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Artificial-intelligence startup Pearl Inc. is using machine learning to analyze dental imagery, helping insurers pinpoint whether the same X-ray was used for more than one patient and whether a procedure was necessary.

The West Hollywood, Calif., company said its system has found thousands of cases in which dentists have used the same X-rays or other images to bill insurers for multiple patients.

The algorithm lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices.

The news: A team at Facebook AI has created a reinforcement learning algorithm that lets a robot find its way in an unfamiliar environment without using a map. Using just a depth-sensing camera, GPS, and compass data, the algorithm gets a robot to its goal 99.9% of the time along a route that is very close to the shortest possible path, which means no wrong turns, no backtracking, and no exploration. This is a big improvement over previous best efforts.

Why it matters: Mapless route-finding is essential for next-gen robots like autonomous delivery drones or robots that work inside homes and offices. Some of the best robots available today, such as Spot and Atlas made by Boston Dynamics and Digit made by Agility Robotics, are packed with sensors that make them pretty good at keeping their balance and avoiding obstacles. But if you dropped them off at an unfamiliar street corner and left them to find their way home, they’d be screwed. While Facebook’s algorithm does not yet handle outside environments, it is a promising step in that direction and could probably be adapted to urban spaces.

By Rohit Talwar, Steve Wells, Alexandra Whittington, and Maria Romero

As artificial intelligence (AI) revolutionises work as we know it, how will the software testing and security industry be impacted?

The robots are coming: “Lock up your knowledge and protect your job at all costs!” The apocalyptic warnings are starting to flow of how artificial intelligence (AI) and robotics combined with other disruptive technologies could eliminate the need for humans in the workplace. Equally sceptical voices are rubbishing the idea that anything drastic will happen, citing previous industrial revolutions as proof that new jobs will emerge to fill any gaps created by the automation of existing ones. In practice, no one really knows how quickly AI might eliminate jobs or what the employment needs will be of the future businesses and industries that have not yet been born.

But the future is not black and white. Aside from the potential to take (and make) jobs, AI might also transform jobs. Below, we share a list of some critical job roles that could be transformed or eliminated completely by the use of AI and robotics over the period from 2020 to 2030. The automation of the following six jobs would bring new opportunities to the software testing world, but could also change it in other possibly in unexpected ways.

Continue reading “Here come the robots: intelligent machines could take, make, or reboot software testing and security jobs” »

RealDoll’s flagship Harmony sex robot was paraded at the AVN Adult Entertainment Expo at Las Vegas’ Hard Rock Hotel and Casino. It is capable of holding conversations with people.

Tech giant Apple has acquired Xnor.ai, an artificial intelligence startup that came from Microsoft co-founder Paul Allen’s research lab. The acquisition suggests that Apple may be planning to Xnor.ai’s machine learning tools int iPhones and iPads in the future, with processing on-device instead of in the cloud.

GeekWire first broke the news earlier Wednesday, citing sources with knowledge of the deal. According to GeekWire, the deal is reportedly worth up about $200 million. Apple paid the same $200 million for another Seattle-based AI startup, Turi, in 2016.

Unlike traditional AI that runs in massive data centers and requires network connectivity, XNOR makes AI highly efficient by allowing deep learning models to run directly on phones, IoT devices and low power microprocessors. XNOR’s technology enables AI experiences that are up to 10x faster, 200 percent more power efficient, and use 15x less memory.

To get greater efficiency, Google DeepMind’s researchers did what chip designers have long done, built a pipeline so that the learning rule for machine learning — backpropagation — is more efficient.

A new prototype uses ultrasound waves to move objects.

In a recent IEEE Spectrum article, learn how engineers are taking machine learning to the smallest microprocessors, while facing challenges in energy consumption, compressing neural networks, and privacy practices. #tinyML #neuralnetworks #machinelearning

IN FEBRUARY, a group of researchers from Google, Microsoft, Qualcomm, Samsung, and half a dozen universities will gather in San Jose, Calif., to discuss the challenge of bringing machine learning to the farthest edge of the network, specifically microprocessors running on sensors or other battery-powered devices.

Within the next 10 years, what we eat and how it’s grown will be fundamentally transformed.

And converging exponential technologies—from materials science to AI-driven digital agriculture—are not slowing down. Today’s breakthroughs will soon allow our planet to boost its food production by nearly 70 percent, using a fraction of the real estate and resources, to feed 9 billion by mid-century.

What you consume, how it was grown, and how it will end up in your stomach will all ride the wave of converging exponentials, revolutionizing the most basic of human needs.

Continue reading “How 3D Printing, Vertical Farming, and Materials Science Are Overhauling Food” »