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He argues policymakers need to do more to protect civilians.

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On Saturday, leaders of the Group of Seven (G7) nations made some public calls for the development and adoption of technical standards to keep artificial intelligence (AI) “trustworthy.” They added that they feared that governance of the technology has not kept pace with its growth.

This is according to a report by the Financial Post published on Saturday.

The leaders from the U.S., Japan, Germany, Britain, France, Italy, Canada and the EU said in a statement that while approaches to “the common vision and goal of trustworthy AI may vary,” the rules for digital technologies like AI should be “in line with our shared democratic values.”

Despite their potential, AI detectors often fall short of accurately identifying and mitigating cheating.

In the age of advanced artificial intelligence, the fight against cheating, plagiarism and misinformation has taken a curious turn.

As developers and companies race to create AI detectors capable of identifying content written by other AIs, a new study from Stanford scholars reveals a disheartening truth: these detectors are far from reliable. Students would probably love to hear this.

A sunspot duo has been exploding non-stop, producing a barrage of M-class solar flares on the Sun. The ultraviolet radiation has resulted in a rolling series of blackouts across the planet. More solar storm trouble coming?

Eye drops that have been contaminated with a highly drug-resistant strain of bacteria have now been linked to four deaths and 14 cases of blindness, according to an update from the Centers for Disease Control and Prevention (CDC).

The CDC said in a post on its website that it has identified 81 patients from 18 states who have been infected with VIM-GES-CRPA, a rare strain of a drug-resistant bacterium called P. aeruginosa. This includes 13 additional patients from the agency’s previous update in March.

A new genetic analysis of 290 people suggests that humans emerged at various times and places in Africa.

Year 2021

The new composite glass could be shatter-resistant.

Researchers at Stanford University have developed digital skin that can convert sensations such as heat and pressure to electrical signals that can be read by electrodes implanted in the human brain.

Although such capability was developed years earlier, the components required at that time to convert digital signals were rigid and unwieldy.

This new e-skin is soft as, well, skin. The conversion elements are seamlessly incorporated within the skin, which measures a few tens of nanometers thick.

Alzheimer’s disease (AD) is a complex neurodegenerative illness with genetic and environmental origins. Females experience faster cognitive decline and cerebral atrophy than males, while males have greater mortality rates. Using a new machine-learning method they developed called “Evolutionary Action Machine Learning (EAML),” researchers at Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute (Duncan NRI) at Texas Children’s Hospital have discovered sex-specific genes and molecular pathways that contribute to the development and progression of this condition. The study was published in Nature Communications.

“We have developed a unique machine-learning software that uses an advanced computational predictive metric called the evolutionary action (EA) score as a feature to identify genetic factors that influence AD risk separately in males and females,” Dr. Olivier Lichtarge, MD, Ph.D., professor of biochemistry and molecular biology at Baylor College of Medicine, said. “This approach lets us exploit a massive amount of evolutionary data efficiently, so we can now probe with greater accuracy smaller cohorts and identify genes involved in sex-specific differences in AD.”

EAML is an ensemble computational approach that includes nine machine learning algorithms to analyze the functional impact of non-synonymous coding variants, defined as DNA mutations that affect the structure and function of the resulting protein, and estimates their deleterious effect on biological processes using the evolutionary action (EA) score.