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) …
Without a doubt, the biggest questions about dreaming are all variants on this question: Why do we dream? We began studying dreaming in the early 1990s and, between the two of us, have published over 200 scientific papers on sleep and dreams. Pulling together a variety of compelling neuroscientific ideas and state-of-the-art findings in the fields of sleep and dream research, we propose a new and innovative model of why we dream. We call this model NEXTUP. It proposes that our dreams allow us to explore the brain's neural network connections in order to understand possibilities.
Scientists from the Max Planck Institute of Psychiatry, led by Nikolaos Koutsouleris, combined psychiatric assessments with machine-learning models that analyze clinical and biological data. Although psychiatrists make very accurate predictions about positive disease outcomes, they might underestimate the frequency of adverse cases that lead to relapses. The algorithmic pattern recognition helps physicians to better predict the course of disease. The results of the study show that it is the combination of artificial and human intelligence that optimizes the prediction of mental illness. "This algorithm enables us to improve the prevention of psychosis, especially in young patients at high risk or with emerging depression, and to intervene in a more targeted and well-timed manner" explains Koutsouleris.
Cheating on your diet could lead to weight gain, but if you follow the Mediterranean diet and switch to unhealthy foods you may also make your brain age faster. A team from Rush University Medical Center found that adding in foods from the Western diet, such as pizza, sweets and processed meats, reverse cognitive benefits from the Mediterranean diet. The study examined more than 5,000 individuals over the age of 65 from 1993 to 2021 and over the course of three years participants were asked to complete cognitive tests and report on how often the ate certain foods. Researcher recently compiled the data and found those who stuck to the Mediterranean diet had brains that were nearly six years younger than their peers on the Western diet. The Mediterranean diet is inspired by the eating habits of Spain, Italy and Greece, and focuses on consuming more fruit and fish and limiting sugar, dairy and processed foods.
Among the things I have not missed since entering middle age is the sensation of being an absolute beginner. It has been decades since I've sat in a classroom in a gathering cloud of incomprehension (Algebra 2, tenth grade) or sincerely tried, lesson after lesson, to acquire a skill that was clearly not destined to play a large role in my life (modern dance, twelfth grade). Learning to ride a bicycle in my early thirties was an exception--a little mortifying when my husband had to run alongside the bike, as you would with a child--but ultimately rewarding. Less so was the time when a group of Japanese schoolchildren tried to teach me origami at a public event where I was the guest of honor--I'll never forget their sombre puzzlement as my clumsy fingers mutilated yet another paper crane. Like Tom Vanderbilt, a journalist and the author of "Beginners: The Joy and Transformative Power of Lifelong Learning" (Knopf), I learn new facts all the time but new skills seldom.
Giving yourself verbal or mental instructions to clear your mind can help make room in your brain for more productive thoughts, a new study reveals. US researchers used brain imaging scans and machine learning to investigate what happens to the brain when we try to stop thinking about something. Three instructions – to'clear' our mind, 'suppress' a thought and'replace' a thought with something else – all successfully removed and manipulated unwanted information in the'working memory'. The working memory is the mental'notepad' that contains fleeting thoughts and is responsible for the temporary holding and processing of information. Holding information in the working memory is essential for cognition, but removing unwanted thoughts is equally important, researchers say.
The influx of hedonic online services (including video streaming, social media, video games) has created rather fierce competition for people's attention, in what is termed the "attention economy--in which every minute of attention and engagement tech companies can "squeeze" out of users counts. To compete in this environment, tech companies, intentionally or unintentionally, have adapted practices that have capitalized on varying features of human decision making and brain physiology to cultivate automatic, and uninterrupted use.4 There is a body of evidence--growing yet debated--suggesting that when some technologies are used excessively, the use can interfere with normal functioning, such as with sleep, physical activity, and school performance.12 What's more, populations such as children and adolescents may be susceptible to excessive use,2 although age related prevalence issues have not always been made clear. We say the evidence is debated because some studies suggest that excessive use may be related to prior mental illness rather than to the technology itself.6
Good mental health is important, and early research suggests AI might help diagnose that health for people who are under a particularly heavy strain. Scientists have developed a combination of deep learning algorithms that use eye tracking to gauge the mental health of cancer patients after surgery. Ideally, this would help spot patients likely to be suffering from anxiety or depression when a human can't perform an initial psychological assessment. The system uses a mix of a convolutional neural network and long short-term memory algorithms to study the eye movements of patients wearing tracking glasses (in this case, Tobii Pro 2 glasses) while they contemplate artwork. The AI used the gaze and pupil position data from those glasses to determine how likely someone was to raise concerns on established hope, anxiety and mental wellbeing questionnaires they would fill out later.
A large number of individuals are suffering from suicidal ideation in the world. There are a number of causes behind why an individual might suffer from suicidal ideation. As the most popular platform for self-expression, emotion release, and personal interaction, individuals may exhibit a number of symptoms of suicidal ideation on social media. Nevertheless, challenges from both data and knowledge aspects remain as obstacles, constraining the social media-based detection performance. Data implicitness and sparsity make it difficult to discover the inner true intentions of individuals based on their posts. Inspired by psychological studies, we build and unify a high-level suicide-oriented knowledge graph with deep neural networks for suicidal ideation detection on social media. We further design a two-layered attention mechanism to explicitly reason and establish key risk factors to individual's suicidal ideation. The performance study on microblog and Reddit shows that: 1) with the constructed personal knowledge graph, the social media-based suicidal ideation detection can achieve over 93% accuracy; and 2) among the six categories of personal factors, post, personality, and experience are the top-3 key indicators. Under these categories, posted text, stress level, stress duration, posted image, and ruminant thinking contribute to one's suicidal ideation detection.
Using Social Agents as health-care assistants or trainers is one focus area of IVA research. While their use as physical health-care agents is well established, their employment in the field of psychotherapeutic care comes with daunting challenges. This paper presents our mobile Social Agent EmmA in the role of a vocational reintegration assistant for burn-out outpatient treatment. We follow a typical participatory design approach including experts and patients in order to address requirements from both sides. Since the success of such treatments is related to a patients emotion regulation capabilities, we employ a real-time social signal interpretation together with a computational simulation of emotion regulation that influences the agent's social behavior as well as the situational selection of verbal treatment strategies. Overall, our interdisciplinary approach enables a novel integrative concept for Social Agents as assistants for burn-out patients.
It's easy to do bad things with Facebook data. From targeting ads for bizarrely specific T-shirts to manipulating an electorate, the questionable purposes to which the social media behemoth can be put are numerous. But there are also some people out there trying to use Facebook for good--or, at least, to improve the diagnosis of mental illness. On December 3, a group of researchers reported that they had managed to predict psychiatric diagnoses with Facebook data--using messages sent up to 18 months before a user received an official diagnosis. The team worked with 223 volunteers, who all gave the researchers access to their personal Facebook messages.