The Iller river stretches for 91 miles through southeastern Germany before meeting up with the Danube. This river, like all rivers, is filled with sediment-- rocks and pebbles from thousands of years ago that sit on the riverbed and along the bank. One day last year, Benjamin Maus was lounging near the Iller admiring a handful of pebbles. "I was basically just sorting pebbles and spraying them with sunscreen, which made the colors much more vivid," Maus recalls. At the time Maus, an artist, had no clue that these pebbles would inspire his latest work.

We've been using TensorFlow in daily research and engineering since it was released almost six months ago. We've learned a lot of things along the way. Because there are many subjective articles on TensorFlow and not enough helpful documentation, I've sprinkled in examples, tutorials, docs, and code snippets wherever possible. Community engagement is the most important thing. When it comes to machine learning, it is easy to focus on the tech (features, capabilities, benchmarks, etc).

As we move into a new era of ITSM computing, new big data and machine learning tools and methodologies are being developed to support IT staff by intelligently extracting insights and making predictions from the enormous amounts of data accumulated from the organization. According to Gartner, I&O leaders must take a comprehensive approach to incorporate advanced big data and machine learning technologies into their organizations or risk becoming irrelevant. But what exactly is big data and machine learning all about? How can you introduce these concepts into your existing Service Desk? Join USF's distinguished Computer Science and Engineering Professor Lawrence Hall and SunView Software's VP of Marketing and Product Strategy John Prestridge as they break down the fundamentals of big data and machine learning and provide real-world examples of the impact the technologies will have on ITSM. Experience ChangeGear for Service Desk! Track, manage, and control IT service from a single-platform solution.

"In a future with mass unemployment, young people are forced to sell blood." This is the opening line of a short film entered in this year's Sci-Fi London Film Challenge. It's dark, enigmatic, contemporary…and written by a computer. In fact, the film's entire screenplay is the work of a neural net trained on sci-fi scripts. Once the software completed the screenplay--which you can read in all its unadulterated glory here--it was up to the film's director and actors to make it into something someone might actually watch.

Search engines that aren't Google rarely have much that's interesting to offer to the average consumer. But Omnity, a new search engine aimed at researchers -- or even just students doing their homework -- offers some glimmers of something new that make it worth taking notice. Search, as we know it, is ripe for some sort of change, after all. Google is certainly working to bake search more fully into our cars, phones and other devices. Specialized search engines -- for flights, places to stay, even .gifs And then there are those AI bots being promised by Google, Facebook, Microsoft and others.

For instance, let's imagine that you've gone to hospital for some unknown condition and you get an X-ray. Rather than handing the slide to a doctor, who could miss a small shadow or other minor clue, the image would be handed to the computer. It would use deep learning to trawl an anonymized patient database looking for any anomalies that you might be suffering from. The current system will work on bone health, cardiovascular analysis and lung conditions, although who knows where the possibilities will end. As deep learning technology gets more powerful, smaller and significantly cheaper, the potential for AI to assist doctors becomes more realistic.

As more and more data is being collected by the country's healthcare system, researchers are exploring the option of using a subfield of artificial intelligence (AI), namely machine learning to help improve the quality of medical care and patient outcomes. An overview of some of the ways in which machine learning has the potential to contribute to advancements in plastic surgery were published in Plastic and Reconstructive Surgery recently. Dr. Jonathan Kanevsky from the McGill University in Montreal and his colleagues wrote, "Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision-making." A few key areas were highlighted, all of which showed how machine learning and "Big Data" would be able to contribute to progress in the fields of reconstructive and plastic surgery. Machine learning analyzes historical data to develop algorithms that are capable of knowledge acquisition.

Vinod Iyengar is Director of Marketing at California-based developer H2O.ai. H2O.ai are the makers behind H2O, the leading open source machine learning platform for smarter applications and data products. They work across a number of mission critical applications, including predictive maintenance, operational intelligence, security, fraud, auditing, credit scoring, user based insurance, ICU monitoring and more in over 5,000 organizations. And with customers including Capital One, PricewaterhouseCoopers, Comcast, Nielsen Catalina Solutions, Macy's and Aetna – to name just a select few – they are clearly in a prominent position in this space. Vinod details some key use cases of artificial intelligence in specific industries, while also sharing H2O.ai's vision for AI and the challenges we face in adopting it… Across industries and business disciplines, businesses use artificial intelligence to increase revenue or reduce costs by performing tasks more efficiently than humans could do unaided. With more than 150 million active digital wallets than 200 billion in annual payments, PayPal leads the online payments industry.

The following interview is one of many included in the report. Benjamin Recht is an associate professor in the electrical engineering and computer sciences department as well as the statistics department at the University of California at Berkeley. His research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning -- exploring the intersections of convex optimization, mathematical statistics, and randomized algorithms. David Beyer: You're known for thinking about computational issues in machine learning, but you've recently begun to relate it to control theory. Can you talk about some of that work?

Google on Wednesday revealed new products and software that uses machine learning to help users better perform simple tasks, access information and entertainment, and communicate with others. Among the specific items unveiled at the company's 10th annual conference for developers was an updated Android system, a new Watch and a virtual-reality platform. Google plans to start selling a device called Home that will answer users' questions and complete tasks for them. At the start of the conference, Chief Executive Sundar Pichai revealed new products and services that use smarter software to make decisions rather than follow instructions, part of a major push into artificial intelligence that he said would define the tech giant over the next decade. Google, a unit of Alphabet Inc. GOOG, 2.02% GOOGL, 1.95%, said it would soon start selling a device called Home that will answer users' questions and complete tasks for them, like scheduling appointments, playing music and sending emails.