Learning new skills can make older people's brains three decades younger in just six weeks, according to a new study. Taking up three new tasks at the same time boosts mental power and protects against Alzheimer's disease, scientists have found. These skills may range from language lessons to using an iPad, photography, writing music or painting. Taking up three new skills, such as language lessons or learning how to use an iPad, at the same time can make older people's brains three decades younger in just six weeks (file photo) The course workload would be similar to an undergraduate's and adds to growing evidence that dementia is avoidable through lifestyle changes. After less than two months, those in their 80s increased their cognitive abilities to levels similar to those seen in someone in their 50s.
In a related editorial, R. Jeffrey Westcott, MD, and James E. Tcheng, MD, said Zack and colleagues' findings support the idea that machine learning could outperform classical statistical approaches to risk prediction--but it'll take some work to make it an industry standard. "Transforming healthcare, and, more specifically, transforming the management of data within healthcare to enable AI and its siblings, requires foundational investment and culture change," the editorialists wrote. They said artificial intelligence and machine learning will undoubtedly become "increasingly important in clinical medicine" as we move forward, with equity funding for healthcare-related AI ventures topping $2.4 billion in 2018. "Machine learning has proven to be valuable and is therefore the future," Westcott and Tcheng wrote. "Data warehouses and data lakes contain amazing amounts of structured and unstructured data that will change how medical research, drug and device trials, and device tracking are done. A collaborative effort is needed with EHR vendors, third-party vendors, professional societies and others to start meaningful standardized data collection and workflow redesign now."
SONAL SHAH: It's also about how do we make data more useful for people to use and to solve problems in their communities? TANYA OTT: Okay, that is a big job. Who is this superhuman who fills it? TANYA OTT: We'll tell you, in a moment. But first, let me say, you're listening to the Press Room, where we talk about some of the biggest issues facing businesses today. I'm Tanya Ott and joining me today are Bill Eggers … I am the executive director and a professor of practice at Georgetown University's Beeck Center. TANYA OTT: Bill and Sonal are coauthors of The CDO Playbook – a guide for Chief Data Officers. For the last decade, government has been focused on making data more open and easily [accessible] to the public.
The human brain with less than 20 W of power consumption offers a processing capability that exceeds the petaflops mark, and thus outperforms state-of-the-art supercomputers by several orders of magnitude in terms of energy efficiency and volume. Building ultra-low-power cognitive computing systems inspired by the operating principles of the brain is a promising avenue towards achieving such efficiency. Recently, deep learning has revolutionized the field of machine learning by providing human-like performance in areas, such as computer vision, speech recognition, and complex strategic games1. However, current hardware implementations of deep neural networks are still far from competing with biological neural systems in terms of real-time information-processing capabilities with comparable energy consumption. One of the reasons for this inefficiency is that most neural networks are implemented on computing systems based on the conventional von Neumann architecture with separate memory and processing units.
QuantX recently became the first-ever computer-aided breast cancer diagnosis system cleared by the FDA for use in radiology, but it's not putting radiologists out of a job any time soon. "Radiology is the backbone of diagnosing many diseases today," said Jeffrey Aronin, chairman and CEO of Paragon Biosciences. "We believe the future is radiologists with technology." The combination of humans and machines apparently works really well. In a clinical study, QuantX helped radiologists interpret MRIs, noting the differences between cancerous and noncancerous breast lesions.
Elon Musk's Neuralink projects have been somewhat secretive since the company was first established. To that effect, all that's been known about the firm was that it was working on machine-brain interfaces. Well, the company has finally gone public with its first project and it turns out that it's an AI that can be inserted into a person's brain to allow them to connect to phones and computers. Machine/brain interface devices have been on the market in some form for over a decade, with people suffering from paralysis seeing many of the benefits of using these kinds of devices. For example, back in 2006, Matthew Nagle, who suffered from a spinal cord injury, was able to play Pong aided by the devices.
Williams Syndrome, a rare neurodevelopmental disorder that affects about 1 in 10,000 babies born in the United States, produces a range of symptoms including cognitive impairments, cardiovascular problems, and extreme friendliness, or hypersociability. In a study of mice, MIT neuroscientists have garnered new insight into the molecular mechanisms that underlie this hypersociability. They found that loss of one of the genes linked to Williams Syndrome leads to a thinning of the fatty layer that insulates neurons and helps them conduct electrical signals in the brain. The researchers also showed that they could reverse the symptoms by boosting production of this coating, known as myelin. This is significant, because while Williams Syndrome is rare, many other neurodevelopmental disorders and neurological conditions have been linked to myelination deficits, says Guoping Feng, the James W. and Patricia Poitras Professor of Neuroscience and a member of MIT's McGovern Institute for Brain Research.
Elon Musk recently gave a presentation on Neuralink, his newest venture designed to create computer-brain interfaces. Founded in 2017, the company is experimenting with a minimally invasive brain implant that utilizes "threads" to reduce the amount of damage done to surrounding brain tissue compared to current implanted devices. Musk spoke on the unnecessary size of most current implants, saying that a smaller chip could be used in their place. Providing patients with a smaller, less obstructive brain implant is exactly what Neuralink is aiming to do with their product. In the presentation, Musk also said he sees Neuralink potentially bridging the gap between the human brain and artificial intelligence as well.
Elon Musk doesn't think his newest endeavor, revealed Tuesday night after two years of relative secrecy, will end all human suffering. At a presentation at the California Academy of Sciences, hastily announced via Twitter and beginning a half hour late, Musk presented the first product from his company Neuralink. It's a tiny computer chip attached to ultrafine, electrode-studded wires, stitched into living brains by a clever robot. And depending on which part of the two-hour presentation you caught, it's either a state-of-the-art tool for understanding the brain, a clinical advance for people with neurological disorders, or the next step in human evolution. The chip is custom-built to receive and process the electrical action potentials--"spikes"--that signal activity in the interconnected neurons that make up the brain.
An artificial skin that senses temperature and pressure can send signals 1000 times faster than the human nervous system. The skin could one day cover prosthetic limbs to help people use them better or be used on robots to help them sense their surroundings. Benjamin Tee at the National University of Singapore and his colleagues created the artificial skin, consisting of physical sensors that can detect pressure, bend, and temperature, placed inside a layer of plastic. All of the sensors are connected together using a single wire, meaning that the measurement from across the skin arrive at the same time."In If you have 1000 sensors, and each one takes 1 millisecond to scan, then the entire scanning operation will take 1 full second."