
Artificial Intelligence beats us in chess, but not in memory
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A new piece of research shows that the brain strategy for storing memories may lead to imperfect memories, but in turn, allows it to store more memories, and with less hassle than AI. The new study, carried out by SISSA scientists in collaboration with Kavli Institute for Systems Neuroscience & Centre for Neural Computation, Trondheim, Norway, has just been published in Physical Review Letters.
Some organisms evolve an internal switch that can remain hidden for generations until stress flicks it on.
Computational materials science experts at the US Department of Energy's Ames Laboratory enhanced an algorithm that borrows its approach from the nesting habits of cuckoo birds, reducing the search time for new high-tech alloys from weeks to mere seconds.
A major roadblock to computational design of high-entropy alloys has been removed, according to scientists at Iowa State University and Lehigh University. Engineers from the Ames Lab and Lehigh University's Department of Mechanical Engineering and Mechanics have developed a process that reduces search time used for predictive design 13,000-fold.
MIT researchers have devised a way to computationally model viral escape, using models that were originally developed to model language. The model can predict which sections of viral surface proteins, including those of influenza, HIV, and SARS-CoV-2, are more likely to mutate in a way that allows the virus to evade the human immune system. It can also identify sections that are less likely to mutate, making them good targets for new vaccines.
Weakly electric fish are specially adapted to traverse murky waters without relying on vision; instead, they sense their environment via electric fields. Researchers developed an innovative algorithm for observing objects via electrosensing that is based on the real behavior of weakly electric fish.
A new method, developed by Olivier Delaneau's group at the SIB Swiss Institute of Bioinformatics and the University of Lausanne, offers game-changing possibilities for genetic association studies and biomedical research. For less than $1 in computational cost, GLIMPSE is able to statistically infer a complete human genome from a very small amount of data. It offers a first realistic alternative to current approaches, and so allows a wider inclusion of underrepresented populations.
Robotics researchers at the University of Zurich show how onboard cameras can be used to keep damaged quadcopters in the air and flying stably -- even without GPS.
In a Science Advances study, UCI researchers describe how they developed a deep-learning framework to observe gene regulation at the cellular level.
Professors at University of Maryland, Baltimore County have developed an artificial intelligence technique to quickly analyze newly collected data based on Arctic ice and snow thickness. Researchers previously analyzed these data manually; this AI will assist them by automating how they detect and analyze patterns in the thickness of the ice. Climate change necessitates a rapid understanding of new developments in the Arctic ice, and this tool provides a faster solution.