In May, 11,865 new passenger plug-in cars were registered in Norway (up 21% year-over-year), which is about 88.9% of the total volume.
Inspired by biological fractals, a team of researchers affiliated with multiple institutions in China has developed a new pore structure for a membrane used to separate uranium from seawater. In their paper published in the journal Nature Sustainability, the group describes their pore structure and how well it worked when tested. Alexander Wiechert and Sotira Yiacoumi with the Georgia Institute of Technology and Costas Tsouris with Oak Ridge National Laboratory, have published a News & Views piece on the work done by the team in China and the work that is left to do before the membrane can be commercialized.
In the 1950s, scientists realized that the world’s oceans held the potential for supplying the uranium needed to produce atomic weapons and electrical power. But it took another 30 years before a viable means of extracting uranium was developed. A team of researchers in Japan developed an amidoxime-grafted adsorbent that appeared able to do the job, but only in a limited way. In this new effort, the researchers have expanded on the work by the Japanese team to create a membrane for use in filtering uranium from seawater.
The membrane created by the team in China is based on a hierarchical pore structure that was modeled on fractals found in nature. Seawater containing uranium enters the outer portion of the membrane through macropores. The molecules in the water then migrate into a branching matrix of smaller channels. From there, they are carried to a microporous inner portion of the membrane where the uranium is absorbed by an amidoxime-grafted adsorbent. Testing showed it capable of extracting 9 mg g−1 from a sample of seawater over four weeks.
One of Meta’s top executives showed employees a preview of the company’s upcoming Twitter competitor during a companywide meeting today that was watched by The Verge. You can see some of the screenshots above.
Meta says people want a communications platform that’s “sanely run.”
A mention of his village in a 13th-century inscription stone made P L Udaya Kumar quit his job and start preserving the history of Bengaluru localities.
Developer Enias Cailliau talks to his girlfriend Sacha on Telegram. She sends him voice memos, texts, and even the occasional selfie. But Sacha isn’t actually real, she’s an AI clone of Cailliau’s real-life girlfriend. Cailliau calls the bot GirlfriendGPT and has now shared his code online for anyone to create their own AI girlfriends too.
“I’d recommend couples to explore the tech as well,” said Sascha Ludwig, whose programmer partner is cloning them with AI.
The Birthplace of AI
Posted in futurism, robotics/AI
Everyone is talking AI will do this AI will do that. Was that what people intended AI to do?
1) How will AI help meet demands.
2) How will AI help solve problems.
We often need to know our past to chart our future.
The Dartmouth Summer Research Project on Artificial Intelligence was a summer workshop widely considered to be the founding moment of artificial intelligence as a field of research. Held for eight weeks in Hanover, New Hampshire in 1956 the conference gathered 20 of the brightest minds in computer-and cognitive science for a workshop dedicated to the conjecture:
When Einstein gave General Relativity to the world, he included an extraneous cosmological constant. How did his ‘biggest blunder’ occur?
Dr. Blanca Lizaola-Mayo, MD, a transplant hepatologist and the Medical Director of the Liver Transplant Center at the Mayo Clinic in Arizona, is sounding the alarm on NASH or nonalcoholic steatohepatitis.
NASH occurs when a “fatty liver” or nonalcoholic fatty liver disease (NAFLD) leads to inflammation and liver cell damage called fibrosis. In its most severe form, it can be fatal.
Different people tend to have unique needs and preferences—particularly when it comes to cleaning or tidying up. Home robots, especially robots designed to help humans with house chores, should ideally be able to complete tasks in ways that account for these individual preferences.
Researchers at Princeton University and Stanford University recently set out to personalize the assistance offered by home robots using large language models (LLMs), a class of artificial intelligence models that are becoming increasingly popular after the release of ChatGPT. Their approach, presented in a paper pre-published on arXiv, was initially tested on a mobile robot called TidyBot engineered to tidy up indoor environments.
“For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios,” Jimmy Wu, Rika Antonova and their colleagues wrote in their paper. “In this work, we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away.”
