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.

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.

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.

Thanks to developments in artificial intelligence (AI) and other emerging technologies, chatbots are changing the way businesses communicate with the world -- including their customers. Plug-and-play machine learning data models that can instantly analyze information and provide intelligent insights? Yep, Microsoft has'em, it says. Chatbots, computer programs that simulate human conversations through AI, in most cases communicate with real people -- but applications are being developed that enable them to communicate with each other. The use of chatbots is on the rise, and they are becoming more sophisticated, said Jen Looper, a web and mobile developer and founder of Ladeez First Media, a developer of mobile apps specializing in education and fitness.

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.

Anthony Ledford and his colleagues at Man AHL spent three painstaking years building a machine-learning model to do something mere mortals often can't: find fresh ideas in an avalanche of data. But even Ledford, chief scientist at the $19 billion Man AHL in London, rolls his eyes when he hears people say that machine learning, a type of artificial intelligence, is going to transform hedge funds tomorrow. To Ledford, a lot of the buzz smacks of hype. The technology is more robust than its predecessors but hardly revolutionary. "There is some real science here, but it's not the way it's been portrayed," said Ledford, who holds a Ph.D. in mathematics.

Deep learning delivers extraordinary cognitive powers in the never-ending battle to distill sense from data at ever larger scales. But high performance doesn't come cheap. Deep learning relies on the application of multilevel neural-network algorithms to high-dimensional data objects. As such, it requires that fast-matrix manipulations in highly parallel architectures in order to identify complex, elusive patterns--such as objects, faces, voices, threats, etc.–amid big data's "3 V" noise. As evidence for the technology's increasingly superhuman cognitive abilities, check out research projects such as this that use it to put the Turing test to shame.

IBM has announced the experimental release of Project Intu, a new, system-agnostic platform designed to enable embodied cognition. The new platform allows developers to embed Watson functions into various end-user device form factors, offering a next generation architecture for building cognitive-enabled experiences. Project Intu, in its experimental form, is now accessible via the Watson Developer Cloud and also available on Intu Gateway and GitHub. Project Intu simplifies the process for developers wanting to create cognitive experiences in various form factors such as spaces, avatars, robots or other IoT devices, and it extends cognitive technology into the physical world. The platform enables devices to interact more naturally with users, triggering different emotions and behaviors and creating more meaningful and immersive experience for users.