If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The news spread quickly when Dr. Lorna Breene, medical director of the emergency department at NewYork-Presbyterian Allen Hospital, died by suicide last month. Dr. Breene had been on the frontlines of the coronavirus pandemic and had contracted COVID-19. She'd recovered enough to return to work before being sent home by the hospital to recuperate. She took her own life while staying with family in Virginia. Dr. Breene's father said she'd described an "onslaught of patients who were dying before they could even be taken out of ambulances," according to the New York Times.
Much has been written in the past few weeks about the COVID-19 crisis and the ripple effects that will impact human society. Beyond the immediate effect of the virus on health and mortality, it is clear that we are also facing a global, massive financial crisis that is likely to affect our lives for years to come. These changes, along with the expected prolonged social isolation, are bound to have a devastating effect on our mental health, collectively and individually, and, in turn, cause a dramatic deterioration in overall health and an increase in the prevalence of chronic illness. From research conducted by the World Health Organization, we know that most people affected by emergency situations experience immediate psychological distress, hopelessness and sleep issues -- and that 22% of people are expected to develop depression, anxiety, post-traumatic stress disorder, bipolar disorder or schizophrenia. This escalation comes on top of a baseline of 19.1% of U.S. adults experiencing mental illness (47.6 million people in 2018, according to the Substance Abuse and Mental Health Services Administration).
It is a known fact that deep learning models get better with diversity in the data they are fed with. For instance, data in a use case related to healthcare data will be taken from several providers such as patient data, history, workflows of professionals, insurance providers, etc. to ensure such data diversity. These data points that are collected through various interactions of people are fed into a machine learning model, which sits remotely in a data haven spewing predictions without exhausting. However, consider a scenario where one of the providers ceases to offer data to the healthcare project and later requests to delete the provided information. In such a case, does the model remember or forget its learnings from this data?
A globally renowned expert in artificial intelligence (AI) from the University of Huddersfield has produced innovative research to show how technology can be used to support the diagnosis of ADHD in adults. Professor Grigoris Antoniou, the project lead from the university, said the work started after the NHS wanted to speed up diagnosis as currently treatments are available, but the process can be slow. "There are long and growing waiting lists, as people wait to be diagnosed and treated, and this can result in adverse effects on their work, their social life and their family life," said Professor Antoniou. He added a reason for the lengthening waiting time due to a limited number of specialist clinicians able to do a full diagnosis. It has been estimated that 1.5 million UK adults have ADHD, leading to a wide range of difficulties, jeopardising careers and relationships.
If you want to impress your children with your mental prowess, you might want to give escape rooms a miss and pull out the scrabble board instead. Twentysomethings may have the sharpest minds -- but over-70s have a superior way with words, the Great British Intelligence Test has revealed. The BBC's online test -- developed in tandem with neuroscientists from Imperial College London -- has been taken by more than 250,000 people from across the UK. Researchers found that our ability to solve problems appears to peak in our twenties -- and then declines steadily as we get older. As a result, the experts say that forty-year-old adults have the same problem solving capacities as their twelve-year-old children.
Getting diagnosed with a sleep disorder or assessing quality of sleep is an often expensive and tricky proposition, involving sleep clinics where patients are hooked up to sensors and wires for monitoring. Wearable devices, such as the Fitbit and Apple Watch, offer less intrusive and more cost-effective sleeping monitoring, but the tradeoff can be inaccurate or imprecise sleep data. Researchers at the Georgia Institute of Technology are working to combine the accuracy of sleep clinics with the convenience of wearable computing by developing machine learning models, or smart algorithms, that provide better sleep measurement data as well as considerably faster, more energy-efficient software. The team is focusing on electrical ambient noise that is emitted by devices but that is often not audible and can interfere with sleep sensors on a wearable gadget. Leave the TV on at night, and the electrical signal – not the infomercial in the background – might mess with your sleep tracker.
Behavioural inhibition and shyness at infancy leads to a reserved, introverted personality by the time a person reaches their mid-twenties, new research shows. US neuroscientists found that infants with'behavioural inhibition' grew up to be reserved and have fewer human interactions aged 26. Individuals who showed sensitivity to making errors at the age of 15, meanwhile, later had a higher risk for internalizing anxiety and depression. The quarter-century-long experiment is evidence of the long-lasting impact of our internal processes at a young age, despite physical changes and years of life experience. 'While many studies link early childhood behaviour to risk for psychopathology, the findings in our study are unique,' said Daniel Pine, study author and chief of the National Institute of Mental Health Section on Development and Affective Neuroscience.
Around one in five children suffer from anxiety and depression, collectively known as "internalizing disorders." But because children under the age of eight can't reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment. "We need quick, objective tests to catch kids when they are suffering," says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center's Vermont Center for Children, Youth and Families and lead author of the study. "The majority of kids under eight are undiagnosed."
Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of "The XL-Files," a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990.