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AI is watching: What to know about workplace surveillance

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BRUSSELS, June 23 (Thomson Reuters Foundation) – From Swedish retailer H&M being fined 35 million euros ($42 million) for recording employees' private data to Britain's Barclays bank accused of spying on its staff, workplace surveillance has come into the spotlight in recent months. On Wednesday, the European Trade Union Institute (ETUI), the European Trade Union Confederation's research arm, said planned regulation by the European Union (EU) to improve privacy does not do enough to stop companies from snooping on their workers in the name of security and efficiency. As artificial intelligence (AI) technology becomes ever more accessible and sophisticated, here's why unions are worried: What kind of surveillance are we talking about? Employee monitoring today can involve software programmes for live monitoring, streaming and recording more than a dozen employees' computer screens at a time. Keystrokes, chat programmes, instant messaging and Skype dialogues may also be monitored and recorded in real time.


AI is watching: What to know about workplace surveillance

#artificialintelligence

FROM Swedish retailer H&M being fined 35 million euros ($42 million) for recording employees' private data to Britain's Barclays bank accused of spying on its staff, workplace surveillance has come into the spotlight in recent months. On Wednesday, the European Trade Union Institute (ETUI), the European Trade Union Confederation's research arm, said planned regulation by the European Union (EU) to improve privacy does not do enough to stop companies from snooping on their workers in the name of security and efficiency. As artificial intelligence (AI) technology becomes ever more accessible and sophisticated, here's why unions are worried: What kind of surveillance are we talking about? Employee monitoring today can involve software programmes for live monitoring, streaming and recording more than a dozen employees' computer screens at a time. Keystrokes, chat programmes, instant messaging and Skype dialogues may also be monitored and recorded in real time.


Is artificial intelligence the key to preventing relapse of severe mental illness?

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New AI software developed by researchers at Flinders University shows promise for enabling timely support ahead of relapse in patients with severe mental illness. The AI2 (Actionable Intime Insights) software, developed by a team of digital health researchers at Flinders University, has undergone an eight-month trial with psychiatric patients from the Inner North Community Health Service, located in Gawler, South Australia. The digital tool is tipped to revolutionise consumer-centric timely mental health treatment provision outside hospital, with researchers labelling it as readily available and scalable. In the trial of 304 patients, the AI2 software found that 10% of them were at increased risk of not adhering to treatment plans by failing to take medication or disengaging with health services. This led to interventions which clinicians believe could have prevented the patient from relapsing and experiencing a deterioration of their mental health.


Fighting breast cancer with artificial intelligence (FCL June 15, 2021)

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Mammograms have decreased since the pandemic, due to COVID restrictions, which means fewer people had the chance for early detection. Thankfully, artificial intelligence is helping, not just with early detection but better accuracy to reduce false positives.


Introducing Dosis - the AI powered dosing platform

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Cloud-based platform Dosis uses AI to help patients and clinicians tailor their medication plans. Shivrat Chhabra, CEO and co-founder, tells us how it works. When and why was Dosis founded? Divya, my co-founder and I founded Dosis in 2017 with the purpose of creating a personalised dosing platform. We see personalisation in so many aspects of our lives, but not in the amount of medication we receive.


Unsupervised machine learning application in ACL

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De-Sheng Chen,* Tong-Fu Wang,* Jia-Wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People's Republic of China *These authors contributed equally to this work Correspondence: Jia-Wang Zhu Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People's Republic of China Email [email protected] Purpose: We aim to present an unsupervised machine learning application in anterior cruciate ligament (ACL) rupture and evaluate whether supervised machine learning-derived radiomics features enable prediction of ACL rupture accurately. Patients and Methods: Sixty-eight patients were reviewed. Their demographic features were recorded, radiomics features were extracted, and the input dataset was defined as a collection of demographic features and radiomics features. The input dataset was automatically classified by the unsupervised machine learning algorithm. Then, we used a supervised machine learning algorithm to construct a radiomics model.


New center for AI, machine-learning research dedicated at IU

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Now, AI at IU has a home. IU President Michael A. McRobbie dedicated the $35 million Luddy Center for Artificial Intelligence, a 58,000-square-foot facility that will serve as the hub for multidisciplinary research in advanced AI and machine-learning applications, during a ceremony June 23 at Luddy Hall. "As we dedicate the Luddy Center for Artificial Intelligence, it is fair to say that we are also celebrating what will be a game-changing development for Indiana University," McRobbie said. "Indiana University has been a center of research in a number of areas of AI for many years. Artificial intelligence has long been an area of strength of the Department of Computer Science and, more broadly, IU faculty in the cognitive sciences, psychological sciences and neurosciences have also long been engaged in areas of research relevant to AI. The explosion worldwide of the uses and applications of AI, building on decades of steady research progress, made this the perfect time for IU to establish a major holistic initiative in artificial intelligence."


AI Edtech Entrepreneur's Journey from Neuroscience to Toys

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Dr. Dhonam Pemba is the CEO and Co-Founder of KidX, he is a neural engineer by education, a former rocket scientist by work, and AI entrepeneur by entrepeneurship. He received his Biomedical Engineering undergraduate degree from Johns Hopkins University, and hi PhD from the University of California, Irvine also in BME, but worked on neural interface for his thesis. Can you me about the NASA JPL project and how it was related to your PhD work? My PhD work was building micro implantable neural implants. Very similar to the work that Elon Musks's company Neuralink is now doing.


DeepMind wants to use its AI to cure neglected diseases

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In November 2020, Alphabet-owned AI firm DeepMind announced that it had cracked one of biology's trickiest problems. For years the company had been working on an AI called AlphaFold that could predict the structure of proteins – a challenge that could prove pivotal for developing drugs and vaccines, and understanding diseases. When the results of the biennial protein-predicting challenge CASP were announced at the end of 2020, it was immediately clear that AlphaFold had swept the floor with the competition. John Moult, a computational biologist at the University of Maryland who co-founded the CASP competition, was both astonished and excited at AlphaFold's potential. "It was the first time a serious scientific problem had been solved by AI," he says.


Using AI to track cognitive deviation in aging brains

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Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a study published in Radiology: Artificial Intelligence. The model has the potential to aid in early detection of cognitive impairment at an individual level. Amnestic mild cognitive impairment (aMCI) is a transition phase from normal aging to Alzheimer's disease (AD). People with aMCI have memory deficits that are more serious than normal for their age and education, but not severe enough to affect daily function. For the study, Ni Shu, Ph.D., from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, in Beijing, China, and colleagues used a machine learning approach to train a brain age prediction model based on the T1-weighted MR images of 974 healthy adults aged from 49.3 to 95.4 years.