If you're worried about the possibility of skin cancer, you might not have to depend solely on the keen eye of a dermatologist to spot signs of trouble. Stanford researchers (including tech luminary Sebastian Thrun) have discovered that a deep learning algorithm is about as effective as humans at identifying skin cancer. By training an existing Google image recognition algorithm using over 130,000 photos of skin lesions representing 2,000 diseases, the team made an AI system that could detect both different cancers and benign lesions with uncanny accuracy. In early tests, its performance was "at least" 91 percent that of its flesh-and-blood counterparts.

"I returned, and saw under the sun, that the race is not to the swift, nor the battle to the strong, neither yet bread to the wise, nor yet riches to men of understanding, nor yet favour to men of skill; but time and chance happeneth to them all." Chance appears to name a single, unitary thing. But its genealogy, its family history, turns out to be a tangled one. One way to understand its branching origins is to turn to literature: We may look, in turn, to two very different novels. Anton Chigurh, the antagonist of Cormac McCarthy's novel No Country for Old Men, forces his victims to guess the outcome of a coin toss, taking their life if they guess in error. That chance is entirely contained, not in Chigurh, but in the toss--in nature itself. This is one source of uncertainty.

Computers are now defeating us in casino table games. Libratus, an Artificial Intelligence system developed by researchers from Carnegie Mellon University has been squaring off against four professional poker players since Jan 11. Libratus is up nearly $800,000 through 80,000 hands of heads-up no limit Texas Hold'em against the four pros. "It's not about the money," said Jason Les, a professional from Costa Mesa, CA, in an interview with The Verge. With 40,000 hands left to play, the $800,000 deficit is nearly impossible for the humans to make up.

To modernize cybersecurity for Livingston County, Mich., officials turned to a machine learning tool that can find anomalies in behaviors without previous knowledge of what to look for. Darktrace's Enterprise Immune System is powered by unsupervised machine learning, meaning county officials didn't have to tell it what to watch out for using rules or signatures. Instead, they plugged it in and let it run for three weeks so that it could learn about the network's typical behavior, establishing what's called a "pattern of life." Then when the system detects something out of the ordinary, an alert is issued in real time. "A tool like this works best when it's placed where it can see the traffic we're most interested in," county Deputy Chief Information Security Officer Paul Curylo said. "We placed it such that we can see traffic of interest traversing through our core as well as traffic traversing out to the internet."

APM (Application Performance Management) is on the precipice of dramatic change. The complexity of cloud native and new stack applications is making many of the traditional ways of monitoring applications irrelevant. With applications which can elastically scale using containers to meet unprecedented amounts of demand, the APM industry needs to reconsider the focus on simple problem identification to provide value for businesses. Understanding how application performance visibility can provide better data as to what should get scaled and what does not need to get scaled is just as important to the business. Managing complexity is the challenge of the next generation of applications.

Today it has been announced that I'm leading a $15 million funding round in Tact, a new generation of Enterprise Software company along with other investors including Microsoft Ventures and previous investors Accel and Redpoint Rather than just waxing lyrically about how great the company is I thought I'd provide some context about why I invested and also about a fundamental change I see in the coming years in the way enterprise software is used. Tact was founded by Chuck Ganapathi, who was formerly the SVP of Products at Salesforce.com having led initiatives like their chat product, CRM and mobile. For six years before that he was at Siebel who was the market leader in CRM before Salesforce, and he has both a masters in product design from Stanford and an MBA. But more importantly I worked with Chuck 10 years ago when I was at Salesforce.com and have kept a relationship over the past decade. Put simply Chuck is amongst the smartest and most knowledgeable leaders in the CRM industry and in the components I consider the future of enterprise software -- voice driving input/output, chat-based computing and mobile-first design.

Legal status, code of ethics, tax situation… Europe is pondering robots. On January 12, members of the Committee on Legal Affairs met to discuss regulations for robotics to be adopted by the European Union. Already in May 2016, the same commission proposed to give robots the status of "electronic persons", and thus rights and duties. The idea, among others, was to create a code of ethics determining responsibility in case of damages caused by a robot. The automobile sector and its autonomous vehicles is "the sector that is most in need of European and international regulations," emphasized Belgian Eurodeputy Mady Delvaux.

Artificial intelligence (AI) is poised to alter data infrastructure and processes in some very significant ways over the next decade, but some applications are riper for deployment than others. While stories abound about talking data centers, self-managing ecosystems, and the wealth of knowledge to be gleaned from AI-powered analytics, the initial implementations will likely center on more practical needs, like managing the flow of data from connected devices and streamlining the interplay between hardware, software and various management systems. According to eWeek's Chris Preimesberger, AI is already a facet of key industry verticals, such as financial services, health care and telecommunications, all of which stand to benefit tremendously from faster, more accurate data processing. In many cases, the drive to implement AI is fueled by a lack of expertise in the knowledge workforce regarding advanced analytics, even though many workers have deep knowledge of their industries. With many vertical-specific tools hitting the channel, AI is considered the best way to leverage that knowledge to develop new products and new market opportunities.

Follow us on Twitter: https://twitter.com/JourneymanVOD Computer scientist Alan Turing created a conversation test for robots that has been a benchmark for over sixty years. But as humanoid robots develop every day, can any yet fool us into thinking they are human? From robots like Atlas at Boston Dynamics, built to withstand physical bullying, to the uncanny silicon and hair humanoids developed at Hanson robotics, we take a look at some of the most advanced androids ever created. Yet imitating simple conversation can still be a test too far, as Microsoft discovered after the widely publicised failure of their Twitter robot, Tay. Alistair Charlton, senior tech reporter for the International Business Times, argues that the industry has more successful options to pursue.

Google is planning to bring artificial intelligence and machine learning tools to the Raspberry Pi. The low-cost credit-card sized computer is widely used by schools and the maker community for programming devices. Google has asked makers to complete a survey about what smart tools would be "most helpful". And it suggests tools to aid face and emotion recognition, speech-to-text translation, natural language processing and sentiment analysis. Google has previously developed a range of tools for machine learning, internet of things devices, wearables, robotics and home automation.