Alphabet Inc.'s artificial intelligence division Google DeepMind is making the maze-like game platform it uses for many of its experiments available to other researchers and the general public. DeepMind is putting the entire source code for its training environment -- which it previously called Labyrinth and has now renamed as DeepMind Lab -- on the open-source depository GitHub, the company said Monday. Anyone will be able to download the code and customize it to help train their own artificial intelligence systems. They will also be able to create new game levels for DeepMind Lab and upload these to GitHub. The decision to make this AI test bed available to the public is further evidence of DeepMind's decision to embrace more openness around its research.

For Katja Hofmann, Minecraft is not just a virtual world: it is a gym for artificial intelligences. Hofmann, 36, is the lead researcher on Microsoft Research Lab's Project Malmo, an open-source platform that makes it possible to test AIs inside the game's pixelated universe. "A question in artificial intelligence is how we get AIs to learn how to interact in a complex environment, to experiment in a wide range of settings," she says. Researchers using Project Malmo, which was made available to developers in July 2016 after a year of in-house testing at Microsoft's Cambridge-based lab, can create AI agents and set them loose in a modified version of Minecraft's free-to-roam 3D environment. There, through trial and error, the agents learn how to move, walk and dodge obstacles in a physically consistent world - something usually requiring expensive robots.

It looks like those rumors of Amazon convenience stores were true. The online shopping giant unveiled Amazon Go today, its spin on brick and mortar retail. It uses computer vision, a whole bunch of sensors and deep learning to let you walk into a store, sign in with an Amazon Go app, fill up your bags and leave without stopping for a checkout line. Amazon is calling it a "Just Walk Out Shopping" experience, a self-descriptive name if there ever was one. The company is starting out with a large store in Seattle, but it's clearly meant to serve as a model for other locations and retail stores.

With dozens of powerful text mining features, including access to free and premium Gnip Twitter data, DiscoverText provides software tools to quickly and accurately evaluate text data. Data scientists know that cleaning data can be very time consuming. Users of DiscoverText build custom machine classifiers or "sifters" to find the most relevant items. DiscoverText shortens a process that used to last weeks or months; our machine-learning sifters are created in hours or just a few minutes. We support technical integrations with Twitter and SurveyMonkey.

More broadly, we hope that MusicNet can be a resource for more creative tasks. Automatic music transcription, inferring a musical score from a recording, is a long-standing open problem in the music information retrieval community. Music streaming services traditionally make recommendations based on collaborative filtering and metadata (e.g. Recently, some services have begun to incorporate audio features into their recommendation engines. Features learned from the MusicNet labels might be useful for recommendation.

When Vic Gundotra left Google in 2014, he thought he might retire, forever. But a lingering interest in wearable technology and machine learning led him to AliveCor, which lets users monitor their heart health from their smartphones. Diving back into the fray of tech, Gundotra is now convinced that the potential of wearables and machine learning is just starting to be unlocked. AliveCor's portable EKG sensor, Kardia, alerts users if their heartbeats are irregular -- and now, the Mayo Clinic, an AliveCor investor, has begun identifying other signals in an EKG reading that a human might miss. "No human doctor can look at your EKG and tell you with a high degree of accuracy what your potassium level is," Gundotra said.

Ride-hailing requires a lot of machine smarts to maintain a competitive edge, so it's not surprising to see Uber make a strategic acquisition in the artificial intelligence space. The company has acquired Geometric Intelligence, a startup co-founded by academic researchers with AI experience, and its team will provide the core for a new central AI lab being established at Uber's SF HQ. Uber's doing a lot with machine learning already through its research team in Pittsburgh, but they're focused specifically on solving issues related to autonomous driving. This new core team will be looking at applications for AI more broadly, with a focus on basic research that's likely to have impact across a range of potential applications, including things like route management. It's also yet another sign that Uber wants to be passed among tech bigs like Google, Apple and Microsoft whose interest range beyond a single domain.

Think Python: How to Think Like a Computer Scientist Allen Downey, 2012 Automate the Boring Stuff with Python: Practical Programming for Total Beginners [Buy on Amazon] Al Sweigart, 2015 Learn Python the Hard Way [Buy on Amazon] Zed A. Shaw, 2013

For the time being, countless decisions still require human engagement. Awash in data, executives dream of a time when the Jetson utopia finally manifests -- and they find themselves sipping coffee and cashing checks while machines slave away for them, uncovering unexpected business insights and learning optimal ways to manage organizations. Despite improvements in cognitive technologies, that dream managerial scenario is still far from reality. Decisions that executives face don't necessarily fit into defined problems well suited for automation. At least for the time being, countless decisions still require human engagement. To oversimplify, machine learning emphasizes algorithms that use numerous examples as inputs.