A robot has just set a new record for the fastest-solved Rubik's Cube, according to its makers. The Sub1 Reloaded robot took just 0.637 seconds to analyse the toy and make 21 moves, so that each of the cube's sides showed a single colour. That beats a previous record of 0.887 seconds, which was achieved by an earlier version of the same machine using a different processor. Infineon provided its chip to highlight advancements in self-driving car tech. But one expert has questioned the point of the stunt.

Cape Analytics has raised $14 million to use computer vision and machine learning to improve automated property underwriting for insurance companies. Formation 8 led the round, with participation from XL Innovate, Data Collective, Lux Capital, Khosla Ventures, Promus Ventures, and Montage Ventures. The funding is one more application of machine learning and computer vision for business automation. Palo Alto, Calif.-based Cape Analytics starts with images of a home to help assess the home's value and allow an insurance carrier to deliver more accurate and fast quotes. The funding will allow Cape Analytics to expand its world-class engineering and sales teams as it brings its proprietary data to more regions and customers around the United States.

Ever think your smartphone is vibrating or ringing when it isn't? It's such a common phenomenon that researchers gave the problem, which plagues between 70% and 90% of smartphone users, a name: phantom vibration syndrome. Some people find themselves so unhealthily attached to or distracted by their digital devices that they go on technology fasts to help them detach. Entrepreneur Rand Hindi says the problem isn't technology but the way we interact with it. Hindi, who was born in Lebanon and grew up in France, started coding at age 10, launched a web development agency at age 15, and earned his PhD in bioinformatics while working as a consultant on algorithmic trading.

Implementing Enterprise AI is a unique and limited edition course that is focussed on AI Engineering / AI for the Enterprise. The course is launched for the first time and has limited spaces. Created in partnership with H2O.ai, the course uses Open Source technology to work with AI use cases. Successful participants will receive a certificate of completion and also validation of their project from H2O.ai. To sign up or learn more, email info@futuretext.com The course targets developers and Architects who want to transition their career to Enterprise AI.

A team of researchers at the Google Brain office have been working on a project that involved creating three separate neural networks that between them have the ability to create and send encrypted messages. This type of machine learning will become more prominent in the world of AI over the next few years, particularly when it comes to handling private or sensitive information. Two of the researchers involved, Martin Abadi and David Anderson, wrote in their paper that "The learning does not require prescribing a set of cryptographic algorithms, nor indicating ways of applying these algorithms: it is based only on a secrecy specification represented by the training objectives." After several thousand simulations, Alice and Bob were each able to send and decrypt messages securely. Eve on the other hand, was unable to fully decrypt the messages.

Is artificial intelligence tech quickly becoming enterprise tech? Vendors are betting on it. Last week at IBM World of Watson, IBM CEO Ginni Rometty laid out her vision for the technology: Namely, that Watson will reach a billion users by the end of 2017, and that the technology will underpin every major personal and corporate decision. Last month, Salesforce rolled out Salesforce Einstein, an AI system that analyzes data to identify trends in marketing and sales. Having a clear-cut IT strategy is key establishing a competitive advantage over any competition.

If you're keeping up with the rapid changes in the technology industry, you're seeing a bunch of terms thrown around as if they're interchangeable--but really, there are some pretty important distinctions. In this post, we're going to demystify the differences, and clarify the relationships, among these terms, especially artificial intelligence, machine learning, and self-driving cars. Let's begin with a simple model for how we'll approach this topic: Artificial intelligence is the broad field that covers all sorts of different initiatives and efforts to create machines that behave intelligently. What exactly it means to'behave intelligently' is a question best left for the philosophers and cognitive scientists, but for us, it refers to creating machines that do the highly complex things that only humans have previously been able to do. That means that AI is about creating machines that do more than just follow the commands that we give them. They can process input, make decisions, and take action.

Jen-Hsun Huang, CEO of Nvidia, recently told the WSJDLive Conference that he would like his car to not just drive him to work, but to recognize who he is, set up his conference calls, and handle just about all the functions of a personal assistant. In the near future, personal artificial intelligence engines will read your emails, create travel itineraries, and summon your autonomous vehicle--all without you having to ask. This knowledge, combined with real-time traffic and route data, will allow your personal artificial intelligence engine to pre-summon an autonomous vehicle for your journey to ensure that you arrive on time. In particular, with the introduction of personal artificial intelligence (A.I.) engines and on-demand autonomous vehicles, the uncertainty of traveling to and from major international airports will be eliminated, and travelers will experience effortless commutes. Anyone who has ever departed from a major congested airport knows that arriving on time is not always easy.

Donald Trump's presidential victory defied just about everything supposedly smart people knew about politics and winning the White House. He prevailed by tapping a force that was far more powerful than the strongest debate performance, the most attention-grabbing TV spot, the savviest turnout operation or the highest-profile surrogates, from the White House down. He tapped into seething anger and voters' ravenous desire for change. If people get mad enough, they will storm the polls without prodding -- and without, apparently, the need to confide in opinion pollsters, who largely missed the huge outpouring of Americans displaced by decades of economic restructuring and unsettled by the country's changing complexion and shifting cultural mores. If people get mad enough, they will look past a candidate's overt prejudice, his coarse put-downs of women, his mockery of a disabled journalist, his taunting of a Gold Star family.

Speaking at the opening of the Centre for the Future of Intelligence last month, theoretical physicist Stephen Hawking was asked about the implications of Artificial Intelligence (AI) on the human race. Sharing his belief that computers can emulate, and in fact exceed, human intelligence, Professor Hawking commented "the rise of powerful AI will be either the best, or the worst thing, ever to happen to humanity. We do not yet know which." The statement epitomises the prevailing air of uncertainty around the implications of AI – will it be mankind's crowning achievement, or lead to our ultimate demise? Now I can't really argue with a man of Professor Hawking's intellect and experience, but I sit firmly on the side of optimism, and believe we are seeing the advent of a new era in how humans relate with machines.