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Geoffrey Hinton, who has been called the ‘Godfather of AI,’ confirmed Monday that he left his role at Google last week to speak out about the “dangers” of the technology he helped to develop.

Hinton’s pioneering work on neural networks shaped artificial intelligence systems powering many of today’s products. He worked part-time at Google for a decade on the tech giant’s AI development efforts, but he has since come to have concerns about the technology and his role in advancing it.

“I console myself with the normal excuse: If I hadn’t done it, somebody else would have,” Hinton told the New York Times, which was first to report his decision.

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Defining computational neuroscience The evolution of computational neuroscience Computational neuroscience in the twenty-first century Some examples of computational neuroscience The SpiNNaker supercomputer Frontiers in computational neuroscience References Further reading

The human brain is a complex and unfathomable supercomputer. How it works is one of the ultimate mysteries of our time. Scientists working in the exciting field of computational neuroscience seek to unravel this mystery and, in the process, help solve problems in diverse research fields, from Artificial Intelligence (AI) to psychiatry.

Computational neuroscience is a highly interdisciplinary and thriving branch of neuroscience that uses computational simulations and mathematical models to develop our understanding of the brain. Here we look at: what computational neuroscience is, how it has grown over the last thirty years, what its applications are, and where it is going.

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A study in the Journal of Investigative Dermatology suggested that using a cannabinoid receptor type 2 (CB2) agonist called lenabasum may lessen the discomfort caused by amyopathic dermatomyositis. Dermatomyositis is a rare systemic autoimmune disease with distinctive cutaneous features frequently accompanied by muscle inflammation, interstitial lung disease, and malignancy. This phase 2 trial examined the potential benefits of activating the endocannabinoid system to reduce the inflammation causing the symptoms.

Study participants included twenty-two adults diagnosed with moderate to severe skin disease caused by dermatomyositis. They received 20 mg daily of lenabasum or a placebo for 28 days, then 20 mg twice daily for 56 days. Their Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI) levels were evaluated relative to baseline as well as secondary outcomes such as quality of life (measured with the Skindex-29) and specific biomarkers.

More than 40% of the patients taking lenabasum demonstrated significant improvements. The study showed that the CB2 agonist lenabasum improved the skin of amyopathic dermatomyositis patients. The researchers noted that lenabasum was well-tolerated and effective. More than 40% of the patients in the study taking lenabasum demonstrated significant improvements on the CDASI, a validated disease-severity scale. Results showed a trend for the change from baseline CDASI to be greater in lenabasum versus placebo starting at Day 43, two weeks after a dose increase. On Day 113 there was a statistically significant difference between the two groups. The researchers noted that the drug was well tolerated.

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This is according to a press release by the institutions published on Thursday.

“We’ve come up with an unprecedented principle. Yes, the wood transistor is slow and bulky, but it does work, and has huge development potential,” said Isak Engquist, senior associate professor at the Laboratory for Organic Electronics at Linköping University.

This isn’t the first time scientists have attempted to produce wooden transistors but previous trials resulted in versions that could regulate ion transport only. Making matters worse was the fact that when the ions ran out, the transistor stopped functioning.

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To get an inside look at the heart, cardiologists often use electrocardiograms (ECGs) to trace its electrical activity and magnetic resonance images (MRIs) to map its structure. Because the two types of data reveal different details about the heart, physicians typically study them separately to diagnose heart conditions.

Now, in a paper published in Nature Communications, scientists in the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard have developed a that can learn patterns from ECGs and MRIs simultaneously, and based on those patterns, predict characteristics of a patient’s . Such a tool, with further development, could one day help doctors better detect and diagnose heart conditions from routine tests such as ECGs.

The researchers also showed that they could analyze ECG recordings, which are easy and cheap to acquire, and generate MRI movies of the same heart, which are much more expensive to capture. And their method could even be used to find new genetic markers of heart disease that existing approaches that look at individual data modalities might miss.

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Worms can entangle themselves into a single, giant knot, only to quickly unravel themselves from the tightly wound mess within milliseconds. Now, math shows how they do it.

Researchers studied California blackworms (Lumbriculus variegatus) — thin worms that can grow to be 4 inches (10 centimeters) in length — in the lab, watching as the worms intertwined by the thousands. Even though it took the worms minutes to form into a ball-shaped blob akin to a snarled tangle of Christmas lights, they could untangle from the jumble in the blink of an eye when threatened, according to a study published April 28 in the journal Science (opens in new tab).

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