Artificial Intelligence in the Second Machine Age
The advancement of artificial intelligence (AI) in this second machine age is, and will continue to be, the defining feature of this age. With smart machines everything changes. This department tracks what is happening on the AI front and how this technology is changing every area of our lives.
Berkeley Lab scientists teach machines to analyze simulations of exotic subatomic ‘soup.’ Computers can beat chess champions, simulate star explosions, and forecast global climate. We are even teaching them to be infallible problem-solvers and fast learners.
Developed by UZH researchers, the algorithm DroNet allows drones to fly completely by themselves through the streets of a city and in indoor environments. Therefore, the algorithm had to learn traffic rules and adapt training examples from cyclists and car drivers.
UNIGE researchers show the limits of the precision of decoding brain activity, via modern machine learning techniques, particularly in processing speech sounds. For about the last ten years, researchers have been using artificial intelligence techniques called machine learning to decode human brain activity. Applied to neuroimaging data, these algorithms can reconstitute what we see, hear, and even what we think. For example, they show that words with similar meanings are grouped together in zones in different parts of our brain.
Machines trained to cooperate by BYU researchers are outperforming their human counterparts.
An ingredient commonly found in toothpaste could be employed as an anti-malarial drug against strains of malaria parasite that have grown resistant to one of the currently-used drugs. This discovery, led by researchers at the University of Cambridge, was aided by Eve, an artificially-intelligent ‘robot scientist’.
On the way to an intelligent laboratory, physicists from Innsbruck and Vienna present an artificial agent that autonomously designs quantum experiments. In initial experiments, the system has independently (re)discovered experimental techniques that are nowadays standard in modern quantum labs. This shows how machines could play a more creative role in research in the future.
In rare diseases, the computer-aided image analysis of patient portraits can facilitate and significantly improve diagnosis. This is demonstrated by an international team of scientists under the leadership of the University Hospital Bonn and the Charité – Universitätsmedizin Berlin on the basis of so-called GPI anchor deficiencies.
Good scientists are not only able to uncover patterns in the things they study, but to use this information to predict the future.
Forget about today’s modest incremental advances in artificial intelligence, such as the increasing abilities of cars to drive themselves. Waiting in the wings might be a groundbreaking development: a machine that is aware of itself and its surroundings, and that could take in and process massive amounts of data in real time.
‘Immunomap’ Suggests More is Better When it Comes to Immune Cell Receptors and Patients’ Response to Immunotherapy
Johns Hopkins scientists have used a form of artificial intelligence to create a map that compares types of cellular receptors, the chemical “antennas” on the surface of immune system T-cells.
AI Department Head