In the wake of the massive DDoS attack that brought down huge swaths of the internet last week, strengthening cybersecurity is on everyone's minds. It's tough to devise systems that are truly secure, because as security evolves, so do the people trying to hack those systems. But a new experiment from Google showed a possible way forward using artificial intelligence. Google created an encryption game using three distinct entities--Alice, Eve, and Bob--created by deep learning neural networks. Alice sent thousands of 16 character strings of zeros and ones to Bob, encrypting them with a shared key. Eve sat in the middle, trying to decrypt the messages.

Fueled by the exponential growth in mobile messaging, chatbots -- interactive messaging bots that harness recent advances in artificial intelligence and machine learning -- are the hottest new technology going right now. Facebook opened up its Messenger platform to bot developers earlier this year; messaging app Telegram is offering developers up to $1 million in prizes to develop bots that are fast, useful, and work in inline mode; and over 20 million people chat with the Xiaoice bot on the Chinese micro-blogging service Weibo. Even the White House has gotten into the act with its Obama Facebook chatbot. Chatbot technology is still in its infancy, but it's quickly being embraced by businesses because of its vast potential for sales, marketing, and customer service. Chatbots stand to help organizations build deeper relationships with their customers and improve service quality, while at the same time save money by automating certain administrative tasks.

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JAPANESE carmaker Honda Motor Co. has chosen to headquarter its artificial intelligence (AI) research hub in Tokyo, saying its own home ground will enable closer interactions between scientists and researchers compared to Silicon Valley. According to Bloomberg, the research and development (R&D) center will launch in 2017 and will consolidate all the company's current AI teams from Silicon Valley, Europe, and Japan in Tokyo. Yoshiyuki Matsumoto, president of Honda's research arm, said in an interview that the carmakers chose Tokyo due to saturation in the San Francisco Bay Area, which is home to thousands of tech companies and startups. Honda chooses Tokyo over Silicon Valley for AI research centre – THE BUSINESS TIMES https://t.co/r25wCfNHKy Matsumoto was quoted saying: "We won't make much difference if we did the same things as everyone else in Silicon Valley. And not everyone has succeeded there."

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

Early A.I. product offerings are making it more efficient to plan meetings and provide customer service. Artificial intelligence (A.I.) has become one of the hottest topics in business recently, with developers building the technology into everything from cars to refrigerators. Some have expressed skepticism about AI's potential, but early product offerings have revealed technology that appears to actually enhance the work professionals do rather than replace it. Jamie Domenici, Salesforce's VP of Small Business, is excited about the way A.I. is affecting sales and marketing. I sat down with her at this year's Dreamforce in San Francisco, where attendees got a close up look at the company's new A.I. tool Einstein, which the company is embedding into its products and services.

Until recently, [Europe's] contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO. London is Europe's startup centre, mixing capital, proximity to markets, and world-class research hubs.

Who here remembers taking computer programming in school? Whether you learned programming by punching holes in a never ending series of cards, or by writing simple DOS or other computer language commands, the fact remained that computers needed an incredibly precise set of instructions to accomplish a task. The more complicated the task, the more complicated your instructions had to be. Machine learning is inherently different. Rather than telling a computer exactly how to solve a problem, the programmer instead tells it how to go about learning to solve the problem for itself.

It's not about what it can do, but the effects of its prioritization As buzzwords become ubiquitous they become easier to tune out. We've finely honed this defense mechanism, for good purpose. It's better to focus on what's in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn't help you. VR could eat all media, but it's hardware requirements keep it many years away from common use.