NEW YORK – Artificial intelligence has made history by beating humans in poker for the first time, the last remaining game in which humans had managed to maintain the upper hand. Libratus, an AI built by Carnegie Mellon University (CMU), racked up over $1.7 million worth of chips against four of the top professional poker players in the world during a 20-day marathon poker tournament that ended Tuesday in Philadelphia. While machines have beaten humans over the last two decade in chess, checkers, and most recently in the ancient game of go, Libratus' victory is significant because poker is a game of imperfect information -- similar to the real world, where not all problems are laid out and the difficulty in figuring out human behavior is one of the main reasons why it was considered immune to machines. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," said Tuomas Sandholm, professor of computer science at CMU who created Libratus with a Ph.D. student Noam Brown, said on Wednesday. The victory prompted inquiries from companies all over the world seeking to use Libratus' algorithm for problem solving.

In its last financial quarter, it sold $32bn (£25.6bn) That "other" category encompasses everything from crucifixes to sex toys, board games to plyboard, and mousemats printed with the faces of obscure TV and Radio personalities. It has also diversified beyond its simple shopping business: the company will sell you something to be delivered in less than one hour, food from restaurants, and even digital content to be watched on your TV or listened to on your phone. And, of course, it has a hardware business which many other companies would kill for, producing ebook readers and tablets, and single-handedly creating the product category of "smart speaker" with the Echo. But there's another chunk of Amazon that you're less likely to know about.

It's been a while since we last wrote about the latest changes in our command line tool, BigMLer. In the meantime, two unsupervised learning approaches have been added to the BigML toolset: Clusters and Anomaly Detectors. Clusters are useful to group together instances that are similar to each other and dissimilar to those in other groups, according to their features. Anomaly Detectors, on the contrary, try to reveal which instances are dissimilar from the global pattern. Clusters and anomaly detectors can be used in market segmentation or fraud detection respectively.

Anyone can look into a crystal ball, but it takes a visionary to translate vague details into useful information. Noted analyst, R "Ray" Wang is just such a visionary. As the founder and chairman of Silicon Valley-based Constellation Research, Wang advises companies on topics including how disruptive technologies impact the future of work, next generation customer experience, cross-channel commerce, big data and analytics, digital marketing transformation, and technology optimization. His latest book, Disrupting Digital Business, is required reading in many boardrooms. We spoke with Ray during the Oracle Cloud Days event in Denver to get his perspective on the role of cloud-based analytics in managing disruption and maximizing business opportunities.

Digital technologies and advanced analytics have the potential to create the invisible bank of the future. Powered by artificial intelligence (AI) and activated by voice, virtual banking assistants can become an integral part of consumers' daily lives. Banking today is becoming less and less a place you go, and more something that is hidden from view behind digital banking and commerce apps. Once an account is opened at a bank or credit union, there is less need to stop into a branch, since functions like deposits, borrowing, payments and transfers can be done without personal interaction through online and mobile devices. According to a new report published by KPMG, "Meet Eva – Your Enlightened Virtual Assistant and the Future Face of the Invisible Bank", technologies like Apple's Siri, Amazon's Alexa or Samsung's Viv will enable an even greater shift in banks and banking by 2030.

Artificial Lawyer recently caught up with Michael Kholodenko, co-founder of Seattle, US-based start-up, Binadox, which makes use of Natural Language Processing (NLP) to provide a compliance tool for software licences that can prevent costly legal violations. At first you may think this does not sound that world-changing, but sometimes it is the very precise uses of AI technology that end up creating a very successful and useful product for lawyers and legal departments. This would appear to be doubly so, as Bindox points out, because getting your software licences in a mess, or just allowing staff to click'Yes – Accept Terms' can open a Pandora's box of legal problems. Bindadox seeks to meet this problem head on in an automated way. Fundamentally the software seeks to answer two questions: what happens when an employee downloads new software or subscribes to a cloud service (SaaS) and clicks on the little'Yes' box that agrees to the licence terms, with all the legal issues that suddenly connects to?

Today in Washington DC, leading US and UK scientists are meeting to share dispatches from the frontiers of machine learning – an area of research that is creating new breakthroughs in artificial intelligence (AI). Their meeting follows the publication of a set of principles for beneficial AI that emerged from a conference earlier this year at a place with an important history. In February 1975, 140 people – mostly scientists, with a few assorted lawyers, journalists and others – gathered at a conference centre on the California coast. A magazine article from the time by Michael Rogers, one of the few journalists allowed in, reported that most of the four days' discussion was about the scientific possibilities of genetic modification. Two years earlier, scientists had begun using recombinant DNA to genetically modify viruses. The Promethean nature of this new tool prompted scientists to impose a moratorium on such experiments until they had worked out the risks.

Four years ago, Neil Mehta was among the 15 million people who watched Ken Jennings and Brad Rutter--the world's greatest "Jeopardy!" "Jeopardy!" is a television game show in which the host challenges contestants with answers for which they must then supply the questions--a task that involves some seriously complicated cognition. Artificial-intelligence experts described Watson's triumph as even more extraordinary than IBM supercomputer Deep Blue's history-making 1997 defeat of chess grandmaster Garry Kasparov. To an AI aficionado, Watson was a tour de force of language analysis and machine reasoning. To Mehta, a physician and professor at the world-renowned Cleveland Clinic, Watson was a question unto itself: What might be possible were Watson's powers turned to medicine? "I love technology, and I was rooting for Watson," says Mehta. "I knew that the world was changing.

Information technologies are accelerating at an exponential rate, ushering in the fourth industrial revolution. This is a digital revolution and the pace of change is unprecedented. This revolution incorporates machine learning (think parallel processing and neural networks) and the concept of self-assembly or self-programmability. As technologies continue to advance, they accelerate the progress of other technologies, and so on, and so on. Thanks to parallel processing, big data, cloud technology, and advanced algorithms, Artificial Intelligence (AI) and machine learning are becoming more powerful.

I didn't worry too much when computers beat humans at checkers, chess, or Go. It was, after all, only a matter of time before someone built a powerful computer with a vast database of known game situations. But now that machines are beating professionals at poker -- a game of imperfect information -- a question must be asked: Is artificial intelligence starting to threaten people in creative jobs? In terms of game complexity -- the number of allowed positions reachable in the course of a game -- no-limit Texas Hold'em poker, isn't an AI researcher's worst nightmare. The chess game tree has 10 to the 120th degree nodes. The one for Go has 10 to the 170th degree.