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Research Report

Artificial Intelligence Has Found an Unknown 'Ghost' Ancestor in The Human Genome


Nobody knows who she was, just that she was different: a teenage girl from over 50,000 years ago of such strange uniqueness she looked to be a'hybrid' ancestor to modern humans that scientists had never seen before. Only recently, researchers have uncovered evidence she wasn't alone. In a 2019 study analysing the complex mess of humanity's prehistory, scientists used artificial intelligence (AI) to identify an unknown human ancestor species that modern humans encountered – and shared dalliances with – on the long trek out of Africa millennia ago. "About 80,000 years ago, the so-called Out of Africa occurred, when part of the human population, which already consisted of modern humans, abandoned the African continent and migrated to other continents, giving rise to all the current populations", explained evolutionary biologist Jaume Bertranpetit from the Universitat Pompeu Fabra in Spain. As modern humans forged this path into the landmass of Eurasia, they forged some other things too – breeding with ancient and extinct hominids from other species. Up until recently, these occasional sexual partners were thought to include Neanderthals and Denisovans, the latter of which were unknown until 2010.

The human memory--facts and information

National Geographic

From the moment we are born, our brains are bombarded by an immense amount of information about ourselves and the world around us. So, how do we hold on to everything we've learned and experienced? Humans retain different types of memories for different lengths of time. We also have a working memory, which lets us keep something in our minds for a limited time by repeating it. Whenever you say a phone number to yourself over and over to remember it, you're using your working memory.

New AI-based tool helps clinicians understand and better predict adverse effects of COVID-19


The symptoms and side effects of Covid-19 are scattered across a diagnostic spectrum. Some patients are asymptomatic or experience a mild immune response, while others report significant long-term illnesses, lasting complications, or suffer fatal outcomes. Three researchers from the Georgia Institute of Technology and one from Emory University are trying to help clinicians sort through these factors and spectrum of patient outcomes by equipping healthcare professionals with a new "decision prioritization tool." The team's new artificial intelligence-based tool helps clinicians understand and better predict which adverse effects their Covid-19 patients could experience, based on comorbidities and current side effects -; and, in turn, also helps suggest specific Food and Drug Administration-approved (FDA) drugs that could help treat the disease and improve patient health outcomes. The researcher's latest findings are the focus of a new study published October 21 in Scientific Reports. The team's new methodology, or tool, is called MOATAI-VIR (Mode Of Action proteins & Targeted therapeutic discovery driven by Artificial Intelligence for VIRuses.

New study suggests breastfeeding may help prevent cognitive decline

FOX News

Breastfeeding can have long-term cognitive benefits for the mother, a new study has found. Researchers at the University of California, Los Angeles, conducted a study that found women over the age of 50 who had breastfed their babies performed better on cognitive tests compared to women who had never breastfed. "While many studies have found that breastfeeding improves a child's long-term health and well-being, our study is one of very few that has looked at the long-term health effects for women who had breastfed their babies," Molly Fox, the study's author, said in a news release. "Our findings, which show superior cognitive performance among women over 50 who had breastfed, suggest that breastfeeding may be'neuroprotective' later in life," she added. The study, titled, "Women who breastfeed exhibit cognitive benefits after age 50," asserts that breastfeeding's biological effects and psychosocial effects, such as improved stress regulation, could exert long-term benefits for the mother's brain.

AI and the tradeoff between fairness and efficacy: 'You actually can get both'


A recent study in Nature Machine Intelligence by researchers at Carnegie Mellon sought to investigate the impact that mitigating bias in machine learning has on accuracy. Despite what researchers referred to as a "commonly held assumption" that reducing disparities requires either accepting a drop in accuracy or developing new, complex methods, they found that the trade-offs between fairness and effectiveness can be "negligible in practice." "You actually can get both. You don't have to sacrifice accuracy to build systems that are fair and equitable," said Rayid Ghani, a CMU computer science professor and an author on the study, in a statement. At the same time, Ghani noted, "It does require you to deliberately design systems to be fair and equitable.

Space News: NASA mission helps solve a mystery -- why are some asteroid surfaces rocky?


This image shows a view of asteroid Bennu's rocky surface in a region near the equator. Scientists thought Bennu's surface was like a sandy beach, abundant in fine sand and pebbles, which would have been perfect for collecting samples. Past telescope observations from Earth had suggested the presence of large swaths of fine-grained material smaller than a few centimeters called fine regolith. But when NASA's OSIRIS-REx mission arrived at Bennu in late 2018, the mission saw a surface covered in boulders. The mysterious lack of fine regolith became even more surprising when mission scientists observed evidence of processes potentially capable of grinding boulders into fine regolith.

State of AI Report tracks transformers in critical infrastructure


Artificial intelligence and machine learning pioneers are rapidly expanding on techniques that were originally designed for natural language processing and translation to other domains, including critical infrastructure and the genetic language of life. This was reported in the 2021 edition of the State of AI Report by investors Nathan Benaich of Air Street Capital and Ian Hogarth, an angel investor. Started in 2018, their report aims to be a comprehensive survey of trends in research, talent, industry, and politics, with predictions mixed in. The authors are tracking "182 active AI unicorns totaling $1.3 trillion of combined enterprise value" and estimate that exits by AI companies have created $2.3 trillion in enterprise value since 2010. One of their 2020 predictions was that we would see the attention-based transformers architecture for machine learning models branch out from natural language processing to computer vision applications.

Scientists used a tiny brain implant to help a blind teacher see letters again

NPR Technology

Former science teacher Berna Gómez played a pivotal role in new research on restoring some sight to blind people. She is named as a co-author of the study that was published this week. Former science teacher Berna Gómez played a pivotal role in new research on restoring some sight to blind people. She is named as a co-author of the study that was published this week. A former science teacher who's been blind for 16 years became able to see letters, discern objects' edges -- and even play a Maggie Simpson video game -- thanks to a visual prosthesis that includes a camera and a brain implant, according to American and Spanish researchers who collaborated on the project.

AI That Can Learn Cause-and-Effect: These Neural Networks Know What They're Doing


A certain type of artificial intelligence agent can learn the cause-and-effect basis of a navigation task during training. Neural networks can learn to solve all sorts of problems, from identifying cats in photographs to steering a self-driving car. But whether these powerful, pattern-recognizing algorithms actually understand the tasks they are performing remains an open question. For example, a neural network tasked with keeping a self-driving car in its lane might learn to do so by watching the bushes at the side of the road, rather than learning to detect the lanes and focus on the road's horizon. Researchers at MIT have now shown that a certain type of neural network is able to learn the true cause-and-effect structure of the navigation task it is being trained to perform.