AI & RoboticsNews

Researchers detail AI that de-hazes and colorizes underwater photos

Ever notice that underwater images tend to be be blurry and somewhat distorted? That’s because phenomena like light attenuation and back-scattering adversely affect visibility. To remedy this, researchers at Harbin Engineering University in China devised a machine learning algorithm that generates realistic water images, along with a second algorithm that trains on those images to both restore…
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AI & RoboticsNews

Microsoft proposes AI that improves when you smile

Positive affectivity, or the characteristic that describes how people experience affects (e.g., sensations, emotions, and sentiments) and interact with others as a consequence, has been linked to increased interest and curiosity as well as satisfaction in learning. Inspired by this, a team of Microsoft researchers propose imbuing reinforcement learning, an AI training technique that employs…
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AI & RoboticsNews

Amazon researchers use AI to improve the recognition of curved text

Optical character recognition (OCR), or the conversion of images of handwritten or printed text into machine-readable text, is a science that dates back to the early ’70s. But algorithms have long struggled to make out characters that aren’t parallel with horizontal planes, which is why researchers at Amazon developed what they call TextTubes. They’re detectors for curved text in natural…
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AI & RoboticsNews

Researchers want to use Mega Man 2 to evaluate AI

Games have long served as a training ground for AI algorithms, and not without good reason. Games — particularly video games — provide challenging environments against which to benchmark autonomous systems. In 2013, a team of researchers introduced the Arcade Learning…
AI & RoboticsNews

Google Brain’s AI achieves state-of-the-art text summarization performance

Summarizing text is a task at which machine learning algorithms are improving, as evidenced by a recent paper published by Microsoft. That’s that’s good news — automatic summarization systems promise to cut down on the amount message-reading done by enterprise workers, which one survey estimates amounts to 2.6 hours each day. Not to be outdone, a Google Brain and Imperial College London team…
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