Words are polysemous and multi-faceted, with many shades of meanings. We suggest that sparse distributed representations are more suitable than other, commonly used, (dense) representations to express these multiple facets, and present Category Builder, a working system that, as we show, makes use of sparse representations to support multi-faceted lexical representations. We argue that the set expansion task is well suited to study these meaning distinctions since a word may belong to multiple sets with a different reason for membership in each. We therefore exhibit the performance of Category Builder on this task, while showing that our representation captures at the same time analogy problems such as "the Ganga of Egypt" or "the Voldemort of Tolkien". Category Builder is shown to be a more expressive lexical representation and to outperform dense representations such as Word2Vec in some analogy classes despite being shown only two of the three input terms.

E-commerce company Etsy announced today that it will open a new artificial intelligence research and development center in Toronto, Canada. The company broke the news yesterday during a meeting with Canadian Prime Minister Justin Trudeau in New York City. Etsy's third Machine Learning Center of Excellence, which follows on the heels of its Brooklyn and San Francisco locations, will play host to leading figures from local universities and Toronto's "deep pool of world-class machine learning talent," according to a statement. It will also aid in the company's efforts to recruit machine learning engineers. According to Etsy, the Canadian e-commerce market's growth was a deciding factor.

The White House's planned advisory committee on artificial intelligence may or may not help keep the country at the forefront of technological innovation, but it is another sign that the government is getting more serious about the importance of AI and the potential threats of falling behind in the "AI arms race." The Trump administration said earlier this month it will establish the Select Committee on Artificial Intelligence as part of an overall effort to coordinate Federal research and development efforts with regard to AI, while working to involve industry and academia to "realize the full potential of AI," according to the White House announcement from its Summit on Artificial Intelligence for American Industry. The committee will be comprised of senior research and development officials at a range of agencies and offices, including the White House Office of Science and Technology Policy (OSTP), the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), and the Intelligence Advanced Research Projects Agency (IARPA). Also at the table will be the technology-focused undersecretaries of the departments of Defense, Commerce and Energy, along with representatives from the Office of Management and Budget (OMB) and the National Security Council (NSC). "Our continued leadership in the field that American researchers pioneered is not just a request; it is an imperative," Michael Kratsios, deputy CTO of the White House OSTP, said at the event.

Over the past five years, artificial intelligence has gone from perennial vaporware to one of the technology industry's brightest hopes. Computers have learned to recognize faces and objects, understand the spoken word, and translate scores of languages. Apple, Facebook, and Microsoft--have bet their futures largely on AI, racing to see who's fastest at building smarter machines. That's fueled the perception that AI has come out of nowhere, what with Tesla's self-driving cars and Alexa chatting up your child. But this was no overnight hit, nor was it the brainchild of a single Silicon Valley entrepreneur. The ideas behind modern AI--neural networks and machine learning--have roots you can trace to the last stages of World War II. Back then, academics were beginning to build computing systems meant to store and process information in ways similar to the human brain. Over the decades, the technology had its ups and downs, but it failed to capture the attention of computer scientists broadly until around 2012, thanks to a handful of stubborn researchers who weren't afraid to look foolish. They remained convinced that neural nets would light up the world and alter humanity's destiny.

Last July, 13 U.S. military commanders and technology executives met at the Pentagon's Silicon Valley outpost, two miles from Google headquarters. It was the second meeting of an advisory board set up in 2016 to counsel the military on ways to apply technology to the battlefield. Milo Medin, a Google vice president, turned the conversation to using artificial intelligence in war games. Eric Schmidt, Google's former boss, proposed using that tactic to map out strategies for standoffs with China over the next 20 years. A few months later, the Defense Department hired Google's cloud division to work on Project Maven, a sweeping effort to enhance its surveillance drones with technology that helps machines think and see.

Naples Drone Solutions, owned by Andrew Kobza, have moved forward and working with the insurance adjusters. "We'll see some movements as rainy season kicks in," mentioned Kobza. "With each natural disaster the use of drones seems to be increasing because they've proven themselves to facilitate a faster claims experience," says Lynne McChristian, Florida representative of Insurance Information Institute. What is the future of drones? Drone companies keep on specializing and innovating to complete the task efficiently and effectively.

Federal immigration officials have abandoned their pursuit of a controversial machine-learning technology that was a pillar of the Trump administration's "extreme vetting" of foreign visitors, dealing a reality check to the goal of using artificial intelligence to predict human behavior. Immigration and Customs Enforcement officials told tech-industry contractors last summer they wanted a system for their "Extreme Vetting Initiative" that could automatically mine Facebook, Twitter and the broader Internet to determine whether a visitor might commit criminal or terrorist acts or was a "positively contributing member of society." But ICE dropped the machine-learning requirement from its request in recent months, opting instead to hire a contractor that can provide training, management and human personnel who can do the job. Federal documents say the contract is expected to cost more than $100 million and be awarded by the end of the year. After gathering "information from industry professionals and other government agencies on current technological capabilities," ICE spokeswoman Carissa Cutrell said, the focus of what the agency now calls its Visa Lifecycle Vetting program "shifted from a technology-based contract to a labor contract."

Federal immigration officials have abandoned their pursuit of a controversial machine-learning technology that was a pillar of the Trump administration's "extreme vetting" of foreign visitors, dealing a reality check to the goal of using artificial intelligence to predict human behavior. Immigration and Customs Enforcement officials told tech-industry contractors last summer they wanted a system for their "Extreme Vetting Initiative" that could automatically mine Facebook, Twitter and the broader Internet to determine whether a visitor might commit criminal or terrorist acts or was a "positively contributing member of society." But ICE dropped the machine-learning requirement from its request in recent months, opting instead to hire a contractor that can provide training, management and human personnel who can do the job. Federal documents say the contract is expected to cost more than $100 million and be awarded by the end of the year. After gathering "information from industry professionals and other government agencies on current technological capabilities," ICE spokeswoman Carissa Cutrell said, the focus of what the agency now calls its Visa Lifecycle Vetting program "shifted from a technology-based contract to a labor contract."

A Utah driver turned on the semi-autonomous functions of her Tesla vehicle and then didn't touch the steering wheel again for 80 seconds before slamming into a firetruck stopped at a red light last week, a summary of data from the car released Wednesday showed. The National Highway Traffic Safety Administration has sent its special crash investigations team to the state, the agency said as details about the Friday evening crash became public Wednesday. According to South Jordan police's summary of technician findings, the 28-year-old driver had repeatedly enabled and disabled the Autopilot features of her Tesla Model S throughout the course of her drive. She took her hands off the wheel more than a dozen times, twice for more than a minute each. The driver re-enabled Autopilot 1 minute and 22 seconds before the crash, let go of the wheel 2 seconds later and then didn't touch the wheel again before hitting the truck at 60 mph (97 kph).

After a career that included helping Alphabet Inc's Google build out data centers and speeding packages for Amazon.com Inc to customers, Jim Miller is doing what many Silicon Valley executives do after stints at big companies: finding more time to ride his bike. But this bike is a little different. Arevo Inc, a startup with backing from the venture capital arm of the Central Intelligence Agency and where Miller recently took the helm, has produced what it says is the world's first carbon fiber bicycle with 3D-printed frame. Arevo is using the bike to demonstrate its design software and printing technology, which it hopes to use to produce parts for bicycles, aircraft, space vehicles and other applications where designers prize the strength and lightness of so-called'composite' carbon fiber parts but are put off by the high-cost and labor-intensive process of making them.