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.
Eliezer Yudkowsky, a leading proponent of "friendly" artificial intelligence, offers a cautionary observation about the potential of AI -- solving problems by using computers to solve tasks that usually require human intelligence. "The greatest danger of Artificial Intelligence," he writes, "is that people conclude too early that they understand it." Any serious discussion of AI's impact on the aging population must start with Yudkowsky's implied question: Do we understand it? And if we do, how do we harness it to enhance the lives of our burgeoning population of older adults? The potential exists for AI to provide lower health care costs, better transportation and longer employment.
The memory mechanisms of great pond snails could one day help develop drugs for trauma and dementia patients. If you think of a snail, and then think of a human, there are some obvious differences. But decades of studies say our memories might have more in common than some might guess. Memory, and its formation, has been the subject of neuroscientific research for quite some time, yet science has only made incremental steps in this extremely complicated field. One of the recent advances is the discovery that memory is likely similar across organisms, at least at a molecular level.
It starts without warning--or rather, the warnings are there, but your ability to detect them exists only in hindsight. First you're sitting in the car with your son, then he tells you: "I cannot find my old self again." You think, well, teenagers say dramatic stuff like this all the time. Then he's refusing to do his homework, he's writing suicidal messages on the wall in black magic marker, he's trying to cut himself with a razor blade. You sit down with him; you two have a long talk. A week later, he runs home from a nighttime gathering at his friend's apartment, he's bursting through the front door, shouting about how his friends are trying to kill him. He spends the night crouching in his mother's old room, clutching a stuffed animal to his chest. He's 17 years old at this point, and you are his father, Dick Russell, a traveler, a former staff reporter for Sports Illustrated, but a father first and foremost. It is the turn of the 21st century.
In the 1920s, the Soviet scientist Ilya Ivanovich Ivanov used artificial insemination to breed a'humanzee' – a cross between a human and our closest relative species, the chimpanzee. Given the moral quandaries a humanzee might create, we can be thankful that Ivanov failed: when the winds of Soviet scientific preferences changed, he was arrested and exiled. But Ivanov's endeavour points to the persistent, post-Darwinian fear and fascination with the question of whether humans are a creature apart, above all other life, or whether we're just one more animal in a mad scientist's menagerie.
Although deep learning has shown some significant achievements in image analysis and classification, their application to medical images has only recently started gaining momentum. This is because medical images are intrinsically noisier and prone to artifacts. Despite these challenges, these techniques have been shown to provide more accurate diagnoses than human doctors in certain scenarios.
MARKHAM, ON, Jan. 16, 2018 /CNW/ - As Canada marks Alzheimer's Awareness Month, a health innovation team has been awarded funding from the Government of Ontario to pilot a groundbreaking brain health assessment and risk management program powered by predictive analytics and artificial intelligence. Offered in multiple healthcare settings across the province, the program will improve access to services, independence to age in place, and quality of life for patients and caregivers. The technology platform, BrainFX, combines leading neuroscience data with cutting edge software to enable early detection of subtle or mild-to-moderate changes in brain function as well as strategies to slow or reverse cognitive decline. Using the data collected through BrainFX assessments and artificial intelligence, Thoughtwire will develop an Early Identification Application that can identify individuals at-risk by scanning electronic medical records. Research shows that early detection and intervention is essential to enabling seniors to remain at home for as long as possible, alleviating burden on individuals and family caregivers, and slowing the progression of brain-related disorders such as Alzheimer's and dementia.