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Category: robotics/AI – Page 529

OpenAI isn’t ruling out that its forthcoming Sora video generator might create nudity — and that could be bad news for the company.

In a sweeping interview with the Wall Street Journal about the forthcoming tool, OpenAI chief technology officer Mira Murati suggested that the company hasn’t yet figured out the whole nudity thing.

“I’m not sure,” Murati told the WSJ’s reporters when asked about nudity. “You can imagine that there are creative settings in which artists might want to have more control over that. Right now we are working with artists, creators from different fields to figure out what’s useful, what level of flexibility the tool [should] provide.”

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Facebook is quickly being overrun by dubious, AI-generated junk — which is somehow attracting huge amounts of attention from its aging user base.

Worse yet, according to a new analysis by Stanford and Georgetown University researchers, first spotted by 404 Media, scammers and spammers are using this lowbrow content to grow their audiences on Facebook.

Even with the proliferation of AI image generators, the novelty has clearly yet to wear off for users on the largest social media network, a veritable social media dinosaur.

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At first glance, Rabih O. Al-Kaysi’s molecular motors look like the microscopic worms you’d see in a drop of pond water. But these wriggling ribbons are not alive; they’re devices made from crystallized molecules that perform coordinated movements when exposed to light. With continued development, Al-Kaysi and colleagues say, their tiny machines could be used by physicians as drug-delivery robots or engineered into arrays that direct the flow of water around submarines.

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Hung-Wei Tseng, a UC Riverside associate professor of electrical and computer engineering, has laid out a paradigm shift in computer architecture to do just that in a recent paper titled, “Simultaneous and Heterogeneous Multithreading.”

Tseng explained that today’s computer devices increasingly have graphics processing units (GPUs), hardware accelerators for artificial intelligence (AI) and machine learning (ML), or digital signal processing units as essential components. These components process information separately, moving information from one processing unit to the next, which in effect creates a bottleneck.

In their paper, Tseng and UCR computer science graduate student Kuan-Chieh Hsu introduce what they call “simultaneous and heterogeneous multithreading” or SHMT. They describe their development of a proposed SHMT framework on an embedded system platform that simultaneously uses a multi-core ARM processor, an NVIDIA GPU, and a Tensor Processing Unit hardware accelerator.

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Tesla Summon and Autopark are set to gain major improvements next month, according to company CEO Elon Musk. Autopark is also getting a new name, Musk said, as it appears to be on its way to being called “Banish.”

After Musk stated earlier this month that Tesla would have some “really cool stuff coming this month and next,” owners and fans of the company were left with their own imaginations to think of what could possibly be coming.

While many owners have wished for improvements of things like the Auto Wipers, Tesla has been working behind the scenes to improve some of its semi-autonomous driving features and certain parts of Enhanced Autopilot, including Summon and Autopark.

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It takes more than a galaxy merger to make a black hole grow and new stars form: machine learning shows cold gas is needed too to initiate rapid growth — new research finds.

When they are active, supermassive black holes play a crucial role in the way galaxies evolve. Until now, growth was thought to be triggered by the violent collision of two galaxies followed by their merger, however new research led by the University of Bath suggests galaxy mergers alone are not enough to fuel a black hole — a reservoir of cold gas at the centre the host galaxy is needed too.

The new study, published this week in the journal Monthly Notices of the Royal Astronomical Society is believed to be the first to use machine learning to classify galaxy mergers with the specific aim of exploring the relationship between galaxy mergers, supermassive black-hole accretion and star formation. Until now, mergers were classified (often incorrectly) through human observation alone.

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