Amazon has launched a real-life shop where people can just pick things up and leave. Amazon Go, as it is calling the shop, uses a range of technologies to watch over people and see what they take from the shop. When they leave – which is done simply by walking back out the door – they'll be charged through their Amazon account for everything that they've picked up. To shop at the store, people just sign in at the door with their Amazon app, by pressing their phone against a sensor. That signs them in – and then everything else is done automatically.

HyperScience delivers machine learning solutions for the enterprise, working with Fortune 500 companies. The HyperScience team is guided by the belief that AI is destined to be the biggest event in the history of human labor since the industrial revolution. HyperScience offers leading global businesses the tools to take advantage of this new technology and create innovative solutions ranging from predictions, automated classifications and anomaly detection in any domain. There are many examples of AI currently applied to everyday life, ranging from self-driving cars to medical software that diagnoses patients. The company already counts a number of businesses in the Fortune 500 as customers and their engagements start at the C-suite, solving these large businesses-- most challenging problems.

Leveraging the power of machine learning in healthcare to improve outcomes has primarily rested in the hands of data scientists--until now. Healthcare.ai--open source predictive analytics software--is on a mission to democratize machine learning--to make it accessible to everyone in healthcare (not just data scientists) with the right technical skillset and tools (e.g., BI developers, SQL developers, data architects, and project managers). Machine learning and artificial intelligence (AI) are transforming healthcare. Health systems are increasingly using predictive analytics to better prioritize at-risk patients and optimize care decisions. Healthcare.ai makes it easy to create predictive models on your healthcare data--and is unlike any other machine learning tool in the industry.

Uber made two big announcements on Monday: It is acquiring Geometric Intelligence, a New York-based startup, and using that 15-person team to help launch a new artificial intelligence division within Uber, called Uber AI Labs. Uber AI Labs will focus on improving both ride-hailing software and the company's self-driving car software, according to a blog post by Uber's product chief Jeff Holden. Geometric Intelligence, a 2-year-old artificial intelligence startup, will move its 15-person staff from New York to Uber's San Francisco headquarters to help build the lab. "In spite of notable wins with machine learning in recent years, we are still very much in the early innings of machine intelligence," Holden wrote in the blog post. "The formation of Uber AI Labs, to be directed by Geometric's Founding CEO Gary Marcus, represents Uber's commitment to advancing the state of the art, driven by our vision that moving people and things in the physical world can be radically faster, safer and accessible to all."

If Uber is going to make its dreams of self-driving ridesharing cars a reality, it's going to need a lot of expertise in artificial intelligence... and it's taking big steps to make that happen. The company has created Uber AI Labs to fuel its research, and it's getting the team started by acquiring AI startup Geometric Intelligence. It's a small 15-person outfit, but the newly purchased company stands out by resisting the urge to train AI by feeding it large data sets. As the New York Times notes, Geometric Intelligence prefers to have systems create their own rules from just a handful of examples -- while Uber ride data will help, the AI won't need a wealth of knowledge to make informed decisions. Autonomous driving will be the star of the show at the new labs, but Uber is promising that its AI work will shape a lot of its day-to-day business. It could improve the accuracy of predicted arrival times for your rides and UberEats deliveries, tackle fraud and improve the chances of UberPool matching your car with other travelers.

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.