to make an accurate diagnoses and often within a limited time. To overcome these issues, Google researchers have developed an automated detection algorithm that is effective at identifying breast cancer.

When I reviewed Nebo, MyScript's award-winning, ink-to-digital-text app, I wrote that it's a "game changer" that "sets new standards for accurate handwriting recognition". Nebo's superior accuracy rests on MyScript's interactive ink technology. Recently I had the opportunity to ask MyScript's CTO, Pierre-Michel Lallican, how interactive ink works. Interactive ink is a complex system with three primary modules that are shown in the image at the top of this article. The Digital Ink Management module is the interface between the screen and interactive ink's text recognition and management functions.

Within the next five years, Google will produce a viable quantum computer. That's the stake the company has just planted. In the pages of Nature late last week, researchers from Google's Quantum AI Laboratory told the world that a machine leveraging the seemingly magical principles of quantum mechanics will soon outperform traditional computers on certain tasks. They said this long-anticipated technology will, among other things, improve the artificial intelligence that's already remaking the tech world. "The field of quantum computing will soon achieve a historic milestone," the team wrote.

Machine Intelligence (AI, ML and Deep Learning) requires a certain calibre of computer science talent. Today, this kind of talent is at the "top of the stack" of computer science. These cutting-edge capabilities used to be found at universities, and work on publicly-funded blue sky research; today, companies have the talent, and use it for private, applied purposes. The Economist touched on this in "Million Dollar Babies": That race for talent, is catalysing acquisitions by corporates interested in adding AI to their products and services. As an example, Ex-Googler Sebastian Thurn estimated that the going rate for self-driving engineering talent is $10 million per person.

Financial institutions can introduce new real-time schemes and payment types continually, support existing and emerging transaction...

A team of scientists has developed an artificial intelligence system called DeepStack that recently defeated professional poker players. The team of computing scientists from University of Alberta's Computer Poker Research Group, including researchers from Charles University in Prague and Czech Technical University, said DeepStack bridges the gap between approaches used for games of perfect information with those used for imperfect information games. "Poker has been a longstanding challenge in artificial intelligence," said Michael Bowling from the University of Alberta, Canada, in the paper published in the journal Science. It is the quintessential game of imperfect information in the sense that the players don't have the same information or share the same perspective while they are playing," Bowling added. Imperfect information games are a general mathematical model that describes how decision-makers interact. Artificial intelligence research has a storied history of using parlour games to study these models, but attention has been focused primarily on perfect information games. "We need new AI techniques that can handle cases where decision-makers have different perspectives," Bowling noted. DeepStack extends the ability to think about each situation during play -- which has been famously successful in games like checkers, chess, and Go -- to imperfect information games using a technique called continual re-solving. This allows DeepStack to determine the correct strategy for a particular poker situation without thinking about the entire game by using its "intuition" to evaluate how the game might play out in the near future.We train our system to learn the value of situations," Bowling said.

A trio of university students is giving Indian startups unprecedented access to Silicon Valley. In April 2016, 20-year-old Abhinav Kukreja and two of his fellow freshman students at the University of California, Berkeley, Anish Prabhu, and Aryaman Dalmia, created an incubator called Moonshot that connects Indian startups with experts, funds, and talent from Silicon Valley. Its four-month program gives companies access to over 20 mentors in India and California, exposes them to various venture capitalists and angel investors, and provides interns. The first batch of startups Moonshot incubated last year included student benefits platform Frapp, marketplace ListUP, small-business cash-flow management startup Numberz, home appliance automation company Hombot, online B2B billing solutions portal Pumpcharge, and real estate and rental management service Azuro. For their second round in January 2017, Moonshot zeroed in on startups "advancing science and technology, artificial intelligence (AI), machine learning, and companies that have a positive social impact," Kukreja, a computer science and statistics major, said.

When should a criminal defendant be required to await trial in jail rather than at home? Software could significantly improve judges' ability to make that call--reducing crime or the number of people stuck waiting in jail. In a new study from the National Bureau of Economic Research, economists and computer scientists trained an algorithm to predict whether defendants were a flight risk from their rap sheet and court records using data from hundreds of thousands of cases in New York City. When tested on over a hundred thousand more cases that it hadn't seen before, the algorithm proved better at predicting what defendants will do after release than judges. Jon Kleinberg, a computer science professor at Cornell involved in the research, says one goal of the project was to show policymakers the potential benefits to society of using machine learning in the criminal justice system.