Body language experts told Dr. Phil Tuesday after the presidential debate that the ABC News moderators clearly favored Vice President Kamala Harris. Dr. Phil spoke with experts Scott Rouse and Greg Hartley in a special post-debate town hall broadcast. Rouse holds multiple certificates in advanced interrogation training and has been trained alongside the FBI, Secret Service, U.S. Military Intelligence, and the Department of Defense. Hartley is a former Army interrogator with expertise in intelligence, business, body language and behavior. When asked whether they saw bias from moderators David Muir and Linsey Davis in Tuesday's debate, Hartley said they were against former President Trump.

New Delhi: Ever since the COVID-19 pandemic began, health organisations such as the WHO have been laying importance on the need for testing. Researchers have suggested that rapid research can, in fact, help bring an end to the pandemic within weeks. However, developing tests with reduced turnaround time has been a challenge for researchers. As the disease progresses and scientists find more ways around it, the most recent development in COVID-19 testing is the use of artificial intelligence – where AI can see, and hear COVID-19 in your lungs. Accoridng to latest reports, Dr Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH) and team, have developed new algorithms to identify patterns of COVID-19 in lung images, and breath sounds, using artificial intelligence.

For Dr Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH), 2020 has been relentless. "It's not a relaxing time to study infectious diseases," she explained. Since the beginning of the COVID-19 pandemic, Dr Hartley's research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become. "We've named the new deep learning algorithms DeepChest – using lung ultrasound images – and DeepBreath – using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising," said Professor Jaggi.

For Dr. Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH), 2020 has been relentless. "It's not a relaxing time to study infectious diseases," she explained. Since the beginning of the COVID-19 pandemic, Dr. Hartley's research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become. "We've named the new deep learning algorithms DeepChest--using lung ultrasound images--and DeepBreath--using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising," said Professor Jaggi.

Convolutional Neural Networks (CNNs) are often described as black boxes due to the difficulty in explaining how they reach their final output for a given task. Consequently a number of techniques have been developed to aid in the process of explainability. These techniques range from the scoring of individual pixels to reflect their impact on the networks decision making, to the scoring of larger regions of the image. Scoring larger regions allows for the results to be more easily interpreted. A popular technique for explaining images is LIME [10]. This uses superpixels, contiguous regions for visualisation, allowing a level of interpretability that may not be present in individual pixel scoring. However, this increased interpretability comes at a cost. The LIME technique relies on perturbing the input image and repeatedly passing it to the network to build an understanding of how important each superpixel region is to the final classification. This requires multiple perturbed images to be passed through the network, by default 1000 in the released code.

MS Amlin has enlisted the help of artificial intelligence (AI) firm Cytora to enhance its commercial underwriting performance and drive premium growth. Announcing the partnership, the global (re)insurer said it will use Cytora's technology to improve processes and power open market underwriting. Cytora, through its Risk Engine API integration with Acturis, will also facilitate better risk selection and pricing in MS Amlin's small- and medium-sized enterprises automated trading book by providing risk scores. "AI technology is transforming the way insurers do business," noted Dr Paul Taffinder, director of strategy & innovation at MS Amlin. "This new and exciting partnership with Cytora is a testament to MS Amlin's commitment to digital innovation and the use of smart technology, further cementing our position as an insurer of the future." Meanwhile Cytora chief executive Richard Hartley, who described MS Amlin as "an ambitious insurer and proven market leader," said its new partner shares the tech company's vision to make insurance more frictionless and data-driven.

Hartley first heard the terms "Fuzzy" and "Techie" while studying political science at Stanford University. At Stanford, if you majored in the humanities or social sciences, you were a Fuzzy. If you majored in the computer sciences, you were a Techie. According to Hartley, this informal division has mistakenly created a business mindset and believes Techies are the real drivers of innovation. Hartley believes that the Fuzzies, not the Techies, are the key talent responsible for creating the most successful new business ideas.

That's because this was the fourth and final meetup for participants of "Program N," a project where volunteers tested a hands-free open-ear audio device (called, appropriately enough, Concept N) for Sony for almost a year. Aside from paying their own money for the hardware, Program N participants were invited to attend several meetups throughout the year, where they interacted with Sony engineers directly. Future Lab also attended the Silicon Valley Bike Festival and Bike To Work Day SF in order to talk to cyclists and bike commuters about how N could improve their experience. "The first version of N didn't have a calling function," said Okamoto, adding that most people in Tokyo don't use headsets to make calls, so it was an afterthought.

With his marriage to Norma Levor over, Claude Shannon was a bachelor again, with no attachments, a small Greenwich Village apartment, and a demanding job. His evenings were mostly his own, and if there's a moment in Shannon's life when he was at his most freewheeling, this was it. He kept odd hours, played music too loud, and relished the New York jazz scene. He went out late for raucous dinners and dropped by the chess clubs in Washington Square Park. He rode the A train up to Harlem to dance the jitterbug and take in shows at the Apollo. He went swimming at a pool in the Village and played tennis at the courts along the Hudson River's edge. Once, he tripped over the tennis net, fell hard, and had to be stitched up. His home, on the third floor of 51 West Eleventh Street, was a small New York studio. "There was a bedroom on the way to the bathroom. It was a boardinghouse ... it was quite romantic," recalled Maria Moulton, the downstairs neighbor. Perhaps somewhat predictably, Shannon's space was a mess: dusty, disorganized, with the guts of a large music player he had taken apart strewn about on the center table. "In the winter it was cold, so he took an old piano he had and chopped it up and put it in the fireplace to get some heat." His fridge was mostly empty, his record player and clarinet among the only prized possessions in the otherwise spartan space. Claude's apartment faced the street. The same apartment building housed Claude Levi-Strauss, the great anthropologist. Later, Levi-Strauss would find that his work was influenced by the work of his former neighbor, though the two rarely interacted while under the same roof. Though the building's live-in super and housekeeper, Freddy, thought Shannon morose and a bit of a loner, Shannon did befriend and date his neighbor Maria. They met when the high volume of his music finally forced her to knock on his door; a friendship, and a romantic relationship, blossomed from her complaint. Maria encouraged him to dress up and hit the town.

Entertainment consulting firm Accenture has been doing its digital consumers survey for about a decade, but its 2017 edition was the first to include questions around artificial intelligence and technology. And the results were surprising to the company. "There's pretty intense interest in AI this year, and a year ago there wasn't anyone talking about it," said Charles Hartley, Accenture's global media and analyst relations manager for communications, media and high-tech businesses. "Consumers aren't intimidated by it at all, apparently. The survey--which interviewed just under 26,000 consumers across 26 countries--found that 62% of people are comfortable with AI apps--like Amazon's Alexa--responding to a voice query, even though only 4% of people actually own a standalone, digital, voice-enabled device (like the Amazon Echo or Google Home) as of the end of 2016. Nearly 90% of respondents said that artificial intelligence simply makes it easier to do things, and a third said they're interested in using voice-enabled digital assistants available in smartphones. Hartley made special note of the 68% who deemed AI "less biased" than humans and the 64% who said AI "communicates more politely." More than half said AI is "less likely to make a mistake." Meanwhile, on Jan. 11, LinkedIn founder Reid Hoffman, the journalism-centric Knight Foundation and others announced they've created a $27 million fund to research AI applications for the public, with MIT's Media Lab and the Berkman Klein Center for Internet & Society at Harvard University serving as academic research stations. The hope is to bring in a wide range of tech and academic voices to the future of AI applications, the groups said in a statement. "Artificial intelligence agents will impact our lives in every society on Earth.