Lifeboat Foundation

Researchers have shown it is possible to perform artificial intelligence using tiny nanomagnets that interact like neurons in the brain.

The new method, developed by a team led by Imperial College London researchers, could slash the energy cost of artificial intelligence (AI), which is currently doubling globally every 3.5 months.

In a paper published today in Nature Nanotechnology, the international team have produced the first proof that networks of nanomagnets can be used to perform AI-like processing. The researchers showed nanomagnets can be used for ‘time-series prediction’ tasks, such as predicting and regulating insulin levels in diabetic patients.

A swarm of 10 bright blue drones lifts off in a bamboo forest in China, then swerves its way between cluttered branches, bushes and over uneven ground as it autonomously navigates the best flight path through the woods.

The experiment, led by scientists at Zhejiang University, evokes scenes from science fiction —and the authors in fact cite films such as “Star Wars,” “Prometheus” and “Blade Runner 2049” in the opening of their paper published Wednesday in the journal Science Robotics.

“Here, we take a step forward (to) such a future,” wrote the team, led by Xin Zhou.

On 7 May 1992, Space Shuttle Endeavour lifted off on her first voyage at 23:40 UTC from Pad-B at the Kennedy Space Center in Florida. Her target: Intelsat VI F-3 (now known as Intelsat 603). The goal: rendezvous with, repair, and re-release the satellite.

In the now-30 years since that mission, on-orbit satellite repair and servicing have largely languished — save for the five Hubble servicing missions Endeavour and the Shuttle fleet would conduct after STS-49.

Continue reading “30 years after Intelsat VI rescue, Northrop Grumman aims to make in-space servicing a permanent reality” »

“We provide a lot of evidence to bolster the case that this is a causal relationship, and it is driven by precisely the industries that are most affected by aging and have opportunities for automating work,”

“For decades, manufacturers in the United States have turned to automation to remain competitive in a global marketplace, but this technological innovation has reduced the number of quality jobs available to adults without a college degree—a group that has faced increased mortality in recent years,”

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Continue reading “Research Links Investment In Automation To Rising Mortality Rates” »

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They may be the only cars to go more viral than Cybertruck.

Remember that Hyundai concept 4×4 with robotic legs?

Continue reading “Hyundai has kicked-off production on novel 4x4 vehicles with robotic legs” »

Artificial intelligence and machine learning hardware research have concentrated on building photonic synapses and neurons and combining them to do fundamental forms of neural-type processing. However, complex processing methods found in human brains—such as reinforcement learning and dendritic computation—are more challenging to replicate directly in hardware.

A new study contributes to closing the “hardware gap” by creating an “Optomemristor” device that responds to numerous electronic and photonic inputs at the same time. The diverse biophysical mechanisms that govern the functions of the brain’s neurons and synapses allow for complex learning and processing in the mammalian brain.

The chalcogenide thin-film technology interacts with both light and electrical impulses to mimic multifactor biological computations in mammalian brains while spending very little energy.

Would start with scanning and reverse engineering brains of rats, crows, pigs, chimps, and end on the human brain. Aim for completion by 12/31/2025. Set up teams to run brain scans 24÷7÷365 if we need to, and partner w/ every major neuroscience lab in the world.

If artificial intelligence is intended to resemble a brain, with networks of artificial neurons substituting for real cells, then what would happen if you compared the activities in deep learning algorithms to those in a human brain? Last week, researchers from Meta AI announced that they would be partnering with neuroimaging center Neurospin (CEA) and INRIA to try to do just that.

Through this collaboration, they’re planning to analyze human brain activity and deep learning algorithms trained on language or speech tasks in response to the same written or spoken texts. In theory, it could decode both how human brains —and artificial brains—find meaning in language.

Continue reading “Meta wants to improve its AI” »

AI researchers are creating novel “benchmarks” to help models avoid real-world stumbles.

Trained on billions of words from books, news articles, and Wikipedia, artificial intelligence (AI) language models can produce uncannily human prose. They can generate tweets, summarize emails, and translate dozens of languages. They can even write tolerable poetry. And like overachieving students, they quickly master the tests, called benchmarks, that computer scientists devise for them.

That was Sam Bowman’s sobering experience when he and his colleagues created a tough new benchmark for language models called GLUE (General Language Understanding Evaluation). GLUE gives AI models the chance to train on data sets containing thousands of sentences and confronts them with nine tasks, such as deciding whether a test sentence is grammatical, assessing its sentiment, or judging whether one sentence logically entails another. After completing the tasks, each model is given an average score.

Continue reading “Computers ace IQ tests but still make dumb mistakes. Can different tests help?” »