On Thursday, Microsoft took the unusual step of combining its Bing and Cortana product teams with Microsoft Research, in a bid to accelerate innovation for both the search engine and the digital assistant. The move was part of a broader reorganization that saw Microsoft split its Applications and Services Group, which also included Microsoft's Office applications, into two separate organizations. Office applications will form their own group. Though Microsoft has asked researchers to work on projects that could eventually be commercialized, combining teams that work on active products, such as Cortana and Bing, with the future-facing teams that comprise Microsoft Research, is in fact unique within Microsoft. Together, Bing and Cortana, plus Microsoft's Information Platform and Ambient Computing and Robotics teams, will form the Microsoft AI and Research Group. All told, the group will include more than 5,000 computer scientists and engineers, Microsoft said.

Computers can already recognize you in an image, but can they see a video or real-world objects and tell exactly what's going on? Researchers are trying to make computer video recognition a reality, and they are using some image recognition techniques to make that happen. Researchers in and outside of Google are making progress in video recognition, but there are also challenges to overcome, Rajat Monga, engineering director of TensorFlow for Google's Brain team, said during a question-and-answer session on Quora this week. The benefits of video recognition are enormous. For example, a computer will be able to identify a person's activities, an event, or a location.

Microsoft has reorganized several disparate projects and programs into an entirely new Artificial Intelligence group. It will be the fourth major division after Windows, Office and cloud, reallocating over 5,000 computer scientists and engineers under its umbrella. The shift shows how much unified effort the tech giant believes the field needs, as well as internally standardized AI tech they can more easily integrate into customer products. Microsoft Research chief Harry Shum will head the new AI division, according to GeekWire. In addition to his old department, the new group will include products like Cortana and Bing with the Ambient Computing and Robotics teams, as well as the company's Information Platform Group. Collecting them all under one banner suggests how standardized they want their AI tech to be between disparate programs.

If you spend your work days toiling in Google's productivity apps, the first thing you might notice today is that Google for Work is now called "G Suite". Once you get past the new label, you might also notice a slew of smart updates across the board that ought to save you time and keep your workflow moving. First up: Docs, Sheets and Slides got a new "Explore" feature that uses natural language search to help you research reports, organize data or design better looking presentations. In each of the main apps, an Explore button brings up a new sidebar with contextual options based on the app you're using. In Docs, this means Explore will search and suggest images, web links or other Drive documents that appear relevant to the content you're writing.

There may now be an easier way to implement advanced machine learning models in your projects. Google has opened up its Cloud Machine Learning to all businesses in a public beta, after a few months of testing it in private alpha. The tool makes it easier to train models at a much faster rate, and is integrated with the Google Cloud Platform. This has applications for businesses in areas such as customer support (learning how to automate responses to a variety of queries and complaints) or any kind of repetition-heavy task. In a blog post, Google described how its customer Airbus Defense and Space used the tool to automate the detection and correction of satellite images that contain imperfections such as cloud formations.

Americans want to stay in control of their cars, a new study finds. According to a study by Kelley Blue Book, 80 percent of Americans say people should always have the option to drive themselves. This study comes just a week after the Department of Transportation released regulatory guidelines for self-driving vehicles. And it comes comes as car companies are spending billions to advance the technology. But despite the push toward autonomous cars, consumers remain unconvinced.

You're so hard to explore. Sometimes you bombard spacecrafts with hurtling rocks and deadly cosmic rays, and other times you're so empty you don't give astronauts a darn thing to hold on to. But while scientists haven't quite figured out how to keep radiation at bay, the scientists at NASA's Jet Propulsion Laboratory--specifically, its Planetary Robotics Laboratory--are building machines that can get a grip on the most difficult surfaces astronauts will find out there. Adhesion-wise, space presents a couple problems. First, robots typically struggle with uneven surfaces, let alone the kind of cliffs and crags you see on Mars.

Tl;dr - has this idea about neural network architecture, similar to autoencoders, been developed already? Correct me if I'm wrong about anything, but this is my understanding so far: Autoencoders purposefully have an information bottleneck in the middle, and this bottleneck is what forces the network to learn high level representations of the input data. Otherwise, without the bottleneck, the network may discover that the optimal connection is one that is roughly equivalent to directly mapping each input to its corresponding output. However, because that bottleneck exists, it is very unlikely that an autoencoder could perform a perfect reconstruction of the input. Contrast this with other encoding, like DCT as used by JPEG images.

The application of machine learning to predictive analytics continues apace as a way to improve IT operations, data security and business intelligence. Among those offering frequent platform upgrades is real-time "operational intelligence" specialist Splunk Inc., which this week rolled out the latest versions of its IT, security and analytics packages that seek to "operationalize" machine data. San Francisco-based Splunk (NASDAQ: SPLK) said machine learning is integrated as a core capability in its latest package of IT, security and analytics offerings in the form of packaged or custom algorithms intended to leverage growing volumes of machine data. Use cases for its enterprise, IT services, security and user behavior analytics products include: "focused investigation" of IT and security incidents to detect data patterns and anomalies; reducing "alert fatigue" by identifying normal patterns for specific use cases; proactive maintenance; demand forecasting, managing inventory; and adjusting to changing business conditions by analyzing historical data. "The enterprise machine data fabric is the foundation for managing and deriving insights from that data at scale," Splunk President and CEO Doug Merritt asserted in a statement.

This week at Microsoft Ignite, we showed how we are infusing artificial intelligence (AI) broadly across Microsoft to help our customers. Our approach to this fundamental shift is to democratize AI and to make it accessible and valuable to everyone. We're focused on building an AI stack spanning infrastructure, services, apps and agents and reaching key customer audiences -- consumers, enterprises, developers. We are creating tools to make it easier for busy professionals to remember their commitments, family members from other countries to talk to each other in spite of language barriers, and multitasking smartphone users to send texts more quickly. And at the same time, we also are providing businesses with the tools they need to incorporate intelligence into every product they build and business decision they make.