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.

When people hear about artificial intelligence, they have one of two responses: they are terrified of a Skynet dystopia, or they are excited for the new possibilities afforded by machine learning and robotics. While 2017 will not be the year that humanoid, Westworld-esque robots work alongside us or take over all of our jobs, we will definitely be seeing an even smarter circulation of "alternative facts". We will see greater capabilities from AI in facilitating business processes such as services, software delivery and IT infrastructure changes. Google has built a hub for chatbots to fetch information from the net, Freshdesk acquired Chatimity to strengthen its customer service chatbot capabilities, and Microsoft has had another bash at its AI chatbot with Zo. In process flow scenarios, ChatOps bots will be more fluid in enabling processes using simple commands. You could write something like "I need help with ticket 6876 from network, database, and payment processing," and all necessary information would be pulled for you, from across all relevant systems.

Three years ago, artificial intelligence research firm DeepMind was acquired by Google for a reported £400m. As part of the acquisition, Google agreed to set up an ethics and safety board to ensure that its AI technology is not abused. The existence of the ethics board wasn't confirmed at the time of the acquisition announcement, and the public only became aware of it through a leak to industry news site The Information. But in the years since, senior members of DeepMind have publicly confirmed the board's existence, arguing that it is one of the ways that the company is trying to "lead the way" on ethical issues in AI. But in all that time DeepMind has consistently refused to say who is on the board, what it discusses, or publicly confirm whether or not it has even officially met. The Guardian has asked DeepMind and Google multiple times since the acquisition on 26 January 2014 for transparency around the board, and received just one answer on the record.

Machine learning is cresting the fresh wave of 2017 HR trends. Gartner research predicts algorithms will positively alter the behavior of over one billion global workers by 2020, while over 3 million people can look forward to "roboboss" supervisors. Yvonne Bauer, Head of Predictive Analytics at SAP SuccessFactors, sees machine learning becoming more widespread this year as part of HR's steady progression from art to data-driven science. "More companies will look into machine learning, moving from individual projects to actual products built into HCM suites," she said. "Conversational interfaces like chat bots and natural language processing will emerge this year, allowing companies to change how workers interact with the system and derive insights from those activities, including what people are working on and how engaged they are."