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A Japanese inventor is hoping a robot that still needs humans will win over Asia's largest tech fair, offering a counterpoint to major technology firms pushing the boundaries of artificial intelligence (AI). Katsumori Sakakibara was showing off his little droid called Caiba at the Cutting-Edge IT & Electronics Comprehensive Exhibition (CEATEC), which kicked off Tuesday near Tokyo. Waist-high Caiba--whose name means hippocampus, a key area of the brain, in Japanese--is controlled by a human wearing a virtual reality handset and mechanical arms. If the person waves their arms, the little robot follows suit. But whatever Caiba does, it depends on a human to control it.

The City and County of San Francisco had launched an official open data portal called SF OpenData in 2009 as a product of its official open data program, DataSF. The portal contains hundreds of city datasets for use by developers, analysts, residents and more. Under the category of Public Safety, the portal contains the list of SFPD Incidents since Jan 1, 2003. In this post I have done an exploratory time-series analysis on the crime incidents dataset to see if there are any patterns. The data for this analysis was downloaded from the publicly available dataset from the City and County of San Francisco's OpenData website SF OpenData.

Japan's biggest IT-electronics trade show is at a crossroads. The annual Combined Exhibition of Advanced Technologies, or CEATEC, kicked off Tuesday in the city of Chiba. Though it earned fame as a major exhibition of consumer electronics such as TVs and home appliances such as washing machines, it now seems to be leaving that image behind. The show's organizer is shifting the focus to the so-called internet of things and related technologies including artificial intelligence and cybersecurity, while also aiming to facilitate business tie-ups between companies in different fields to foster innovation. "When you think of value in our society now, there is not so much value in just releasing some new products," said Hisato Nagao, president of Japan Electronics and Information Technology Industries Association, one of the organizers of CEATEC, which runs through Friday.

The new robot from Japanese automaker Toyota Motor Corp. can't do much but chatter in a high-pitched voice. The 39,800-yen ( 390), 10-centimeter (4-inch) -tall, doll-like Kirobo Mini -- whose name comes from "kibo," or "hope," and "robot" -- supposedly has the smarts of a 5-year-old. Fuminori Kataoka, general manager in charge of the project, says its value is emotional, going from home to car to the outdoors as a faithful companion, although the owner must do all the walking and driving. Preorders start later this year. Shipments are set for next year.

The world's first laundry sorting and folding robot will go on sale in 2017, its manufacturer said on Tuesday at the Ceatec electronics show just outside of Tokyo. Laundroid is the size of a large refrigerator and has a pull-out drawer near its base where unsorted clothes can be thrown in. A robot inside the device picks up each item of clothing and uses image analysis with artificial intelligence to figure out what kind of clothing it is so it knows the correct way to fold it. For humans, identifying and folding laundry is an easy albeit mundane task, but for a machine it's very difficult. That's reflected in the size of the device and its speed. During a demonstration on Tuesday it took about 10 minutes to pick out one garment, identify it and fold it.

Skip all the talk and go directly to the Github Repo with code and exercises. Reinforcement Learning is one of the fields I'm most excited about. Over the past few years amazing results like learning to play Atari Games from raw pixels and Mastering the Game of Go have gotten a lot of attention, but RL is also widely used in Robotics, Image Processing and Natural Language Processing. Combining Reinforcement Learning and Deep Learning techniques works extremely well. Both fields heavily influence each other.

Although technology seems to be everywhere, we continue to fill in the voids. Existing technologies evolve and change at a higher pace every year, making it challenging for some professionals to adjust.We have reached a point where some are having difficulty coping with game-changing technologies that are presumably contributing to progress. Such progress can be difficult to perceive if you are not directly benefiting from it. In my previous article, I discussed the ability of social robotics to make a positive impact on our society. This article provides an optimistic vision of the opportunity for this technology to include the general public in its development.

Compelling data reveal a discouraging truth about growth today. There has been a marked decline in the ability of traditional levers of production--capital investment and labor--to propel economic growth. Yet, the numbers tell only part of the story. Artificial intelligence (AI) is a new factor of production and has the potential to introduce new sources of growth, changing how work is done and reinforcing the role of people to drive growth in business. Accenture research on the impact of AI in 12 developed economies reveals that AI could double annual economic growth rates in 2035 by changing the nature of work and creating a new relationship between man and machine.

In Part I of this two-part series, Fred Giasson and I looked back over a decade of working within the semantic Web and found it partially successful but really the wrong question moving forward. The inadequacies of the semantic Web to date reside in its lack of attention to practical data interoperability across organizational or community boundaries. An emphasis on linked data has created an illusion that questions of data integration are being effectively addressed. Linked data is hard to publish and not the only useful form for consuming data; linked data quality is often unreliable; the linking predicates for relating disparate data sources to one another may be inadequate or wrong; and, there are no reference groundings for relating data values across datasets. Neither the semantic Web nor linked data has developed the practices, tooling or experience to actually interoperate data across the Web.