State company tested how artificial intelligence could minimise electricity disruptions and now looks to expand the technology.
Category: robotics/AI – Page 1,391
MIT scientists have managed to build neural networks that are both faster and cheaper to produce while using less energy.
Constructing a tiny robot from DNA and using it to study cell processes invisible to the naked eye… You would be forgiven for thinking it is science fiction, but it is in fact the subject of serious research by scientists from Inserm, CNRS and Université de Montpellier at the Structural Biology Center in Montpellier[1]. This highly innovative “nano-robot” should enable closer study of the mechanical forces applied at microscopic levels, which are crucial for many biological and pathological processes. It is described in a new study published in Nature Communications.
Our cells are subject to mechanical forces exerted on a microscopic scale, triggering biological signals essential to many cell processes involved in the normal functioning of our body or in the development of diseases.
For example, the feeling of touch is partly conditional on the application of mechanical forces on specific cell receptors (the discovery of which was this year rewarded by the Nobel Prize in Physiology or Medicine).
From Human to Artificial General Intelligence
Humans have an almost unbounded set of skills and knowledge, and quickly learn new information without needing to be re-engineered to do so. It is conceivable that an AGI can be built using an approach that is fundamentally different from human intelligence. However, as three longtime researchers in AI and cognitive science, our approach is to draw inspiration and insights from the structure of the human mind. We are working toward AGI by trying to better understand the human mind, and better understand the human mind by working toward AGI.
From research in neuroscience, cognitive science, and psychology, we know that the human brain is neither a huge homogeneous set of neurons nor a massive set of task-specific programs that each solves a single problem. Instead, it is a set of regions with different properties that support the basic cognitive capabilities that together form the human mind.
Artificial Intelligence has ushered the advancement of several disciplines throughout the years. But could it ever discover a new form of physics?
A group of roboticists from Columbia University wanted to exploit the vast potential of AI and find out if it can ever find an “alternative physics.”
Hence, they created an AI tool that could recognize physical occurrences and identify pertinent variables, essential building blocks for every physics theory.
Human-like temporal variability in movements is a powerful hint that humans use to ascribe humanness to robots.
A team of researchers at the University of Geneva has found that ketamine is unlikely to be addictive to people who use it for extended periods of time. In their paper published in the journal Nature, the group describes their study of the impact of the synthetic compound on the brains of mice and what they learned about its impact on different brain regions. Rianne Campbell and Mary Kay Lobo, with the University of Maryland School of Medicine have published a News and Views piece in the same journal issue outlining the work done by the team in Switzerland.
In recent years, more vehicles include partially autonomous driving features, such as blind spot detectors, automatic braking and lane sensing, which are said to increase safety. However, a recent study by researchers from The University of Texas at Austin finds that some of that safety benefit may be offset by people driving more, thereby clogging up roads and exposing themselves to more potential crashes.
The study, published recently in Transportation Research Part A—Policy and Practice, found that drivers with one or more of these autonomous features reported higher miles traveled than those of similar profiles who didn’t have them. This is important, because miles traveled is one of the most—if not the most—significant predictor of crashes. The more you drive, the more likely you are to crash.
“What we showed, without any ambiguity in our results, is that after embracing autonomous features, people tend to drive more,” said Chandra Bhat, one of the authors on the project and professor in the Cockrell School of Engineering’s Department of Civil, Architectural and Environmental Engineering. “There are certainly engineering benefits to these safety features, but they are offset to a good extent because people are driving more and exposed more.”
DeepMind’s AlphaFold tool has determined the structures of around 200 million proteins, from almost every known organism on Earth.
MIT researchers created protonic programmable resistors — building blocks of analog deep learning systems — that can process data 1 million times faster than synapses in the human brain. These ultrafast, low-energy resistors could enable analog deep learning systems that can train new and more powerful neural networks rapidly, which could be used for areas like self-driving cars, fraud detection, and health care.