Lifeboat Foundation

A new study in Nature reports an AI-driven advance in biotechnology with implications for drug development, disease detection, and environmental monitoring. Scientists at the Institute for Protein Design at the University of Washington School of Medicine used software to create protein molecules that bind with exceptionally high affinity and specificity to a variety of challenging biomarkers, including human hormones.

Notably, the scientists achieved the highest interaction strength ever reported between a computer-generated biomolecule and its target.

Senior author David Baker, professor of biochemistry at UW Medicine and Howard Hughes Medical Institute investigator, emphasized the potential impact: “The ability to generate novel proteins with such high binding affinity and specificity opens up a world of possibilities, from new disease treatments to advanced diagnostics.”

Shoppers can now get at a Walmart in Delaware — and more of the walk-in clinics are set to open in 2024.

Elon Musk’s sometimes spicy rival to OpenAI’s ChatGPT is trained on X posts, and can speak with a sense of humor.

Juan Bernabé-Moreno is IBM’s director of research for Ireland and the United Kingdom. The Spanish computer scientist is also responsible for IBM’s climate and sustainability strategy, which is being developed by seven global laboratories using artificial intelligence (AI) and quantum computing. He believes quantum computing is better suited to understanding nature and matter than classical or traditional computers.

Question. Is artificial intelligence a threat to humanity?

Answer. Artificial intelligence can be used to cause harm, but it’s crucial to distinguish between intentional and malicious use of AI, and unintended behavior due to lack of data control or governance rigor.

Electronics that mimic the treelike branches that form the network neurons use to communicate with each other could lead to artificial intelligence that no longer requires the megawatts of power available in the cloud. AI will then be able to run on the watts that can be drawn from the battery in a smartphone, a new study suggests.

As the brain-imitating AI systems known as neural networks grow in size and power, they are becoming more expensive and energy-hungry. For instance, to train its state-of-the-art neural network GPT-3, OpenAI spent US $4.6 million to run 9,200 GPUs for two weeks. Generating the energy that GPT-3 consumed during training released as much carbon as 1,300 cars would have spewed from their tailpipes over the same time, says study author Kwabena Boahen, a neuromorphic engineer at Stanford University, in California.

Now Boahen proposes a way for AI systems to boost the amount of information conveyed in each signal they transmit. This could reduce both the energy and space they currently demand, he says.

The concept of dendrocentric learning artificial intelligence is proposed to replace synaptocentric learning, reducing the energy use requirement and removing the thermal constraint.

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Summary: A recent study highlights the cognitive benefits of Interlingual Respeaking (IRSP), where language professionals collaborate with speech recognition software to create live subtitles in another language. This process, which combines simultaneous translation with the addition of punctuation and content labels, was the focus of a 25-hour upskilling course involving 51 language professionals.

The course showed significant improvements in executive functioning and working memory. Researchers emphasize that such training not only enhances cognitive abilities but also equips language professionals for the rapidly evolving AI-driven language industry.

Deep learning AI models could be used to screen for autism and check on the severity of the condition, according to new research – and all the AI might need is a photo of the subject’s retina.

Previous studies have linked changes in retinal nerves with altered brain structures, and from there to Autism Spectrum Disorder (ASD). The evidence suggests the eye really is a window to the brain, via the interconnectedness of the central nervous system.

“Individuals with ASD have structural retinal changes that potentially reflect brain alterations, including visual pathway abnormalities through embryonic and anatomic connections,” researchers write in their new paper.