Singer John Legend is the newest voice of the Google Assistant, the tech company announced Wednesday. Google Assistant users, get ready to have the silky voice of John Legend in your home. The tech company on Wednesday announced the 40-year-old singer as the voice of its virtual assistant for a limited time in the U.S. Legend's voice will be able to give users the weather forecast, sing happy birthday and tell jokes. His voice will also tell you a few things about the singer's personal life, including about his wife, Chrissy Teigen. There will also be a few Easter eggs.

I get that there's too much TV, I really do. Even so, I can't understand why no one's been talking about Black Monday, the Showtime comedy about Wall Street in the '80s that stars Don Cheadle and Regina Hall. On some level I want to punish those of you who haven't given the show a chance, but instead I'm going to politely ask that, seeing as the first season ended Sunday, you watch the whole thing: Now that it's waiting for you in one bingeable chunk, you really have no excuse. Its co-creator David Caspe (of the fan favorite Happy Endings) suggested as much in an interview with the Hollywood Reporter that doubled as a pitch for viewers to consider a bender: "A lot of times if you don't break out right away in a crazy way, it's between seasons one and two that people find stuff. The show is obviously written as incredibly serialized, and I think in some ways getting to watch a few in a row might be helpful for a viewer."

Walmart shoppers can now do some online shopping using just their voice. The retail giant is partnering with Google to launch grocery shopping on any devices equipped with Google Assistant. Walmart Voice Order is rolling out to consumers beginning this month, the company said today. Walmart shoppers can now shop using just their voice. It'll become available for users with Google Home devices, as well as Assistant-equipped smart displays, Android phones and iPhones.

STEM has a bit of an image problem: Despite efforts to make it colorful and friendly, it's still intimidating to a lot of students. When there are parents shoving electronics kits at them while offering no help and teachers insisting that learning to code is fundamental to their future career prospects, some kids end up completely turned off. But now Lego Education has a new $330 kit, Spike Prime, aimed at building coding literacy and overcoming the confidence problem that drives many kids away from STEM before they reach high school. Instead of pointing students toward more complex projects, Spike Prime is about basic knowledge and practicality. As Esben Stærk Jørgensen, the president of Lego Education, said during a press event in New York today, Spike Prime is not about learning to code so much as it is coding to learn.

An artificial intelligence has gone to the top of its class after passing an English exam. Though it can't beat more able human students, it achieved the best mark yet for a machine. Hai Zhao at Shanghai Jiao Tong University in China and his colleagues trained their AI on more than 25,000 English reading comprehension tests. Each contained a 200 to 300-word story followed by a series of related multiple-choice questions.

For internet-goers, Boston Dynamics is that company that uploads insane videos of the humanoid Atlas robot doing backflips, of four-legged SpotMini opening doors and fighting off stick-wielding men, and as of last week, of a Segway-on-mescaline called Handle jetting around picking up and stacking boxes with a vacuum arm. For journalists and industry watchers, however, Boston Dynamics is that company that almost never talks about where all of this work is ultimately headed. The company is now teasing its ambitions as the four-legged SpotMini nears its commercial release. Today, Boston Dynamics is getting even more explicit about its vision with an announcement that it's acquired a Silicon Valley startup called Kinema Systems, which builds vision software that helps industrial robot arms manipulate boxes. This acquisition is giving the Handle robot the gray matter it needs to follow SpotMini to market.

The Animal-AI Olympics, which will begin this June, aims to "benchmark the current level of various AIs against different animal species using a range of established animal cognition tasks." At stake are bragging rights and US $10,000 in prizes. The project, a partnership between the University of Cambridge's Leverhulme Centre for the Future of Intelligence and GoodAI, a research institution based in Prague, is a new way to evaluate the progress of AI systems toward what researchers call artificial general intelligence. Such an assessment is necessary, the organizers say, because recent benchmarks are somewhat deceiving. While AI systems have bested human grandmasters in a host of challenging competitions, including the board game Go and the video game StarCraft, these matchups only proved that the AIs were astoundingly good at those particular games.

In 2014, IBM opened swanky new headquarters for its artificial intelligence division, known as IBM Watson. Inside the glassy tower in lower Manhattan, IBMers can bring prospective clients and visiting journalists into the "immersion room," which resembles a miniature planetarium. There, in the darkened space, visitors sit on swiveling stools while fancy graphics flash around the curved screens covering the walls. It's the closest you can get, IBMers sometimes say, to being inside Watson's electronic brain. One dazzling 2014 demonstration of Watson's brainpower showed off its potential to transform medicine using AI--a goal that IBM CEO Virginia Rometty often calls the company's moon shot. In the demo, Watson took a bizarre collection of patient symptoms and came up with a list of possible diagnoses, each annotated with Watson's confidence level and links to supporting medical literature. Within the comfortable confines of the dome, Watson never failed to impress: Its memory banks held knowledge of every rare disease, and its processors weren't susceptible to the kind of cognitive bias that can throw off doctors. It could crack a tough case in mere seconds. If Watson could bring that instant expertise to hospitals and clinics all around the world, it seemed possible that the AI could reduce diagnosis errors, optimize treatments, and even alleviate doctor shortages--not by replacing doctors but by helping them do their jobs faster and better.

If you haven't seen the latest Boston Dynamics video, released last week, it shows an upgraded version of the company's Handle robot moving boxes in a warehouse. Handle is a mobile manipulator that integrates both legs and wheels, and the new version features a swinging "tail" that serves as a counterweight and allows the robot to balance and move in a dynamic fashion--just as you'd expect from the company that created such nimble machines as Atlas, Spot, and BigDog. Boston Dynamics, which SoftBank bought from Google in 2017, is showing off Handle toiling in a warehouse for a reason: The company is officially entering the logistics market, with plans to offer robots for material-handling applications. As part of that strategy, it is announcing today the acquisition of Kinema Systems, a startup based in Menlo Park, Calif., that develops vision sensors and deep-learning software to enable industrial robot arms to locate and move boxes. Boston Dynamics founder and CEO Marc Raibert says the two Handle robots seen in the video aren't moving as fast as they could, and one of the factors limiting their performance is their vision systems.

Ever heard of the "Britney Spears problem"? Contrary to what it sounds like, it's got nothing to do with the dalliances of the rich and famous. Rather, it's a computing puzzle related to data tracking: Precisely tailoring a data-rich service, like a search engine or fiber internet connection, to individual users hypothetically requires tracking every packet sent to and from the service provider, which needless to say isn't practical. To get around this, most companies leverage algorithms that make guesses about the frequency of data exchanged by hashing it (i.e., divvying it up into pieces). But this necessarily sacrifices nuance -- telling patterns that emerge naturally in large data volumes fly under the radar.