The 2017 Voice Report is a look ahead at what to expect in the coming chat wars between companies like Microsoft, Samsung, Google, and Amazon. It's also a survey of Google Assistant and Alexa users, and a look into why some startups make more successful Alexa skills than others. The report was put together by VoiceLabs, a voice analytics startup tapped by Google for the launch of its Actions on Google, a platform for the creation of Google Assistant actions. VoiceLabs works with developers making Google Assistant actions and Alexa skills. The company was created last year by Alexandre Linares and Adam Marchick shortly after Marchick stepped down as CEO of mobile marketing company Kahuna, which he cofounded in 2012.

Robert O. Work, the veteran defense official retained as deputy secretary by President Trump, calls them his "A.I. dudes." The breezy moniker belies their serious task: The dudes have been a kitchen cabinet of sorts, and have advised Mr. Work as he has sought to reshape warfare by bringing artificial intelligence to the battlefield. Last spring, he asked, "O.K., you guys are the smartest guys in A.I., right?" No, the dudes told him, "the smartest guys are at Facebook and Google," Mr. Work recalled in an interview. The United States no longer has a strategic monopoly on the technology, which is widely seen as the key factor in the next generation of warfare.

This seems to be the very first book on this ML framework (H2O). And is is just great. The book is crystal clear and extremely comprehensive, very easy to read, with examples you can reproduce easily (datasets are on line in a public Git repo). It covers a very practical ground on the 4 main algorithms implemented in H2O cluster: RandomForest, GBM, GLM, and last but not least: deep learning... "Practical" means explanations are strongly grounded on a set of 4 datasets, the author plays with, explaining both their preparation, analysis with H2O (code is both in R and PYTHON), and a great deal of time is spent on very useful considerations on how to'tune' the various algorithms to obtain better models, comparing their effectiveness. A must have for everyone interested in implementing ML features concretely.

The days of a depending on a human doctor may soon be numbered, as the future of the health industry looks increasingly like an AI-assisted scenario. Researchers and startups are developing artificially intelligent systems that are capable of diagnosing disease using a patient's breath and even from the emotional inflection of their voice. Someday, your smartphone may help you and your doctor determine whether a strange-looking lesion on your skin is cancerous or not, thanks to a team of Stanford University scientists that have developed a deep learning algorithm tailored just for the task. Led by Sebastian Thrun, an adjunct professor at the Stanford Artificial Intelligence Laboratory, the team found that their diagnostic tool, which builds upon the same classification technique used by Google to differentiate between images of cats and dogs, performed as well or better than 21 board-certified dermatologists. Their findings were detailed in a recent paper published in Nature.

Anticipatory Design is possibly the next big leap within the field of Experience Design. "Design that is one step ahead" as Shapiro refers to it. This sounds amazing, but where does it lead us? And how will it affect our relationship with technology? I've dedicated my Master thesis to this topic to identify both ethical as design challenges that come with the development of predictive UX and application of Anticipatory Design as design pattern.

Industries around the world have caught AI fever. Developers around the world have made more ways than ever for the technology to automate, optimize and enable different services. Facebook, Alphabet, IBM, Microsoft, Amazon and other major companies are all working on AI projects, along with numerous tech startups. One such upstart, which leverages artificial intelligence and image recognition in part to enable commerce, is Clarifai. Founded in 2013, Clarifai utilizes neural networks and provides customers with an image and video recognition API.

Every Artificial Intelligence researcher has probably a vision of how intelligent machines will reshape the world. My idealistic view is one in which automation maximizes people's freedom by giving us more time to concentrate on the things we enjoy the most. Research in all areas is astonishing: we live better and longer, and our planet has started to heal. Robots lead to superlative boosts in productivity, and an unprecedented abundance reduces tensions amongst people. We have more time to learn and inform us, and machines enable us to manage much more knowledge, so we take better decisions.

On the Stanford University campus, you could practically throw a rock and hit 100 graduate students who are building apps that enable people to communicate more effectively. But Terry Winograd is particularly enthusiastic about the app one of his graduate students, Catalin Voss, is working on. Voss, a native of Germany who completed his bachelor's and master's degrees last June at the age of 21, is working on an app that deploys Google Glass, linked to a smartphone, to help autistic children recognize human emotions through facial expressions. Venture capitalists weren't interested, even though Voss had created and sold a startup that used eye-tracking technology to monitor attentiveness to a Toyota subsidiary while still a freshman. But Terry Winograd was interested. "It runs, it has AI [artificial intelligence]," says Winograd, who 20-odd years ago advised another graduate student on the then nascent field of searching the World Wide Web. "It's at a stage where we've actually put 30 devices into homes. Our goal is to have 100 in the trial." Voss says his objective is to build a medical product that insurers will be willing to pay for. "We want to prove the investors wrong, who didn't believe in it, and build an aid for people with autism, and other mental disorders as well," he says. "We believe we've built a fairly holistic system for mental health." Winograd was Voss's first choice for an advisor even though the 70-year-old professor retired from teaching three years ago.

We are on the verge of a very fast rate of technological change. If you think the Internet and mobile communications have changed the world, just wait. Coming years will be even more disruptive and amazing. When most people ponder technology they default to thoughts about compute power and its limitations. In 1965 Gordon Moore, the cofounder of Intel theorized that processing power should be capable of doubling every 18-24 months.

Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.