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) …
Research groups at KAIST, the University of Cambridge, Japan's National Institute for Information and Communications Technology, and Google DeepMind argue that our understanding of how humans make intelligent decisions has now reached a critical point in which robot intelligence can be significantly enhanced by mimicking strategies that the human brain uses when we make decisions in our everyday lives. In our rapidly changing world, both humans and autonomous robots constantly need to learn and adapt to new environments. But the difference is that humans are capable of making decisions according to the unique situations, whereas robots still rely on predetermined data to make decisions. Despite the rapid progress being made in strengthening the physical capability of robots, their central control systems, which govern how robots decide what to do at any one time, are still inferior to those of humans. In particular, they often rely on pre-programmed instructions to direct their behavior, and lack the hallmark of human behavior, that is, the flexibility and capacity to quickly learn and adapt.
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.
Scientists in multiple fields of psychology are actively gathering data and undergoing testing in an effort to teach artificial intelligence programs to diagnose mental illness in humans. This is according to a report in The Verge written by B. David Zarley, who himself has borderline personality disorder, as part of its Real World AI issue. Zarley met with multiple scientists who are each taking their own approach to machine learning in the service of finding a better way to diagnose psychological disorders. Sponsored adThis sponsor paid to have this advertisement placed in this section. The current model, based on referring to the DSM to guide psychiatrists to make diagnoses around a patient's self-reported symptoms, is inherently biased and considered by many in the field of psychology to be flawed.
Sina Habibi, CEO of Cognetivity Neurosciences, spoke with INN about the company's partnership with DPUK and additional plans for 2019. At the recent Cantech Investment Conference, Sina Habibi, CEO of Cognetivity Neurosciences (CSE:CGN,OTCQB:CGNSF) spoke with the Investing News Network (INN) about the company's partnership with the Dementia Platform UK (DPUK) and additional plans for 2019. Habibi said the company will be putting more efforts into its artificial intelligence (AI) platform and collecting more data as it seeks to train its solutions to detect mental health disorders, like attention deficit hyperactivity disorder (ADHD). As it currently stands, Cognetivity is using AI and machine learning to aid in the early detection of dementia and Alzheimer's disease. On that note, in addition to the DPUK partnership, Habibi spoke to INN about a health application the company has that could be launched by the end of 2019.
Emotion is a fundamental element of human society. If you think about it, everything worth analyzing is influenced by human behavior. Cyber attacks are highly impacted by disgruntled employees who may either ignore due diligence or engage in insider misuse. The stock market depends on the effect of the economic climate, which itself is dependent on the aggregate behavior of the masses. In the field of communication, it is common knowledge that what we say account for only 7% of the message while the rest 93% is encoded in facial expressions and other non-verbal cues.
Robots, artificial intelligence and smart speakers will ease the burden on doctors and give them more time with patients, according to an NHS report on the pending technological "revolution" in healthcare. Developments in the ability to sequence individuals' genomes – the entirety of their genetic data – will also spur on advances, according to the review published on Monday. The report, led by a US academic, Eric Topol, calls for fresh education for staff, with 90% of all NHS jobs predicted to require digital skills within 20 years. But those who fear robots could edge out human practitioners may be reassured by the review's suggestion that technology will "enhance" professionals, giving them greater time for patients. Smart speakers such as Siri and Alexa are envisioned as having a major impact on care.
Breaking up with your first love is hard to do, but at the age of 18, it was a particularly traumatic experience for Nikki Mattocks. Rather than the clean break she had hoped for, she found herself being bombarded with hateful messages on social media from her ex-boyfriend's friends. One even urged her to kill herself. The messages made me so depressed and led to me taking an overdose," says Mattocks. She is just one of millions of people around the world who have found themselves the victim of bullying.
Now, it's not difficult to spot businesses across industries applying AI and ML technologies to streamlines their operations and to stay informed for making better decisions. With the right AI experts and right AI app development, nearly every industry can reap the benefits of the technology. Here are 10 industries which will soon be revolutionized by AI. AI and Machine learning have brought in limitless possibilities to the world of manufacturing. Now, these technologies have capabilities to help businesses implement preventive maintenance to timely take care of machinery and equipment, and to avoid any sudden failure or breakdown.
Pearl Chiu has jet black hair and a bearing of quiet confidence. She pauses to think before she speaks, and radiates a teacher's joy in discussing her work. She's the only clinically trained psychologist in the lab who has direct experience with patients in a clinical setting, and she arrived at machine learning from a distinctly human place. "As I was seeing, working with, patients, I was just frustrated with how little we knew about what is happening," Chiu says. She believes bringing in machines to detect patterns may be a solution.
A novel artificial intelligence (AI) enabled tool can help diagnose schizophrenia more accurately than other such systems, according to a study led by an Indian origin scientist. The tool called EMPaSchiz, developed by researchers at the University of Alberta in Canada, examined brain scans from patients who were diagnosed with schizophrenia and predicted the diagnosis with 87 per cent accuracy. The finding, published in the journal NPJ Schizophrenia, follows on a previous study in 2017 in which researchers at IBM and Alberta developed a tool capable of predicting schizophrenia with 74 per cent accuracy. "Schizophrenia is characterised by a constellation of symptoms that might co-occur in patients. Two individuals with the same diagnosis might still present different symptoms. This often leads to misdiagnosis," said Sunil Kalmady, a post-doctoral fellow at the University of Alberta.