Science Space Deep Space Halley's comet may need a new, medieval name Astronomers suggest the honor should go to an 11th century monk known for a disastrous flying attempt. Breakthroughs, discoveries, and DIY tips sent six days a week. One of most recognizable comets in astronomy may require rebranding. But even if everyone continues to call the famed space rock Halley's comet, some researchers say an eccentric 11th century monk deserves at least credit. According to a review of historical materials including the famous Bayeux tapestry, a team from Leiden University in the Netherlands believes it makes more sense to name the icy space rock in honor of Aethelmaer of Malmesbury --a member of the Order of Saint Benedict who also lived with an ill-fated fascination with flying.

The 770-pound Bayeux Tapestry depicts the Norman conquest of England in 1066. Breakthroughs, discoveries, and DIY tips sent every weekday. Whether it's the morning paper, the games on the back of a cereal box, or just scrolling through social media, there is something nice about reading with a meal. For the monks living in St. Augustine's Abbey in Canterbury, England, one of the most famous (and phallic) tapestries in the world may have been their equivalent to the back of the cereal box. New research recently published in the journal claims that the 1,000-year-old Bayeux Tapestry may have served as mealtime reading.

An artificial intelligence-based chatbot will start answering questions from a Buddhist viewpoint in English in Bhutan, its developers including a Kyoto University professor said Monday. The team of the university and a startup initially developed a chatbot called Buddhabot in 2021 with the Japanese translation of the Sutta Nipata, considered to be the oldest collection of discourses of Buddha. Data on other classic collections were also incorporated into the AI Buddha later. In 2023, the team remodeled the Buddhabot using OpenAI's ChatGPT generative AI to create Buddhabot Plus, which adds interpretations and explanations to the discourses. The English version of Buddhabot Plus was completed last year following the Bhutanese government's request.

It appears that no boy sharks were invited to this gathering of sleeping female Port Jackson sharks (Heterodontus portusjacksoni) in Australia. The fish were spotted snuggled up along the seafloor at Beagle Marine Park in the central Bass Strait. "There were thousands of sharks tightly packed like a carpet spread across the seafloor," voyage leader and University of Tasmania quantitative marine spatial ecologist Jacquomo Monk said in a statement. "Port Jackson sharks grow to 1.65 meters [5.4 feet] in length and are found across southern Australia." Scientists supported by Australia's National Environmental Science Program from the South Australian Research and Development Institute's research vessel MRV Ngerin were operating an underwater robot when they spotted and recorded the gathering.

After evidence surfaced in 2018 that leading face-analysis algorithms were less accurate for people with darker skin, companies including Google and Meta adopted measures of skin tone to test the effectiveness of their AI software. New research from Sony suggests that those tests are blind to a crucial aspect of the diversity of human skin color. By expressing skin tone using only a sliding scale from lightest to darkest or white to black, today's common measures ignore the contribution of yellow and red hues to the range of human skin, according to Sony researchers. They found that generative AI systems, image-cropping algorithms, and photo analysis tools all struggle with yellower skin in particular. The same weakness could apply to a variety of technologies whose accuracy is proven to be affected by skin color, such as AI software for face recognition, body tracking, and deepfake detection, or gadgets like heart rate monitors and motion detectors. "If products are just being evaluated in this very one-dimensional way, there's plenty of biases that will go undetected and unmitigated," says Alice Xiang, lead research scientist and global head of AI Ethics at Sony.

Detection tools being developed to combat the growing threat of deepfakes – realistic-looking false content – must use training datasets that are inclusive of darker skin tones to avoid bias, experts have warned. Most deepfake detectors are based on a learning strategy that depends largely on the dataset that is used for its training. It then uses AI to detect signs that may not be clear to the human eye. This can include monitoring blood flow and heart rate. However, these detection methods do not always work on people with darker skin tones, and if training sets do not contain all ethnicities, accents, genders, ages and skin-tone, they are open to bias, experts warned.

Detection tools being developed to combat the growing threat of deepfakes – realistic-looking false content – must use training datasets that are inclusive of darker skin tones to avoid bias, experts have warned. Most deepfake detectors are based on a learning strategy that depends largely on the dataset that is used for its training. It then uses AI to detect signs that may not be clear to the human eye. This can include monitoring blood flow and heart rate. However, these detection methods do not always work on people with darker skin tones, and if training sets do not contain all ethnicities, accents, genders, ages and skin-tone, they are open to bias, experts warned.

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