Laundry-folding robot maker will represent Japan at startup competition

The Japan Times

A Tokyo-based startup recognized for its laundry-folding robot has gained entry to an international competition for venture firms, and hopes to represent Japan as a country of innovation. Seven Dreamers Laboratories Inc. was chosen as Japan's entry for the Startup World Cup on Wednesday night in Tokyo, beating out nine other competitors in a business presentation contest. The Startup World Cup was launched last year by Silicon Valley-based Fenox Venture Capital with the aim of connecting more startups and investors worldwide. The final round in this year's competition will be held in San Francisco in May, and will feature 32 firms that have each won their regional preliminary rounds. The overall winner will receive a $1 million (about ¥113 million) investment.

Frost & Sullivan Names IntelliVision the 2017 Entrepreneurial Company


IntelliVision, a pioneer and leader in AI/Deep Learning video analytics software for smart cameras, announced today that it has been named the 2017 Entrepreneurial Company of the Year for Security Intelligence and Video Analytics by leading analyst firm Frost & Sullivan. "Video analytics and intelligence functions will remain the most in-demand security technology segment for customers looking to modernize their security operations and increase overall efficiency," said Danielle VanZandt, security industry analyst with Frost & Sullivan. "IntelliVision's advanced analytics and intelligence technologies, coupled with its ability to deploy inside the camera, on-premise servers, or on the cloud put it well ahead of its competition in this field." "We are honored to receive this award from Frost & Sullivan," said Vaidhi Nathan, IntelliVision's CEO. "It is a vindication of our many years of research, development and customer service in the growing field of AI-based video analytics for smart cameras."

Bots are transforming personal banking around the world


Digital banking brought a transformational wave to the banking system. As traditional banking practices slowly adjusted to the wave, artificial intelligence quickly caught up and made the bot market hotter than ever before. VentureBeat's 2016 Bots Landscape showed that under 200 companies, which make products ranging from personal assistants to AI tools and messaging, had $22 billion in funding and came with a whopping valuation of $159 billion. But there is a reason for these massive numbers. Do you have an AI strategy -- or hoping to get one?

Big question for US cities: Is Amazon's HQ2 worth the price?

Boston Herald

Dozens of cities are working frantically to land Amazon's second headquarters, raising a weighty question with no easy answer: Amazon is promising $5 billion of investment and 50,000 jobs over the next decade and a half. Yet the winning city would have to provide Amazon with generous tax breaks and other incentives that can erode a city's tax base. Most economists say the answer is a qualified yes -- that an Amazon headquarters is a rare case in which a package of at least modest enticements could repay a city over time. That's particularly true compared with other projects that often receive public financial aid, from sports stadiums to the Olympics to manufacturing plants, which generally return lesser, if any, benefits over the long run. For the right city, winning Amazon's second headquarters could help it attain the rarefied status of "tech hub," with the prospect of highly skilled, well-paid workers by the thousands spending freely, upgrading a city's urban core and fueling job growth beyond Amazon itself.

Almost Half of All Companies Have Deployed Machine Learning


If you're concerned (or super excited) about machine learning (ML) becoming mainstream, a recent survey by Oxford Economics on behalf of human resources (HR) and IT asset management company ServiceNow should pique your interest. The report, which surveyed 500 Chief Information Officers (CIOs) in 11 countries and across 25 industries, found that 49 percent of the companies are already using ML to improve traditional business processes. Of the 500 CIOs surveyed, 200 said they're already beyond the pilot stage and have begun deploying ML in some capacity. CIOs are hoping to limit user error and errors in judgement by introducing automation. Almost 70 percent of CIOs said decisions made by machines will be more accurate than those made by humans.

How 5G Will Affect the Internet of Things? - SmartData Collective


The Internet of Things (IoT) is at the heart of modern big data. It's what allows companies, and even cities, to collect endless quantities of information with minimal effort – and to act on that information, monetizing it, basing decisions on user data. Right now, though, IoT is on the edge of change because there's a new kid on the scene: 5G connectivity. Now phones act like computers and the competition for the next cutting edge innovation is stiffer than ever. As of this writing, 5G wireless technology is not yet ready to launch but the competition to bring it to market is stiff.

How Will Artificial Intelligence Impact Open Technologies?


As new technologies emerge, the battle over closed versus open systems continues to be one of the most important factors for a range of concerns that are critical to a healthy information ecosystem -- innovation, competition, privacy, security, consumer protection -- and even civil rights. With new advances in artificial intelligence -- particularly in the fields of machine learning and sensor technology -- questions of "open" versus "closed" have arisen again. In addition to code, algorithms, and data sets, this machine learning method requires immense computational power to discern complex patterns and data representations. While efforts to provide "open" code, algorithms and training data are laudable, the computation, competition, and accountability/audit concerns are unlikely to be answered with standard open source approaches.

Numerai's Master Plan – Numerai – Medium


Due to the extraordinary success of Numeraire staking, we are doubling the payouts in the staking tournament from 3000 USD per week to 6000 USD per week. For example, a data scientist could build a server that automatically downloads new data from Numerai, trains a machine learning algorithm, stakes Numeraire on the set of predictions, and repeats this process forever earning more and more money and NMR for the data scientist, and adding more and more intelligence to Numerai's hedge fund. The idea is to create an AWS AMI which has all the software you would need to do machine learning, and also all the connections to Numerai's API that you would need to send predictions live automatically to Numerai forever. Compute will create an entirely new use case for Numeraire which is directly related to improving machine learning models, increasing engagement, and achieving the goals of the master plan.


IEEE Spectrum Robotics Channel

Deep reinforcement learning (DRL) provides a model-agnostic approach to control complex dynamical systems, but has not been shown to scale to high-dimensional dexterous manipulation. Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency. In this work, we show that model-free DRL with natural policy gradients can effectively scale up to complex manipulation tasks with a high-dimensional 24-DoF hand, and solve them from scratch in simulated experiments. We demonstrate successful policies for multiple complex tasks: object relocation, in-hand manipulation, tool use, and dooropening.

Why The Time Is Now For AI And Machine Learning


With all the commotion, I found myself wanting to understand more about AI, and more specifically machine learning (ML). One of the biggest changes is the ready availability of processing power, with Moore's Law chugging away year after year. And the promise of quantum computers makes Moore's Law quite sedentary, once we get our collective heads around what an optimization algorithm looks like using the power of quantum computing. Finally, the abundance of data is a huge boon for machine learning – the "learning" part of ML is where Big Data comes in, where the algorithm looks back at history and learns from data to help predict the future.