Tag Archives: dog
There's a lot of hype around the release of Sony's latest robotic dog. It's called Aibo, and is promoted as using artificial intelligence to respond to people looking at it, talking to it and touching it. Continue reading
Elon Musk Presents His Tunnel Vision to the People of LA
Jack Stewart and Aarian Marshall | Wired
“Now, Musk wants to build this new, 2.1-mile tunnel, near LA’s Sepulveda pass. It’s all part of his broader vision of a sprawling network that could take riders from Sherman Oaks in the north to Long Beach Airport in the south, Santa Monica in the west to Dodger Stadium in the east—without all that troublesome traffic.”
Feel What This Robot Feels Through Tactile Expressions
Evan Ackerman | IEEE Spectrum
“Guy Hoffman’s Human-Robot Collaboration & Companionship (HRC2) Lab at Cornell University is working on a new robot that’s designed to investigate this concept of textural communication, which really hasn’t been explored in robotics all that much. The robot uses a pneumatically powered elastomer skin that can be dynamically textured with either goosebumps or spikes, which should help it communicate more effectively, especially if what it’s trying to communicate is, ‘Don’t touch me!’”
In Virtual Reality, How Much Body Do You Need?
Steph Yin | The New York Times
“In a paper published Tuesday in Scientific Reports, they showed that animating virtual hands and feet alone is enough to make people feel their sense of body drift toward an invisible avatar. Their work fits into a corpus of research on illusory body ownership, which has challenged understandings of perception and contributed to therapies like treating pain for amputees who experience phantom limb.”
How Graphene and Gold Could Help Us Test Drugs and Monitor Cancer
Angela Chen | The Verge
“In today’s study, scientists learned to precisely control the amount of electricity graphene generates by changing how much light they shine on the material. When they grew heart cells on the graphene, they could manipulate the cells too, says study co-author Alex Savtchenko, a physicist at the University of California, San Diego. They could make it beat 1.5 times faster, three times faster, 10 times faster, or whatever they needed.”
Robotic Noses Could Be the Future of Disaster Rescue—If They Can Outsniff Search Dogs
Eleanor Cummins | Popular Science
“While canine units are a tried and fairly true method for identifying people trapped in the wreckage of a disaster, analytical chemists have for years been working in the lab to create a robotic alternative. A synthetic sniffer, they argue, could potentially prove to be just as or even more reliable than a dog, more resilient in the face of external pressures like heat and humidity, and infinitely more portable.”
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Boston Dynamics’ SpotMini Robot Dog Goes on Sale in 2019
Stephen Shankland | CNET
“The company has 10 SpotMini prototypes now and will work with manufacturing partners to build 100 this year, said company co-founder and President Marc Raibert at a TechCrunch robotics conference Friday. ‘That’s a prelude to getting into a higher rate of production’ in anticipation of sales next year, he said. Who’ll buy it? Probably not you.”
Also from Boston Dynamics’ this week:
Made In Space Wins NASA Contract for Next-Gen ‘Vulcan’ Manufacturing System
Mike Wall | Space.com
“’The Vulcan hybrid manufacturing system allows for flexible augmentation and creation of metallic components on demand with high precision,’ Mike Snyder, Made In Space chief engineer and principal investigator, said in a statement. …When Vulcan is ready to go, Made In Space aims to demonstrate the technology on the ISS, showing Vulcan’s potential usefulness for a variety of exploration missions.”
Duplex Shows Google Failing at Ethical and Creative AI Design
Natasha Lomas | TechCrunch
“But while the home crowd cheered enthusiastically at how capable Google had seemingly made its prototype robot caller—with Pichai going on to sketch a grand vision of the AI saving people and businesses time—the episode is worryingly suggestive of a company that views ethics as an after-the-fact consideration. One it does not allow to trouble the trajectory of its engineering ingenuity.”
What Artists Can Tech Us About Making Technology More Human
Elizabeth Stinson| Wired
“For the last year, Park, along with the artist Sougwen Chung and dancers Jason Oremus and Garrett Coleman of the dance collective Hammerstep, have been working out of Bell Labs as part of a residency called Experiments in Art and Technology. The year-long residency, a collaboration between Bell Labs and the New Museum’s incubator, New Inc, culminated in ‘Only Human,’ a recently-opened exhibition at Mana where the artists’ pieces will be on display through the end of May.”
The White House Says a New AI Task Force Will Protect Workers and Keep America First
Will Knight | MIT Technology Review
“The meeting and the select committee signal that the administration takes the impact of artificial intellgence seriously. This has not always been apparent. In his campaign speeches, Trump suggested reviving industries that have already been overhauled by automation. The Treasury secretary, Steven Mnuchin, also previously said that the idea of robots and AI taking people’s jobs was ‘not even on my radar screen.’”
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As Japan celebrates the year of the dog, electronics giant Sony on Thursday unleashed its new robot canine companion, packed with artificial intelligence and internet connectivity. Continue reading
Major websites all over the world use a system called CAPTCHA to verify that someone is indeed a human and not a bot when entering data or signing into an account. CAPTCHA stands for the “Completely Automated Public Turing test to tell Computers and Humans Apart.” The squiggly letters and numbers, often posted against photographs or textured backgrounds, have been a good way to foil hackers. They are annoying but effective.
The days of CAPTCHA as a viable line of defense may, however, be numbered.
Researchers at Vicarious, a Californian artificial intelligence firm funded by Amazon founder Jeff Bezos and Facebook’s Mark Zuckerberg, have just published a paper documenting how they were able to defeat CAPTCHA using new artificial intelligence techniques. Whereas today’s most advanced artificial intelligence (AI) technologies use neural networks that require massive amounts of data to learn from, sometimes millions of examples, the researchers said their system needed just five training steps to crack Google’s reCAPTCHA technology. With this, they achieved a 67 percent success rate per character—reasonably close to the human accuracy rate of 87 percent. In answering PayPal and Yahoo CAPTCHAs, the system achieved an accuracy rate of greater than 50 percent.
The CAPTCHA breakthrough came hard on the heels of another major milestone from Google’s DeepMind team, the people who built the world’s best Go-playing system. DeepMind built a new artificial-intelligence system called AlphaGo Zero that taught itself to play the game at a world-beating level with minimal training data, mainly using trial and error—in a fashion similar to how humans learn.
Both playing Go and deciphering CAPTCHAs are clear examples of what we call narrow AI, which is different from artificial general intelligence (AGI)—the stuff of science fiction. Remember R2-D2 of Star Wars, Ava from Ex Machina, and Samantha from Her? They could do many things and learned everything they needed on their own.
Narrow AI technologies are systems that can only perform one specific type of task. For example, if you asked AlphaGo Zero to learn to play Monopoly, it could not, even though that is a far less sophisticated game than Go. If you asked the CAPTCHA cracker to learn to understand a spoken phrase, it would not even know where to start.
To date, though, even narrow AI has been difficult to build and perfect. To perform very elementary tasks such as determining whether an image is of a cat or a dog, the system requires the development of a model that details exactly what is being analyzed and massive amounts of data with labeled examples of both. The examples are used to train the AI systems, which are modeled on the neural networks in the brain, in which the connections between layers of neurons are adjusted based on what is observed. To put it simply, you tell an AI system exactly what to learn, and the more data you give it, the more accurate it becomes.
The methods that Vicarious and Google used were different; they allowed the systems to learn on their own, albeit in a narrow field. By making their own assumptions about what the training model should be and trying different permutations until they got the right results, they were able to teach themselves how to read the letters in a CAPTCHA or to play a game.
This blurs the line between narrow AI and AGI and has broader implications in robotics and virtually any other field in which machine learning in complex environments may be relevant.
Beyond visual recognition, the Vicarious breakthrough and AlphaGo Zero success are encouraging scientists to think about how AIs can learn to do things from scratch. And this brings us one step closer to coexisting with classes of AIs and robots that can learn to perform new tasks that are slight variants on their previous tasks—and ultimately the AGI of science fiction.
So R2-D2 may be here sooner than we expected.
This article was originally published by The Washington Post. Read the original article here.
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