Cortana has a new boss, and he has a problem: If Google's turning its Google Now digital assistant into a chatbot, should Microsoft do the same with Cortana? In 2014, Microsoft first unveiled Cortana, the digital assistant within Windows 10. Interacting with Cortana was simple: You asked a question, she responded. In the last few months, however, Google's Assistant has offered an alternative: a chatbot that interacts with users via an ongoing stream of text and images. In an interview with PCWorld, Shum said he hasn't ruled out adding a Cortana chatbot to its roster of digital assistants.

If you add them all up, Google actually makes a lot of gadgets. It sells Nexus phones, Chromebook Pixels, Pixel C tablets, Nest smart-home products, cardboard VR viewers, Chromecast and Chromecast Audio, pretty Wi-Fi routers, even weird custom phone cases. But you'd never think of Google as a hardware company, because there's no rhyme or reason to any of it. Some things are made for developers, others to show off how great a form factor or operating system could be. Some become really expensive doorstops. I'm sure I'm forgetting stuff, too--Google's made so many small-time moves in the gadget market that it's hard to track them all.

In the early 1990s, Iris Murdoch was writing a new novel, as she'd done 25 times before in her life. But this time something was terribly off. Her protagonist, Jackson, an English manservant who has a mysterious effect on a circle of friends, once meticulously realized in her head, had become a stranger to her. As Murdoch later told Joanna Coles, a Guardian journalist who visited her in her house in North Oxford in 1996, a year after the publication of the book, Jackson's Dilemma, she was suffering from a bad writer's block. It began with Jackson and now the shadows had suffused her life. "At the moment I'm just falling, falling … just falling as it were," Murdoch told Coles.

At the Hotel Parco dei Principi in Rome, in September of 1973, the Hungarian mathematician Paul Erd?s approached his friend Richard Guy with a request. He said, "Guy, veel you have a coffee?" It cost a dollar, a small fortune to a professor of mathematics at the hinterland University of Calgary who was not much of a coffee drinker. Yet, as Guy later recalled--during a memorial talk following Erd?s's death at age 83 two decades ago--he was curious why the great man had sought him out. Guy and Erd?s were in the Eternal City for an international colloquium on combinatorial theory, so Erd?s--who sustained himself with espresso and other stimulants, worked on math problems 19 hours a day, and in his lifetime published in excess of 1,500 papers with more than 500 collaborators--most likely had another problem on the go.

When robots fight, the least safe place to be is inside of one. When it comes to military tech, one major advantage of robots is the distance they put between their pilots and battle. When it comes to giant robot duels for sport, instead, the whole point is to make the machine a fancy, battle-ready exoskeleton for the human inside. To do that, the human needs to survive the fight. So MegaBots, the American team that built a giant human-carrying fighting robot and immediately challenged a Japanese robot to a fight, are working on making their machine safe for the human inside when the duel finally happens.

Che Voigt believes his company has solved problems that have plagued the working world since the advent of typing. It's a solution to hunched backs, stiff necks and tight shoulders. It's a workstation that, with a push of a button, transitions from a standing desk to a seated table to a fully reclined platform like a dentist's chair. Its seat expands and retracts, supporting the whole body from head to heels. There's a screen and mouse and keyboard that follows the user's eyes and hands.

Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data. Deep Learning/Machine Learning (Applications): Companies that utilize computer algorithms that operate based on existing data in vertically specific use cases. Examples include using machine learning technology to detect banking fraud or to identify the top retail leads. Natural Language Processing (General): Companies that build algorithms that process human language input and convert it into understandable representations.

I like to watch long-term technology and business trends and watch as they shape the products and services that I get to use and to write about. As I was preparing to write today's post, three such trends came to mind: As the industry pushes forward in accord with these trends, a couple of interesting challenges have surfaced over the past decade or so. Again, here's a quick list (yes, I do think in bullet points): The GPU (Graphics Processing Unit) was born of these trends, and addresses many of the challenges! Processors have reached the upper bound on clock rates, but Moore's Law gives designers more and more transistors to work with. Those transistors can be used to add more cache and more memory to a traditional architecture, but the von Neumann Bottleneck limits the value of doing so. On the other hand, we now have large markets for specialized hardware (gaming comes to mind as one of the early drivers for GPU consumption).

The Apache Software Foundation is now incubating a project backed by Cloudera Inc. and Intel Corp. that aims to bolster cyber security with Big Data analytics and machine learning. Previously known as the Open Network Insights (ONI) initiative, the project is now called Apache Spot, since it uses machine learning to sniff out bad traffic amongst good data. It can also study network traffic to characterize its unique behavior using the open source distributed storage and processing software Hadoop, which helps it to discover if any anomalies are present. Intel launched the project back in February on Cloudera's cloud computing platform. Apache Spot works by storing large amounts of information within Hadoop, then using Apache Spark to process data from deep packet inspection of domain name system (DNS) traffic, connections and log files from proxies.

Posted by Riku Inoue, Product Manager, Google Cloud Platform Earlier this year at GCP NEXT, we introduced new Cloud Machine Learning products with the intention to change the way businesses operate and create new customer experiences, while deepening the insights derived from data.Today, we want to share how Google aims to help more businesses benefit from the advancements in machine learning, while making it easier for them to use it. Google Cloud Machine Learning is now publicly available in beta and can empower all businesses to easily train quality machine learning models at a faster rate. With its powerful distributed training capability, you can train models on terabytes of data within hours, instead of waiting for days. Integrated with Google Cloud Platform (GCP), Cloud Machine Learning is a fully-managed service that can scale and creates a rich environment across TensorFlow and cloud computing tools such as Google Cloud Dataflow, BigQuery, Cloud Storage and Cloud Datalab. We're also introducing a new feature, HyperTune, that automatically improves predictive accuracy. It allows data scientists to build better performing models faster by automatically tuning hyperparameters, instead of manually discovering values that work for their model.