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IDTechEx: the role of emerging tech in fighting COVID-19

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Details on the roles that emerging technologies have played in the face of the COVID-19 pandemic throughout 2020 have been released by IdTechEx. With results needed at an unprecedented scale in a limited time, diagnostic approaches were explored to quickly diagnose COVID-19 patients. The lungs of patients with COVID-19 have certain visual hallmarks such as ground-glass opacities and areas of increased density, both of which can be detected using CT and x-ray imaging. To further speed up this process, companies developing artificial intelligence solutions for the detection of respiratory diseases quickly tailored their software to differentiate COVID-19 from other respiratory infections, decreasing image analysis time to the matter of seconds. In order to prepare for the oncoming surge in COVID-19 patients and to prevent the spread of COVID-19 between patients in healthcare settings, healthcare systems around the world halted provision of "non-urgent" doctor visits, which extends to everything from cardiac patients to cancer treatments. This, combined with the public's concern and confusion around COVID-19 diagnosis, caused a huge increase in demand for digital health services.


The Potential Benefits of Integrating AI with LIMS

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The incorporation of AI into LIMS will equip laboratories with enhanced capabilities, enabling better data retrieval and laboratory diagnostics. FREMONT, CA: The emergence of the laboratory information management system (LIMS) has transformed the pharmaceutical industry, enabling organizations to process numerous lab samples and streamline the laboratory workflow. It has bolstered the capabilities of laboratories with robust decision support and decision making. The integration of artificial intelligence (AI) with LIMS will enable organizations to resolve many of the challenges in the industry. A vast amount of data is gathered from the multiple tasks performed on a daily basis in clinical laboratories. The data has to be stored and recorded in an orderly manner to track useful information.


Salesforce-backed AI project SharkEye aims to protect beachgoers

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Salesforce is backing an AI project called SharkEye which aims to save the lives of beachgoers from one of the sea's deadliest predators. Shark attacks are, fortunately, quite rare. However, they do happen and most cases are either fatal or cause life-changing injuries. Just last week, a fatal shark attack in Australia marked the eighth of the year--an almost 100-year record for the highest annual death toll. Once rare sightings in Southern California beaches are now becoming increasingly common as sharks are preferring the warmer waters close to shore.


Can machines replace human workers? Ben-Gurion weighs in

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This article is the second in a series on David Ben-Gurion's exchanges with Prof. Amos de-Shalit. A year and a half passed "quietly" since David Ben-Gurion and Prof. Amos de-Shalit last corresponded. During this time no letters or ideas were exchanged between the two. Nevertheless, the subject seems to have continued to preoccupy Ben-Gurion's thoughts, to the point where he began to read scientific articles by renowned physicists on related subjects. On June 10, 1959, Ben-Gurion decided to break his silence and sent de-Shalit a short and to-the-point letter.


Face recognition isn't just for humans -- it's learning to identify bears and cows, too

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San Francisco (CNN Business)It's hard for the average person to tell Dani, Lenore, and Bella apart: They all sport fashionably fuzzy brown coats and enjoy a lot of the same activities, like playing in icy-cold water and, occasionally, ripping apart a freshly caught fish. Melanie Clapham is not the average person. As a bear biologist, she has spent over a decade studying these grizzly bears, who live in Knight Inlet in British Columbia, Canada, and developed a sense for who is who by paying attention to little things that make them different. "I use individual characteristics -- say, one bear has a nick in its ear or a scar on the nose," she said. But Clapham knows most people don't have her eye for detail, and the bears' appearances change dramatically over the course of a year -- such as when they get winter coats and fatten up before denning -- which makes it even harder to distinguish between, say, Toffee and Blonde Teddy.


To do in 2021: Get up to speed with quantum computing 101

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If "figure out quantum computing" is still in your future file, it's time to update your timeline. The industry is nearing the end of the early adopter phase, according to one expert, and the time is now to get up to speed. Denise Ruffner, the vice president of business development at IonQ, said that quantum computing is evolving much faster than many people realize. "When I started five years ago, everyone said quantum computing was five to 10 years away and every year after that I've heard the same thing," she said. "But four million quantum volume was not on the radar then and you can't say it's still 10 years away any more."


AI and pharma

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The COVID-19 pandemic has increased the focus on the use of artificial intelligence (AI) across the life sciences organization, from R&D to manufacturing, supply chain, and commercial functions. During the pandemic, company leadership and management realized that they could run many aspects of their business remotely and with digital solutions. This experience has transformed mindsets; leaders are more likely to lean into a future that lies in digital investments, data, and AI because of this experience. At present, the life sciences industry has only begun to scratch the surface of AI's potential, primarily applying it to automate existing processes. By melding AI with rigorous medical and scientific knowledge, companies can do even more to leverage this technology to transform processes and achieve a competitive edge. AI has the potential to identify and validate genetic targets for drug development, design novel compounds, expedite drug development, make supply chains smarter and more responsive, and help launch and market products. We will highlight a number of these use cases in this report.


Airbnb Data Exploration with K-Means Clustering from Scratch

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Before we dive into the Airbnb dataset and our findings, let's do an in-depth review of the K-Means clustering algorithm. An unsupervised learner receives unlabeled training data and makes predictions for unseen points. Clustering analysis falls under unsupervised learning. A cluster is a collection of data objects that are similar (or related) to one another within the same group, or dissimilar (or unrelated) to the objects in other groups. "Good" clusters will have the following: high intra-class similarity (cohesiveness within clusters) and low inter-class similarity (distinctive between clusters). The K-Means algorithm is common a type of clustering analysis.


Need a Hypothesis? This A.I. Has One

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Machine-learning algorithms seem to have insinuated their way into every human activity short of toenail clipping and dog washing, although the tech giants may have solutions in the works for both. If Alexa knows anything about such projects, she's not saying. But one thing that algorithms presumably cannot do, besides feel heartbreak, is formulate theories to explain human behavior or account for the varying blend of motives behind it. They are computer systems; they can't play Sigmund Freud or Carl Jung, at least not convincingly. Social scientists have used the algorithms as tools, to number-crunch and test-drive ideas, and potentially predict behaviors -- like how people will vote or who is likely to engage in self-harm -- secure in the knowledge that ultimately humans are the ones who sit in the big-thinking chair.


AI Is Replacing Human Jobs: Low-Skilled And Manual

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The concept of self-driving cars and the pace at which they're base is enough to cover this one. Tesla is the most popular one but other companies like Uber, despite the problems, are making success in this niche. As a result of their innovation, semi-automated cars are already here and it won't take long for fully-autonomous vehicles to take over transportation and taxis. Although the government bans self-driving cars for safety issues and to protect jobs in some countries, these issues will not be there soon. Nonetheless, pilots need not be cautious for now–while there do exist self-flying software, travelers will need time to fly by a machine.