MIT researchers have developed a way for humans to control robots with their mind. The new system can detect when a person notices a robot making a mistake, and classifies these brain waves almost instantly to provide feedback. In tests with a robot named Baxter, the researchers found that the new technique helped the humanoid to make correct choices during an object-sorting task – and it could one day allow humans to wordlessly communicate with robots. MIT researchers have developed a way for humans to control robots with their mind. As the robot, Baxter, attempts to sort objects between'Paint' or'Wire' bins, the system looks for brain signals from the human observer known as'error-related potentials.' Once an error has been detected, machine-learning algorithms can sort these brain waves in just 10 to 30 milliseconds to provide feedback.
When you get right down to it, developing vaccines is about data and luck. Scientists start with a set of variables--what drugs a virus responds to, how effectively, and for whom--and then it's a whole lot of trial and error until they stumble upon a cure. One of the most exciting possibilities in medical research right now is how technology like machine learning could help researchers rapidly process those enormous sets of data, more quickly leading to cures. This is already starting to happen: In a study published Wednesday in the journal Macromolecules, researchers from IBM and Singapore's Institute of Bioengineering and Nanotechnology reveal a breakthrough that could help prevent deadly virus infections. With the help of IBM super computer Watson, they hope their finding will soon make its way into vaccines.
WASHINGTON, DC (March 8, 2017)--Interventional radiologists at the University of California at Los Angeles (UCLA) are using technology found in self-driving cars to power a machine learning application that helps guide patients' interventional radiology care, according to research presented today at the Society of Interventional Radiology's 2017 Annual Scientific Meeting. The researchers used cutting-edge artificial intelligence to create a "chatbot" interventional radiologist that can automatically communicate with referring clinicians and quickly provide evidence-based answers to frequently asked questions. This allows the referring physician to provide real-time information to the patient about the next phase of treatment, or basic information about an interventional radiology treatment. "We theorized that artificial intelligence could be used in a low-cost, automated way in interventional radiology as a way to improve patient care," said Edward W. Lee, M.D., Ph.D., assistant professor of radiology at UCLA's David Geffen School of Medicine and one of the authors of the study. "Because artificial intelligence has already begun transforming many industries, it has great potential to also transform health care."
Driven by a veritable tsunami of data and bolstered by powerful analytics tools, the artificial intelligence solution is on the rise in the enterprise space. AI innovations are driving new efficiencies and a wealth of other positive metrics across a range of industries. In its latest survey of 235 business executives, Narrative Science took a deep look at the adoption of artificial intelligence in the enterprise. The resulting report details the emergence of AI as a core business strategy, with 38 percent of organizations saying they already use AI technologies in the workplace, and 62 percent likely to be using the technology by 2018. The study found AI manifesting in a number of different forms in the enterprise, including deep learning, natural language generation and recommendation engines.
The devastating neurodegenerative condition Alzheimer's disease is incurable, but with early detection, patients can seek treatments to slow the disease's progression, before some major symptoms appear. Now, by applying artificial intelligence algorithms to MRI brain scans, researchers have developed a way to automatically distinguish between patients with Alzheimer's and two early forms of dementia that can be precursors to the memory-robbing disease. The researchers, from the VU University Medical Center in Amsterdam, suggest the approach could eventually allow automated screening and assisted diagnosis of various forms of dementia, particularly in centers that lack experienced neuroradiologists. Additionally, the results, published online July 6 in the journal Radiology, show that the new system was able to classify the form of dementia that patients were suffering from, using previously unseen scans, with up to 90 percent accuracy. "The potential is the possibility of screening with these techniques so people at risk can be intercepted before the disease becomes apparent," said Alle Meije Wink, a senior investigator in the center's radiology and nuclear medicine department.