Forget the CPU, GPU, and FPGA, Google says its Tensor Processing Unit, or TPU, advances machine learning capability by a factor of three generations. "TPUs deliver an order of magnitude higher performance per watt than all commercially available GPUs and FPGA," said Google CEO Sundar Pichai during the company's I/O developer conference on Wednesday. TPUs have been a closely guarded secret of Google, but Pichai said the chips powered the AlphaGo computer that beat Lee Sedol, the world champion in the incredibly complicated game called Go. Pichai didn't go into details of the Tensor Processing Unit but the company did disclose a little more information in a blog posted on the same day as Pichai's revelation. "We've been running TPUs inside our data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning. This is roughly equivalent to fast-forwarding technology about seven years into the future (three generations of Moore's Law)," the blog said.

Back in 1950, computer scientist, codebreaker, and war hero Alan Turing introduced the world to a very simple premise: If a robot can engage in a text-based conversation with a person and fool that person into believing it is human at least 30 percent of the time, surely we could agree that the robot is a "thinking" machine. Turing's goal was to force people to think more creatively about computer interaction, but he inadvertently ended up creating the test that robot intelligence developers and commentators have relied on for years. They're focused on more substantive metrics. Fundamentally, the problem with the Turing Test is that it's poorly defined therefore facilitates hype (i.e. that fake teaching assistant in Georgia) rather than offering easily duplicated results. Beyond that, one can argue that it measures human weakness, not artificial strength.

One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r 0.90 with R2 0.80 and MAE 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r 0.91 with R2 0.82 and MAE 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes.

Artificial intelligence can control self-driving cars, beat the best humans at incredibly complex board games, and fight cancer; but one thing it can't do perfectly is communicate. To help solve the problem, Google has been feeding it's AI with more than 11,000 unpublished books, including 3,000 steamy romance titles. "come with me," she said. "talk to me," she said. "don't worry about it," she said.

Didi currently processes over 11 million trips, plans over 9 billion routes, and collects over 50TB of data per day. Machine learning strategies are vital to the company's success, and with growth comes the need to constantly improve on core algorithms, especially those that impact supply-demand forecasting. The competition is a challenge to machine learning and big data students around the world to improve how the company ensures riders always get a car when and where they need it, and drivers know where to be even before a ride is hailed. Didi has just published the competition data set, and registration closes on June 17 when the first round submission is due. The Top 10 teams will be invited to Didi in July to compete for the top prize.

Today at I/O, Google took the wraps off its latest foray into the world of communications: the company announced Allo, a smart messaging app supercharged with machine learning and Google's new Google Assistant service (its answer to Amazon's Alexa), giving users the ability not just to chat to each other with animated graphics and enlarging/shrinking text, but to call in Google (and later other third-party apps) to share media, plan events, buy things, and even think of what to say to each other. The iOS and Android app is being unveiled today, but it will only be live this summer, Google says. If you are a Google news watcher, Allo may not come as a complete surprise: back in December the WSJ reported that the company was working on an AI-based messaging app: this appears to be that very product. The app comes at an interesting time for Google. The company has made a number of attempts at building social products over the years, but products like Google, Wave and Buzz never really caught on at a time when other products like Facebook, Twitter and Snapchat have taken off.

Computers are pretty good at stocking shelves and operating cars, but are not so good at writing poetry. Scientists in a Dartmouth College competition reached that conclusion after designing artificial intelligence algorithms that could produce sonnets. Judges compared the results with poems written by humans to see if they could tell the difference. In every instance, the judges were able to find the sonnet produced by a computer program. The competition was a variation of the "Turing Test," named for British computer scientist Alan Turing, who in 1950 proposed an experiment to determine if a computer could have humanlike intelligence.

Ashok Goel had run into a problem. As a computer science professor at Georgia Tech, he taught an online course on artificial intelligence, and its 300 students sent in thousands of questions via an online forum each semester. The sheer volume of messages overwhelmed Goel and his eight teaching assistants. So he tried an experiment--quietly inserting some AI into the class itself. This January, with the help of several graduate students and support from IBM's breakthrough Watson technology, Goel built an AI chatbot that could field basic questions and relieve some of the burden on the class's human instructors.

Computers are pretty good at stocking shelves and operating cars, but are not so good at writing poetry. Scientists in a Dartmouth College competition reached that conclusion after designing artificial intelligence algorithms that could produce sonnets. Judges compared the results with poems written by humans to see if they could tell the difference. In every instance, the judges were able to find the sonnet produced by a computer program. The competition was a variation of the "Turing Test," named for British computer scientist Alan Turing, who in 1950 proposed an experiment to determine if a computer could have humanlike intelligence.

Life would be pretty dull without imagination. In fact, maybe the biggest problem for computers is that they don't have any. That's the belief motivating the founders of Vicarious, an enigmatic AI company backed by some of the most famous and successful names in Silicon Valley. Vicarious is developing a new way of processing data, inspired by the way information seems to flow through the brain. The company's leaders say this gives computers something akin to imagination, which they hope will help make the machines a lot smarter.