Zulfikar Ramzan, the former chief technology officer for cybersecurity giant RSA, holds a controversial belief regarding artificial intelligence …

What is vector search and how is it transforming the search experience? Edo Liberty, CEO of Pinecone and former head of Amazon's AI lab, explains.

A new research hub at Northwestern University will explore the impact of artificial intelligence systems and seek ways to better incorporate …

Summary: A new computer interface allowed participants to convey their emotions through music by changing elements of the musical tune. New research conducted by experts from Durham University's Department of Music found that people are able to convey particular emotions through music by changing certain elements of the musical tune. The researchers created an interactive computer interface called EmoteControl which allows users to control six cues (tempo, pitch, articulation, dynamics, brightness, and mode) of a musical piece in real-time. The participants were asked to show how they think seven different emotions (sadness, calmness, joy, anger, fear, power, and surprise) should sound as music. They did this by changing the musical cues in EmoteControl, essentially allowing them to create their own variations of a range of music pieces that portrayed different emotions.

You didn't think Netflix would leave its TikTok-style comedy feed on phones, did you? Sure enough, the company is launching a test that brings the Fast Laughs feature to TVs. Opt in and you'll get a flurry of hopefully funny clips from Netflix shows, movies and (of course) comedy specials. Find something you enjoy and you can watch the whole affair or add it to your watch list. The addition is "slowly" deploying to subscribers in English-speaking countries including the US, Canada, UK, Ireland, Australia and New Zealand.

An image of the rapper Lil Peep before (left) and after (right) tattoo removal by an algorithm. Researchers at Germany's Darmstadt University of Applied Sciences trained an algorithm to remove facial tattoos to improve facial recognition systems. The process involved automatically adding ink to 41 images of untattooed faces, with tattoos covering 5% to 25% of the face in the image. A generative adversarial network (GAN) was trained using these images and was able to remove the tattoos, though it had issues with those covering the entire face. The GAN-altered images were tested against a facial recognition system, halving the system's error rate when the tattoos were removed by the GAN.

we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough …

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Nokia's Intelligent RAN Operations solution goes beyond automation with machine learning to help mobile operators keep costs down with superior 5G …