Some people seem to get along with everyone. Most people have an innate ability to read other people and quickly adjust their own style to match. We all do it to some degree, but some of us do it better than others. Consciously and unconsciously, we read other people by looking for cues – we evaluate facial expressions, vocal tone, body language, posture and gestures to assess how similar theirs are to our own. Sometimes we adjust accordingly and sometimes we don't, but the real purpose of reading other people is to reduce the ambiguity of our social interactions Great communicators exploit this phenomenon to build rapport with virtually anyone.

Technology major Hewlett-Packard Enterprise (HPE) today announced the commercial launch of Haven OnDemand, a cloud platform that provides advanced machine learning application programme interfaces and services that enable developers, startups and enterprises to build data-rich mobile and enterprise applications. The company also announced a new version of Idol analytics platform, which applies data analytics and machine learning for organisations to automate and supplement a vast array of manual-based tasks such as trend analysis and video surveillance.

HPE Haven OnDemand is a platform for building next generation solutions and apps using text analysis, speech recognition, image analysis, indexing and search APIs. Simply put, developers and businesses use APIs to add advanced capabilities such as natural language processing, machine learning, and predictive analytics to their applications. Building your next cognitive computing, machine learning, artificial intelligence, or data rich application is much faster and less hassle with these handy self-service resources from HPE Haven OnDemand.

Ali Kheyrollahi is a solution architect building web scale solutions, trying to marry scalability with performance. He loves HTTP, API design and business-modelling DDD-style. An Open Source author and blogger, he tries to give back to the community what he has gratefully taken from it. He is a blogger and has co-authored a book. "BUILD STUFF" is a Software Development Conference for people who actually build stuff.

Q: Whenever I teach artificial intelligence (AI), machine learning or intelligent robotics, my class and I end up having what I call'The Terminator Conversation'. My point in this conversation is that the dangers from AI are overblown by media and non-understanding news, and the real danger is the same danger in any complex, less-than-fully-understood code: edge case unpredictability. In my opinion, this is different from'dangerous AI' as most people perceive it, in that the software has no motives, no sentience and no evil morality, and is merely (ruthlessly) trying to optimise a function that we ourselves wrote and designed. Your viewpoints (and Elon Musk's) are often presented by the media as a belief in'evil AI,' though of course that's not what your signed letter says. Students that are aware of these reports challenge my view, and we always end up having a pretty enjoyable conversation. How would you represent your own beliefs to my class?

Associate Professor Julian Togelius works at the intersection of artificial intelligence (AI) and games--a largely unexplored juncture that he has shown can be the site of visionary and mind-expanding research. Could games provide a better AI test bed than robots, which--despite the way they excite public imagination--can be slow, unwieldy and expensive? According to him, the answer is resoundingly yes. "I'm teaching computers to be more creative than humans," he says. Togelius, a member of the NYU Tandon School of Engineering's Department of Computer Science and Engineering, is at the forefront of the study of procedural content generation (PCG)--the process of creating game content (such as levels, maps, rules, and environments) by employing algorithms, rather than direct user input.

Although the machine-learning discipline is more than 30 years old, the nascent technology is poised for a serious growth spurt that may put puberty to shame. "As machine learning becomes more mainstream and there's more understanding of how the technology works, I thing we will see exponential growth," said Drew Warren, president and CEO of smart-data processing vendor Recognos Financial. "As organizations' demand increases and grows more powerful, we see more organizations allotting more money to bring its development to the next level." Warren attributes machine learning's increased pace of evolution to better and more abundant enabling technologies, improved performance, and the rise of more specialized vendors that have the potential to reconfigure the technology's linear improvement curve into an exponential one. The advent of open-source resources like Google's TensorFlow machine-learning library has made development much easier, noted Nitin Rakesh, president and CEO of technology and knowledge-processing outsourcing provider Syntel.

Could you invent an autonomous hacking system that could find and fix vulnerabilities in computer systems before criminals could exploit them, and without any human being involved? That's the challenge faced by seven teams competing in Darpa's Cyber Grand Challenge in August. Each of the teams has already won 750,000 for qualifying and must now put their hacking systems up against six others in a game of "capture the flag". The software must be able to attack the other team's vulnerabilities as well as find and fix weaknesses in their own software – all while protecting its performance and functionality. The winning team will walk away with 2m.

Apple's purchase of Emotient last month, a California-based firm that uses Artificial Intelligence (AI) to interpret emotions from facial expressions, signals that big changes are coming to mobile devices. The move means that another tech giant has entered the AI arms race alongside Google and Facebook. This fact alone should have brands learning about the new technologies and integrations available to better engage with consumers on an emotional level. AI's capacity to respond to mood, gesture, natural language, and other complex human behaviors solves many issues facing consumers and brands, including, discovery, attention, and ease of product/service use. The big three" tech companies – Google, Apple and Facebook -- are leveraging AI in different ways to gain a competitive advantage in an economy where attention is a scarce resource.

Last week artificial intelligence, for the first time in history, secured a definitive victory over a grandmaster Go player. While chess playing humans were outpaced by computer brains almost two decades ago, Go is multitudes more complex than chess, with an estimated 10761 possible games (Chess tops out around 10120). Given this complexity, experts didn't expect artificial intelligence to be able to beat a master Go player for another ten years. But this news shouldn't send Go players into a panic about the coming robot insurrection. Chess players, for the most part, have chilled out about the fact that computers are now much better than we are.