From 4,675 fully labeled bear faces on DSLR photographs, taken from research and bear-viewing sites at Brooks River, Ala., and Knight Inlet, they randomly split images into training and testing data sets. Once trained from 3,740 bear faces, deep learning went to work "unsupervised," Dr. Clapham said, to see how well it could spot differences between known bears from 935 photographs. First, the deep learning algorithm finds the bear face using distinctive landmarks like eyes, nose tip, ears and forehead top. Then the app rotates the face to extract, encode and classify facial features. The system identified bears at an accuracy rate of 84 percent, correctly distinguishing between known bears such as Lucky, Toffee, Flora and Steve.

Our blood transports many chemicals besides oxygen and carbon dioxide. Some of these molecules provide useful indicators of the state of our health. Indeed, measuring such biomarkers is a common feature of clinical blood tests. Other molecules present, such as hormones and drugs, directly affect health by modulating processes such as metabolism and immune responses. Writing in Nature, Bar et al.1 shed light on the factors that affect the recipe for human blood's chemical brew.

The age of pervasive AI is here.1 Since 2017, Deloitte's annual State of AI in the Enterprise report has measured the rapid advancement of AI technology globally and across industries. In the most recent edition, published in July 2020, a majority of those surveyed reported significant increases in AI investments, with more than three-quarters believing that AI will substantially transform their organization in the next three years. In addition, AI investments are increasingly leading to measurable organizational benefits: improved process efficiency, better decision-making, increased worker productivity, and enhanced products and services.2 These possible benefits have likely driven the growth in AI's perceived value to organizations--nearly three-quarters of respondents report that AI is strategically important, an increase of 10 percentage points from the previous survey.

You've probably heard us say this countless times: GPT-3, the gargantuan AI that spews uncannily human-like language, is a marvel. You can tell with a simple trick: Ask it the color of sheep, and it will suggest "black" as often as "white"--reflecting the phrase "black sheep" in our vernacular. That's the problem with language models: because they're only trained on text, they lack common sense. Now researchers from the University of North Carolina, Chapel Hill, have designed a new technique to change that. They call it "vokenization," and it gives language models like GPT-3 the ability to "see."

The partnership between Sensyne Health and Microsoft will entail the aim of improving, augmenting, and reducing the cost of patient care. Expected to deliver latest generation healthcare'cloud-first' systems and predictive machine learning algorithms, while reducing demand on global health systems and increasing scalability of care, the agreement includes the following aspects: Sensyne's AI healthcare expertise will be combined with Micosoft's latest healthcare capabilities. These include clinical workflow and patient engagement tool Health Cloud, digital waiting room and remote consultation service Virtual Consult, and natural language interaction system Health Bot. Richard Farrell, chief innovation officer at Netcall, explores how healthcare can undergo effective digital transformation one step at a time. "This strategic partnership with Microsoft will further enhance Sensyne's ability to advance and scale the benefits that advanced clinical AI can bring to improve patient outcomes and accelerate the development of new medicines through its research partnerships with NHS Trusts," said Lord Paul Drayson, CEO of Sensyne Health.

The Hoover Institution's Condoleezza Rice and HAI Co-Director Fei-Fei Li discuss AI ethics, technology advances, and the...

With the advent of pharmacogenomics, machine learning research is well underway to predict patients' drug response that varies by individual from the algorithms derived from previously collected data on drug responses. Entering high-quality learning data that can reflect a person's drug response as much as possible is the starting point for improving the accuracy of the prediction. Previously, preclinical studies of animal models were used which were relatively easier to obtain compared to human clinical data. In light of this, a research team led by Professor Sanguk Kim in the Department of Life Sciences at POSTECH is drawing attention by successfully increasing the accuracy of anti-cancer drug response predictions by using data closest to a real person's response. The team developed this machine learning technique through algorithms that learn the transcriptome information from artificial organoids derived from actual patients instead of animal models.

Walmart Inc. has ended its effort to use roving robots in store aisles to keep track of its inventory, reversing a yearslong push to automate the task with the hulking machines after finding during the coronavirus pandemic that humans can help get similar results. The retail giant has ended its contract with robotics company Bossa Nova Robotics Inc., with which it joined over the past five years to gradually add six-foot-tall inventory-scanning machines to stores. Walmart had made the robots a frequent topic of conversation at media and investor events in recent years, hoping the technology could help reduce labor costs and increase sales by making sure products are kept in stock. Walmart ended the partnership because it found different, sometimes simpler solutions that proved just as useful, said people familiar with the situation. As more shoppers flock to online delivery and pickup because of Covid-19 concerns, Walmart has more workers walking the aisles frequently to collect online orders, gleaning new data on inventory problems, said some of these people.

Robots have been a mainstay in factories for decades, but their use has been expanding everywhere else, from warehouses and hospitals to retail. That trend continued last year, and the novel coronavirus pandemic has accelerated service robot demand for automated logistics, disinfection, and delivery, according to the International Federation of Robotics. The Frankfurt, Germany-based IFR said that the sales value of professional service robots increased by 32% to $11.2 billion (U.S.) worldwide between 2018 and 2019. The organization published its full research in the "World Robotics 2020 – Service Robots" report, which is available for download. Sales of medical robotics accounted for 47% of the total service robot value turnover in 2019, according to the IFR.

Researchers from Penn State University and Houston Methodist Hospital recently outlined their work on a machine learning tool that uses a smartphone camera to quickly gauge facial movements for sign of a stroke. The tool – which was presented as a virtual poster at this month's International Conference on Medical Image Computing and Computer Assisted Intervention – relies on computational facial motion analysis and natural language processing to spot sagging muscles, slurred speech or other stroke-like symptoms. To build and train it, the researchers used an iPhone to record 80 Houston Methodist patients who were experiencing stroke symptoms as they performed a speech test. According to a Penn State release, the machine learning model performed with 79% accuracy when tested again on that dataset, which the researchers said is roughly on par with emergency room diagnoses using CT scans. "Currently, physicians have to use their past training and experience to determine at what stage a patient should be sent for a CT scan," James Wang, professor of information sciences and technology at Penn State, said in a release from the university.