Wikipedia has a long-acknowledged gender bias problem. A new AI system is offering to help that issue by spotting missing scientist entries, most of which are female, and even generating write-ups for the omitted posts.
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Category: robotics/AI – Page 2066
The battle to create the best artificial intelligence chips is underway. Intel is approaching this challenge from its position as a maker of central processing units (CPUs) or the Xeon microprocessors that dominate the datacenter market. Rival Nvidia is attacking from its position as a maker of graphics processing units (GPUs), and both companies are working on solutions that will handle ground-up AI processing.
Nvidia’s GPUs have already grabbed a good chunk of the market for deep learning neural network solutions, such as image recognition — one of the biggest breakthroughs for AI in the past five years. But Intel has tried to position itself through acquisitions of companies such as Nervana, Mobileye, and Movidius. And when Intel bought Nervana for $350 million in 2016, it also picked up Nervana CEO Naveen Rao.
Rao has a background as both a computer architect and a neuroscientist, and he is now vice president and general manager of the Artificial Intelligence Products Group at Intel. He spoke this week at an event where Intel announced that its Xeon CPUs have generated $1 billion in revenue in 2017 for use in AI applications. Rao believes that the overall market for AI chips will reach $8 billion to $10 billion in revenue by 2022.
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According to Marc Ph. Stoecklin, principal research scientist at IBM Research, DeepLocker is a “new breed of highly targeted and evasive attack tools powered by AI.”
DeepLocker was designed in an attempt to improve understanding of how AI models can be combined with malware techniques to create a “new breed of malware,” Stoecklin explained in a post. This new type of malware can disguise its intent until it reaches an intended victim, which could be determined by taking advantage of facial recognition, geolocation, and voice recognition.
“The DeepLocker class of malware stands in stark contrast to existing evasion techniques used by malware seen in the wild. While many malware variants try to hide their presence and malicious intent, none are as effective at doing so as DeepLocker,” Stoecklin wrote.
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Traffic jams could be a thing of the past. Learn more about a driverless future: https://wef.ch/2Op4rCc
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What if an ultra-advanced flying robot designed for extreme military missions could join the fight to combat wildfire alongside human fire crews?
The biggest wildfire in Californian history is raging, with fire officials stating earlier this week that an area almost the size of Los Angeles has been compromised.
It is actually expected to burn through the rest of August, and experts predict the escalation in frequency and scale of wildfires will only continue going forward.
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But the grand work-changing projects of A.I., like self-driving cars and humanoid robots, are not yet commercial products. A more humble version of the technology, instead, is making its presence felt in a less glamorous place: the back office.
Artificial intelligence software is making its presence felt in subtle ways, in an unglamorous place: the back office.
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Researchers at the National Institute of Standards and Technology (NIST) have made a silicon chip that distributes optical signals precisely across a miniature brain-like grid, showcasing a potential new design for neural networks.
The human brain has billions of neurons (nerve cells), each with thousands of connections to other neurons. Many computing research projects aim to emulate the brain by creating circuits of artificial neural networks. But conventional electronics, including the electrical wiring of semiconductor circuits, often impedes the extremely complex routing required for useful neural networks.
The NIST team proposes to use light instead of electricity as a signaling medium. Neural networks already have demonstrated remarkable power in solving complex problems, including rapid pattern recognition and data analysis. The use of light would eliminate interference due to electrical charge, and the signals would travel faster and farther.
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