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AI can detect how lonely you are by analysing your speech

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Artificial intelligence (AI) can detect loneliness with 94 per cent accuracy from a person's speech, a new scientific paper reports. Researchers in the US used several AI tools, including IBM Watson, to analyse transcripts of older adults interviewed about feelings of loneliness. By analysing words, phrases, and gaps of silence during the interviews, the AI assessed loneliness symptoms nearly as accurately as self-reports of loneliness and questionnaires completed by the participants themselves, which can be biased. The AI also revealed that lonely individuals tend to have longer responses to direct questions about loneliness, and express more sadness in their answers. 'Most studies use either a direct question of "how often do you feel lonely", which can lead to biased responses due to stigma associated with loneliness,' said senior author Ellen Lee at UC San Diego (UCSD) School of Medicine.


Using AI with Explainable Deep Learning To Help Save Lives

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The Covid-19 tragedy and crisis has placed the spotlight on the healthcare systems around the world and placed an additional strain on systems that in many cases were already under stress to meet demand and led to a growth in digital medicine. A video from the BBC observers that Covid-19 brings remote medicine revolution to the UK "Apps which allow doctors to connect with patients remotely have been available for a while, but the coronavirus pandemic has seen doctors finding new ways to consult with critical patient care, including reviewing scans and X-rays from home." McKinsey in an article relating to the US healthcare situation and entitled "Preparing for the next normal now: How health systems can adopt a growth transformation in the COVID-19 world" state that "Covid-19 unprecedented impact on health, economies, and daily life has created a humanitarian crisis. Health systems have been at the epicenter of the fight against COVID-19, and have had to balance the need to alleviate suffering and save lives with substantial financial pressures." "Health systems' income statements are likely to see negative pressure as a result of the COVID-19 crisis. While health systems have ramped up capacity to handle COVID-19 cases and incurred additional costs to procure personal protective equipment and operationalize surge capacity plans, they also have had declines of up to 70 percent in surgical volume and 60 percent in emergency department traffic. In a recent McKinsey survey of health system CFOs, more than 90 percent of respondents reported that COVID-19 will have a negative financial impact, even after accounting for federal and state funding."


AI can detect Covid-19 in lungs like virtual physician: Study - Telugu Bullet

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Researchers have demonstrated that an artificial intelligence (AI) algorithm could be trained to classify Covid-19 pneumonia in computed tomography (CT) scans with up to 90 per cent accuracy. Also, it correctly identifies positive cases 84 per cent of the time and negative cases 93 per cent of the time. The study, recently published in Nature Communications, shows the new technique can also overcome some of the challenges of current testing. "We demonstrated that a deep learning-based AI approach can serve as a standardized and objective tool to assist healthcare systems as well as patients," said study author Ulas Bagci from the University of Central Florida in the US. "It can be used as a complementary test tool in very specific limited populations, and it can be used rapidly and at large scale in the unfortunate event of a recurrent outbreak," Bagci added.


Data Science Role and Environment at Microsoft

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After being named sexiest job of the 21st Century" by Harvard Business Review, data science has blended the enthusiasm of the overall population. Numerous individuals are fascinated by the job and can't help thinking about how they themselves can become data scientists. There are endless tools, courses, and applications for people to learn data science, however, let's be honest: for somebody new to the field, every one of these options can appear to be a jungle of complex information. Microsoft has been a major player in the data science industry after Azure and it's machine learning tools have been gradually ruling as the biggest service provider in the cloud-computing market. Therefore, Microsoft has been working out its data science team gradually in recent years to get one of the greatest companies hiring for data scientists.


Who Should Get The Credit For AI-Generated Artworks

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"Anthropomorphising AI systems can undermine our ability to hold powerful individuals and groups accountable." Edmond De Belamy, a portrait generated by a machine learning (ML) algorithm was sold at Christie's art auction for $432,500; 40 times higher than the initial estimate of $10,000. The whole event was marketed by Christie's as ''the first portrait generated by an algorithm to come up for auction''. AI for art, especially generative adversarial networks (GANs) have come a long way since then. Christie's affair makes one wonder how good has AI become, but if one looks closely they might think otherwise.


Adversarial generation of extreme samples

AIHub

Modelling extreme events in order to evaluate and mitigate their risk is a fundamental goal in many areas, including extreme weather events, financial crashes, and unexpectedly high demand for online services. In order to mitigate such risk it is vital to be able to generate a wide range of extreme, and realistic, scenarios. Researchers from the National University of Singapore and IIT Bombay have developed an approach to do just that. In work recently posted on arXiv Siddharth Bhatia, Arjit Jain, and Bryan Hooi, note that in many applications, stress-testing is an important tool. This typically involves testing a system on a wide range of extreme but realistic scenarios to check that the system can cope in such situations.


Deep Learning to Diagnose Dystonia in Milliseconds

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Mass Eye and Ear researchers have discovered a unique diagnostic tool that can detect dystonia from MRI scans. It is the first technology of its kind to provide an objective diagnosis of the disorder. Dystonia is a potentially disabling neurological condition which causes involuntary muscle contractions, driving to abnormal movements and postures. It is often mistreated and sometimes takes people up to 10 years to get a correct diagnosis. A new study by PNAS researches shows that they have developed an AI-based deep learning platform on September 28, called DystoniaNet to compare brain MRIs of 612 people.


Artificial intelligence called GPT-3 can write like a human but don't mistake that for thinking

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Since it was unveiled earlier this year, the new AI-based language generating software GPT-3 has attracted much attention for its ability to produce passages of writing that are convincingly human-like. Some have even suggested that the program, created by Elon Musk's OpenAI, may be considered or appears to exhibit, something like artificial general intelligence (AGI), the ability to understand or perform any task a human can. This breathless coverage reveals a natural yet aberrant collusion in people's minds between the appearance of language and the capacity to think. Language and thought, though obviously not the same, are strongly and intimately related. And some people tend to assume that language is the ultimate sign of thought.


As AI chips improve, is TOPS the best way to measure their power?

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Once in a while, a young company will claim it has more experience than would be logical -- a just-opened law firm might tout 60 years of legal experience, but actually consist of three people who have each practiced law for 20 years. The number "60" catches your eye and summarizes something, yet might leave you wondering whether to prefer one lawyer with 60 years of experience. There's actually no universally correct answer; your choice should be based on the type of services you're looking for. A single lawyer might be superb at certain tasks and not great at others, while three lawyers with solid experience could canvas a wider collection of subjects. If you understand that example, you also understand the challenge of evaluating AI chip performance using "TOPS," a metric that means trillions of operations per second, or "tera operations per second."


How Machine Learning Models Fail in the Real World

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This article was published as a part of the Data Science Blogathon. Yesterday, my brother broke an antique at home. I began to search for FeviQuick (a classic glue) to put it back together. Given that it's one of the most misplaced items, I began to search for it in every possible drawer and every untouched corner of the house I hadn't been to in the past 3 months. I gave up the search after an hour – the FeviQuick was nowhere to be found.