A report by the World Health Organisation (WHO) states that 7.5% of the Indian population suffers from some type of mental disorder. There is a variety of psychiatric conditions, including chronic depression, schizophrenia, psychosis, bipolar, personality disorders like attention-deficit hyperactivity disorder, obsessive-compulsive disorder and many more. Considering that one person dies from suicide every 40 seconds, researchers are working on finding ways to minimise the traumatic impact of mental ailments. On World Mental Health Day, we take a look at how what role AI can play in diagnosing and managing psychiatric issues, along with the challenges that exist in the broader adoption of the technology.
A ground-breaking study has revealed that members of the great apes, such as bonobos, chimps and orangutans, have a theory of mind. This, researchers say, proves they can understand others' mental states -- an ability previously though exclusively reserved to humans. The idea other animals possess this trait has been debated for decades and researchers at Kyoto University think they have proved its existence. A ground-breaking study has revealed that members of the great apes, such as bonobos, chimps and orangutans, have a theory of mind. This, researchers say, proves they can understand others' mental states Theory of mind is a higher cognitive function which allows individuals to understand others' mental states.
Education through machines is a reality. From industry to business and to education now, it is time to lose our reservations and embrace artificial intelligence. Teachers can see the limitations that technology can't or doesn't observe. However, there is no scalable way for students to closely engage with teachers as class sizes grow. This leads to gaps of knowledge between the lessons teachers submit to their administrators and the principles students are able to absorb.
For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.
Treatment plans for troubled teens are highly specialized. Designed to restore young people struggling with issues such as: anger, oppositional defiant disorder (ODD), attention deficit disorder (ADD), substance abuse, depression, grief and loss, adoption, obsessive compulsive disorder (OCD), eating disorders, self-harm, or rebellion, just to name a few. Located in Idaho, near the Sawtooth Mountains, we serve families from all over the U.S. Most of our parents and clients can come from California, Texas, Washington, Oregon, Illinois, Idaho, Colorado, Nevada, Georgia, Florida, New York, Connecticut, Massachusetts, Maine, and Tennessee just to list a few. Several families also come to us from overseas.
It shouldn't really be a surprise that artificial intelligence (AI) gets a special mention in the long-term plan for the NHS, published in March. AI is seen as important for the future of the NHS because it can make healthcare more effective and efficient, leaving staff free to focus on, as the plan puts it, the'complexity of human interactions that technology will never master'. With a growing population, limited resources yet more and more treatments available, the use of intelligent technology will be key to ensuring our healthcare services can keep pace. At Grow MedTech, we see AI as one of the most important digital technologies that will combine with traditional medtech to create the products and technologies of the future. And Yorkshire is a hotbed for the technology, with all of our partner universities offering expertise in the field.
ADHD is being recognized as a diagnosis which persists into adulthood impacting economic, occupational, and educational outcomes. There is an increased need to accurately diagnose and recommend interventions for this population. One consideration is the development and implementation of reliable and valid outcome measures which reflect core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity when compared to their peers (Michalek et al., 2014). A reduction in working memory capacity indicates attentional control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as eye tracking technology, to generate a relationship between ADHD and measures of working memory capacity would be useful to advancing our understanding and treatment of the diagnosis in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a working memory capacity task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.
In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.
A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the 'true' underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated 'ground-truth' dynamical (benchmark) systems as well as on actual experimental fMRI time series. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research.