Google has taken a brilliant and unexpected step toward building an AI with more humanlike intuition, developing a computer capable of beating even expert human players at the fiendishly complicated board game Go. The objective of Go, a game invented in China more than 2,500 years ago, is fairly simple: players must alternately place black and white "stones" on a grid of 19 horizontal and 19 vertical lines with the aim of surrounding the opponent's pieces, and avoiding having one's own pieces surrounded. Mastering Go, however, requires endless practice, as well as a finely tuned knack of recognizing subtle patterns in the arrangement of the pieces spread across the board. Google's team has shown that the skills needed to master Go are not so uniquely human after all. Their computer program, called AlphaGo, beat the European Go champion, Fan Hui, five games to zero.

Humans have an impressive ability to reason about new concepts and experiences from just a single example. In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept. We develop machine learning systems with this important capacity by developing new deep generative models, models that combine the representational power of deep learning with the inferential power of Bayesian reasoning. We develop a class of sequential generative models that are built on the principles of feedback and attention. These two characteristics lead to generative models that are among the state-of-the art in density estimation and image generation. We demonstrate the one-shot generalization ability of our models using three tasks: unconditional sampling, generating new exemplars of a given concept, and generating new exemplars of a family of concepts. In all cases our models are able to generate compelling and diverse samples---having seen new examples just once---providing an important class of general-purpose models for one-shot machine learning.

IMAGE: Insilico Medicine will present their results in applying deep learning to biomarker development and cosmetics applications at the INNOCOS World Beauty Innovation Summit in Vienna 9-10th of June. Insilico Medicine to present their results in applying deep learning to biomarker development and cosmetics applications at INNOCOS World Beauty Innovation Summit in Vienna 9-10th of June. INNOCOS is one of the largest annual events in the beauty industry bringing together top experts from many areas of research, R&D heads of the cosmetics conglomerates, innovation and strategy professionals and digital media experts. In addition to heading Insilico Medicine, Inc, a big data analytics company focused on applying advanced signaling pathway activation analysis and deep learning methods to biomarker and drug discovery in cancer and age-related diseases, Alex Zhavoronkov, PhD is the co-founder and principal scientist of Youth Laboratories, a company focusing on applying machine learning methods to evaluating the condition of human skin and general health status using multimodal inputs. The company developed an app called RYNKL, a mobile app for evaluating the effectiveness of various anti-aging interventions by analyzing "wrinkleness" and other parameters.

The idea of cyborgs running the world may seem like science fiction but may be becoming reality sooner than you think. No, there will not be an epic world war of us versus them. Instead, the shift from human to automation is slowly creeping into our society. Automation is already running assembly lines and even surgeries. From ATM's, pay-at-the-pump, self check-out, self-serve kiosks, order and pay at the table in restaurants, and all of the banking, shopping, record sharing, reading, socialization that takes place online, computers are already taking the place of humans.

Like any proud father, Gary Marcus is only too happy to talk about the latest achievements of his two-year-old son. More unusually, he believes that the way his toddler learns and reasons may hold the key to making machines much more intelligent. Sitting in the boardroom of a bustling Manhattan startup incubator, Marcus, a 45-year-old professor of psychology at New York University and the founder of a new company called Geometric Intelligence, describes an example of his boy's ingenuity. From the backseat of the car, his son had seen a sign showing the number 11, and because he knew that other double-digit numbers had names like "thirty-three" and "seventy-seven," he asked his father if the number on the sign was "onety-one." "He had inferred that there is a rule about how you put your numbers together," Marcus explains with a smile.

Here comes the blind fury of autonomous vehicle startups. We knew it was coming, since everyone from Google and Uber to GM and Tesla is working on the technology. Boston-area startup NuTonomy, founded by a pair of MIT scientists in 2013, is the latest to make a splash. The company announced on Tuesday a Series A funding round of 16 million, led by Highland Capital Partners, with participation from other investors including Singapore's economic development group and Fontinalis Partners (which was co-founded by Ford Motor's executive chairman Bill Ford). The company raised 3.6 million in seed funding earlier this year.

China will speed up the development of its artificial intelligence (AI) sector and create a market worth more than 100 billion yuan ( 15.26 billion) over the next three years, the National Development and Reform Commission said in a statement released on its website Monday. By 2018, China shall build platforms for fundamental AI resources and innovation and make breakthroughs on basic core technology, said the three-year implementation program for "Internet Plus" artificial intelligence. The plan is formulated jointly by the National Development and Reform Commission, the Ministry of Science and Technology, the Ministry of Industry and Information Technology, and the Cyberspace Administration of China. According to the website, the country shall be in line with global AI technology and industries by 2018. At key regions, the country will cultivate some global leading AI enterprises and set up an innovative, open, cooperative, green and safe AI industrial ecology.

A KFC restaurant in China is now using two robots to help take customers' orders. KFC said it enlisted the robots in its concept store in Shanghai to bring a fun and high-tech element to the dining experience. The store is still largely run and staffed by humans who prepare food and take orders. But customers also have the option of carrying on a conversation with one of the robots to place and pay for their order. The robots, developed by the Chinese tech company Baidu, specialize in understanding human speech.

Two major car companies announced on Tuesday investments in ride-hailing apps, signaling both a growing role for on-demand cars and a new groundwork for app-enabled self-driving fleets. Toyota will be investing and partnering with Uber, and Volkswagen is putting 300m into Tel Aviv-based ride-sharing app called Gett. In January, General Motors, a longtime Toyota rival, announced that it was putting 500m into Lyft, Uber's most direct competitor. "Ride-sharing has huge potential in terms of shaping the future of mobility," said Shigeki Tomoyama, senior managing officer of Toyota Motor Corporation and president of the Connected Company, a new group within Toyota. "Through this collaboration with Uber, we would like to explore new ways of delivering secure, convenient and attractive mobility services to customers."

Zebra capitalizes on Israel's strong computer vision industry and automates medical diagnoses computed by looking at x-rays, MRIs, and other scans The new investment was led by Intermountain Healthcare with participation from existing funders Khosla Ventures and Marc Benioff. The round brings their total funding to 20 million following an 8 million round in April 2015. Zebra capitalizes on Israel's strong computer vision industry and automates medical diagnoses computed by looking at x-rays, MRIs, and other scans. The company already claims hundreds of thousands of cases have been scanned with algorithms specifically focused on liver health, lung health, cardiovascular analysis, and bone density. They currently claim over 1,100 customers.