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West Point Cadets Are Shooting Down Drones With Cyber Rifles

Popular Science

Tall grass hid the advancing cadets from my perch in building 7. The tall grass hid nothing from the drone the defenders flew over their position, a Parrot AR 2.0, a common model used by civilian fliers. A minute later, after the drone pilot filmed the crawling cadets, instructors called in mock artillery fire. The cadets' position was compromised, and while the rest of their platoon advanced to take the buildings, these 10 cadets instead spent an hour in the sun contemplating what they could have done about the drone. The answer was standing right behind them.


Microsoft features machine-learning startups at pitch night

#artificialintelligence

One startup makes a website that connects high-school students with the ideal college. Another operates a chatbot that can answer your simple medical questions. All have the resources of Microsoft backing them. Nine companies made pitches onstage Thursday night at Showbox SoDo as part of Microsoft Accelerator's third demo day in Seattle. The program selects 10 to 15 companies twice a year to participate in a startup accelerator program that provides resources, Microsoft Azure credits, and -- perhaps most compelling -- introductions to Microsoft's deep pool of customers.


Deep learning: How the mining industry got smart

#artificialintelligence

Recovering the planet's natural resources is hard. It's difficult, dangerous, and can be environmentally damaging. Cue an IT revolution, with smart communications, 'extreme Wi-Fi' covering vast deserts, autonomous vehicles that extract vital rocks and minerals, and geofenced employees who receive warnings if they get close to a mine's famously colossal big machinery. There's even a'smart bolt' that creates an underground support structure which is classic Internet of Things. The final goal is the autonomous mine, where humans are completely removed from the mining process.


IBM Watson: Six lessons from an early adopter on how to do machine learning - TechRepublic

#artificialintelligence

That dream of universal expertise is what IBM says its Watson question-answering, machine-learning system makes possible. Watson can be trained to answer questions on any subject you choose. The system uses natural language processing to read huge numbers of documents, extracts and organises information about a particular topic and then refines its understanding of that subject based on human feedback. But how useful are the answers given by Watson and how difficult is it to train? One person who's well-placed to talk about using the Jeopardy!-winning


The Next Revolution in AI: How it Impacts You

#artificialintelligence

We've talked a little about how the AI in our existing devices works and how it needs to improve moving forward in order to better assist us. What is the next big move in AI though? Well, many think it will be an artificial intelligence living right in your ear. In fact, it seems that in-ear assistants similar to the one featured in the movie Her aren't science fiction at all; but rather an imminent reality. There are obviously a million different implications if this fully comes to fruition. But, specifically, what does this mean to you as a business person?


Andy Rubin: Artificial intelligence may be key to all connected things

#artificialintelligence

Artificial intelligence might eventually be the key not just to smartphones but to all connected things, according to Android co-founder Andy Rubin. And a single AI may power them all. "If you have computing that is as powerful as this could be, you might only need one," Rubin told attendees at Bloomberg's Tech Conference in San Francisco Tuesday. "It might not be something you carry around; it just has to be conscious." Data will pave the way for AI to reach its potential, Rubin continued, so sensors and robots will play a crucial role by gleaning information and learning from their environments.


What's Next for Artificial Intelligence

#artificialintelligence

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.


For the Golden State Warriors, Brain-Zapping Could Provide an Edge

The New Yorker

Though you couldn't tell from the picture, these particular headphones incorporated a miniature fakir's bed of soft plastic spikes above each ear, pressing gently into the skull and delivering pulses of electric current to the brain. Made by a Silicon Valley startup called Halo Neuroscience, the headphones promise to "accelerate gains in strength, explosiveness, and dexterity" through a proprietary technique called neuropriming. "Thanks to @HaloNeuro for letting me and my teammates try these out!" McAdoo tweeted. On Thursday night, McAdoo and his teammates will seek the eighty-ninth and final win of their record-breaking season, as they defend their National Basketball Association title in Game 6 of the final series against LeBron James's Cleveland Cavaliers. The headphones' apparent results, in other words, have been impressive.


FBI uses questionable facial recognition software to comb vast photo database

The Guardian

The FBI maintains a huge database of more than 411m photos culled from sources including driver's licenses, passport applications and visa applications, which it cross-references with photos of criminal suspects using largely untested and questionably accurate facial recognition software. A study from the Government Accountability Office (GAO) released on Wednesday for the first time revealed the extent of the program, which had been queried several years before through a Freedom of Information Act request from the Electronic Frontier Foundation (EFF). The GAO, a watchdog office internal to the US federal government, found that the FBI did not appropriately disclose the database's impact on public privacy until it audited the bureau in May. The office recommended that the attorney general determine why the FBI did not obey the disclosure requirements, and that it conduct accuracy tests to determine whether the software is correctly cross-referencing driver's licenses and passport photos with images of criminal suspects. The Department of Justice "disagreed" with three of the GAO's six recommendations, according to the office, which affirmed their validity.