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Queensland police to trial AI tool designed to predict and prevent domestic violence incidents

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Queensland police are preparing to begin trials of an artificial intelligence system to identify high-risk domestic violence offenders, and officers intend to use the data to "knock on doors" before serious escalation. The "actuarial tool" uses data from the police Qprime computer system to develop a risk assessment of all potential domestic and family violence offenders. The algorithm has been in development for about three years and practical trials will begin in some police districts before the end of 2021. "With these perpetrators, we will not wait for a triple-zero phone call and for a domestic and family violence incident to reach the point of crisis," acting Supt Ben Martain said. "Rather, with this cohort of perpetrators, who our predictive analytical tools tell us are most likely to escalate into further DFV offending, we are proactively knocking on doors without any call for service."


La veille de la cybersécurité

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But what if that second opinion could be generated by a computer, using artificial intelligence? Would it come up with better treatment recommendations than your professional proposes? A pair of Canadian mental-health researchers believe it can. In a study published in the Journal of Applied Behavior Analysis, Marc Lanovaz of Université de Montréal and Kieva Hranchuk of St. Lawrence College, in Ontario, make a case for using AI in treating behavioral problems. To find a better way, Lanovaz and Hranchuk, a professor of behavioral science and behavioral psychology at St. Lawrence, compiled simulated data from 1,024 individuals receiving treatment for behavioral issues.


Study: AI can make better clinical decisions than humans

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But what if that second opinion could be generated by a computer, using artificial intelligence? Would it come up with better treatment recommendations than your professional proposes? A pair of Canadian mental-health researchers believe it can. In a study published in the Journal of Applied Behavior Analysis, Marc Lanovaz of Université de Montréal and Kieva Hranchuk of St. Lawrence College, in Ontario, make a case for using AI in treating behavioral problems. "Medical and educational professionals frequently disagree on the effectiveness of behavioral interventions, which may cause people to receive inadequate treatment," said Lanovaz, an associate professor who heads the Applied Behavioral Research Lab at UdeM's School of Psychoeducation.


Rule-Based AI vs. Machine Learning for Development - Which is Best? - AI Trends

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A variation of the problems posed by black-box decision-making is the experience of researchers at Mount Sinai Hospital in New York, in applying a learning system to the hospitals' database of records on some 700,000 individuals. The resulting learning system, called Deep Patient, turned out to be very good at predicting disease. It even appeared to anticipate the onset of psychiatric disorders like schizophrenia, which is difficult for physicians to predict, quite well. "Deep Patient offers no clue as to how it does this," say the authors, referencing Joel Dudley, former leader of the Mount Sinai team, now chief scientific officer at Tempus Labs, which advances precision medicine through the practical application of AI in healthcare.


Here's how new artificial intelligence algorithm can treat sleep disorder - Impact Lab

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In a new study, researchers from the University of Copenhagen's Department of Computer Science have collaborated with the Danish Center for Sleep Medicine at the Danish hospital Rigshospitalet to develop an artificial intelligence algorithm that can improve diagnoses, treatments, and our overall understanding of sleep disorders. Difficulty sleeping, sleep apnea and narcolepsy are among a range of sleep disorders that thousands of Danes suffer from. Furthermore, it is estimated that sleep apnea is undiagnosed in as many as 200,000 Danes. "The algorithm is extraordinarily precise. We completed various tests in which its performance rivalled that of the best doctors in the field, worldwide," states Mathias Perslev, a PhD at the Department of Computer Science and lead author of the study, recently published in the journal npj Digital Medicine (link).


XiaoIce robot users have ended up in therapy for falling in love with their Artificial Intelligence

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The love between a human and a robot is no longer just the plot of a science fiction movie or'Black Mirror' . Futuristic predictions caught up with us and the proof of this is that cases have been reported of users ending up in psychological therapy for falling in love with the robot XiaoIce, the most popular virtual assistant with Artificial Intelligence (AI) in China. XiaoIce is an advanced AI system, designed as a chatbot to create emotional bonds with its users, and is found on most Chinese smartphones and social platforms. Today, XiaoIce has 150 million users in China alone, and 660 million worldwide. According to Li Di, founder and CEO of the firm, it currently attends about 60% of global interactions between humans and AI, placing it among the top virtual assistants in the market, according to statements to AFP.


'Psychonauts 2' is a Saturday morning cartoon with a side of mental health

Washington Post - Technology News

The game does a consistent job of homing in on small moments and recalling earlier details from the franchise and expanding upon them, adding a sense of heft and solidity to this fictional world. Early on, for example, a henchman utters an obscure riddle in the 2021 sequel; by the game's closing act, we learn what the riddle means, even if it's a small detail that some may miss. There's even a nod to the first game, when Coach Oleander, one of the original villains who has since been reformed, asks if "Psychonauts 2′s" Big Bad should be allowed to return to a normal life; the other Psychonauts give him a pointed look before he relents.


Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach

arXiv.org Artificial Intelligence

Social media such as Instagram and Twitter have become important platforms for marketing and selling illicit drugs. Detection of online illicit drug trafficking has become critical to combat the online trade of illicit drugs. However, the legal status often varies spatially and temporally; even for the same drug, federal and state legislation can have different regulations about its legality. Meanwhile, more drug trafficking events are disguised as a novel form of advertising commenting leading to information heterogeneity. Accordingly, accurate detection of illicit drug trafficking events (IDTEs) from social media has become even more challenging. In this work, we conduct the first systematic study on fine-grained detection of IDTEs on Instagram. We propose to take a deep multimodal multilabel learning (DMML) approach to detect IDTEs and demonstrate its effectiveness on a newly constructed dataset called multimodal IDTE(MM-IDTE). Specifically, our model takes text and image data as the input and combines multimodal information to predict multiple labels of illicit drugs. Inspired by the success of BERT, we have developed a self-supervised multimodal bidirectional transformer by jointly fine-tuning pretrained text and image encoders. We have constructed a large-scale dataset MM-IDTE with manually annotated multiple drug labels to support fine-grained detection of illicit drugs. Extensive experimental results on the MM-IDTE dataset show that the proposed DMML methodology can accurately detect IDTEs even in the presence of special characters and style changes attempting to evade detection.


Twitter User Representation using Weakly Supervised Graph Embedding

arXiv.org Artificial Intelligence

Social media platforms provide convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, this rapidly growing access has also increased the diverse information, and characterizing user types to understand people's lifestyle decisions shared in social media is challenging. In this paper, we propose a weakly supervised graph embedding based framework for understanding user types. We evaluate the user embedding learned using weak supervision over well-being related tweets from Twitter, focusing on 'Yoga', 'Keto diet'. Experiments on real-world datasets demonstrate that the proposed framework outperforms the baselines for detecting user types. Finally, we illustrate data analysis on different types of users (e.g., practitioner vs. promotional) from our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our method for constructing user representation readily generalizes to other domains.


Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

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Objectives Psychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists. Design Cross-sectional study. Setting We conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists. Participants An AI model of the neural network and six psychiatrists. Primary outcome The accuracies of the AI model and psychiatrists for predicting psychological distress. Methods In total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model. Results The accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy. Conclusions A machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views. No data are available.