The adoption and effectiveness of cognitive assistive technologies hinge on harnessing the dynamics of human emotion. The authors discuss seminal advances in the integration of emotions in assistive technologies for dementia and propose Bayesian Affect Control Theory (BayesACT), a quantitative social-psychological theory, to model behavior and emotion in such systems.
"Almost all fields of artificial intelligence have applications in healthcare."1 Medicine appears to have entered the era of data, and artificial intelligence (AI) will prove a valuable tool in the future, notably as an aid to diagnosis. Watson, the program developed by IBM, is the most emblematic example. Based on deep learning, the best known branch of artificial intelligence, it operates by layers, like a network of interconnected neurons spread between different strata for each calculation. The answer is only "produced" after a learning process which from the start associates symptoms and pathology.
"I'm a nerd!" Jana Eggers tells us in a tone that suggests she is very much at ease with the description. Realistically, this airy room in the Berlin offices of the state of Baden-Württemberg, where a major conference on artificial intelligence (AI) took place on Wednesday and Thursday, is probably full of self-confessed nerds unlikely to be too upset by the moniker. Considering the tasks many of them have taken on in their professional lives -- the understanding and developing of artificial intelligence systems -- that brain power is needed. In effect, they are trying to build tech that mirrors the functioning of that most remarkable of natural organs, the human brain. Read more: Teachers for AI -- can robots create more jobs than they retire?
Three times a day I take a drug called levodopa. I take it because my brain does not produce enough dopamine, without it my hands and feet shake and I have difficulty getting my body to do what I want it to do. These are symptoms of Parkinson's disease and mean that many of my dopamine producing neurons have died. But, thanks to levodopa, I can feed my brain synthetic dopamine. It is an incredible little drug that we discovered to be naturally produced in the broad bean plant, pictured here.
When David Graham wakes up in the morning, the flat white box that's Velcroed to the wall of his room in Robbie's Place, an assisted living facility in Marlborough, Massachusetts, begins recording his every movement. It knows when he gets out of bed, gets dressed, walks to his window, or goes to the bathroom. It can tell if he's sleeping or has fallen. It does this by using low-power wireless signals to map his gait speed, sleep patterns, location, and even breathing pattern. All that information gets uploaded to the cloud, where machine-learning algorithms find patterns in the thousands of movements he makes every day.
No two neurons are alike. What does that mean for brain function? Brain cells may be as unique as the people to which they belong. This genetic, molecular, and morphological diversity of the brain leads to the functional variation that is likely necessary for the higher-order cognitive processes that are unique to humans. As researchers continue to probe the enormous complexity of the human brain at the single-cell level, they will likely begin to uncover the answers to these questions--as well as those we haven't even thought to ask yet.
The tiny tadpole embryo looked like a bean. One day old, it didn't even have a heart yet. The researcher in a white coat and gloves who hovered over it made a precise surgical incision where its head would form. Moments later, the brain was gone, but the embryo was still alive. The brief procedure took Celia Herrera-Rincon, a neuroscience postdoc at the Allen Discovery Center at Tufts University, back to the country house in Spain where she had grown up, in the mountains near Madrid. When she was 11 years old, while walking her dogs in the woods, she found a snake, Vipera latastei.
We are looking for a Postdoctoral Research Associate with a background in electrical engineering, physics, statistics or computer science to work on a research project involving the application of machine learning techniques to neuroanatomical data. The project will lead to the development of a practical and flexible web-based tool for measuring neuroanatomical alterations in any brain-based disorders. The successful applicant will have previous experience in the application of machine learning - including both shallow and deep learning algorithms - to neuroimaging data. The post holder will join a multi-disciplinary team including clinicians, neuroscientists, psychologists and computer scientists. The selection process will include a panel interview.
My tongue is orange!" my 2-year-old daughter shrieked after licking a dollop of clear hand sanitizer. "Mommy, my ear feels orange," she moaned when an earache struck. It's orange," she whined from inside her snowsuit when a scratchy tag in her new white glove rubbed uncomfortably against her wrist. As her vocabulary blossomed, she started to associate colors with scents. "What smells pink?" (Dryer exhaust puffing out of a neighbor's basement vent.) Anyone who has spent time around toddlers knows they say some strange things.