When it comes to coronavirus infections, the third time is not the charm. What is?
A Carnegie Mellon team used an automated system and machine-learning software to develop fast-charging electrolytes that outperformed a standard one.
face_with_colon_three circa 2021.
Think about where our energy comes from: drilling rigs and smokestacks, windmills and solar panels. Lithium-ion battery packs might even come to mind.
We probably don’t think about the farms that comprise over one-third of Earth’s total land area. But farms can also be an energy source. Barcelona-based battery company Bioo is generating electricity from the organic matter in soil and creating biological batteries that can power agricultural sensors, a growing 1.36 billion dollar global market.
Bioo’s tech eliminates the need for single-use chemical batteries, which have to be replaced frequently. The company will work with large players such as Bayer Crop Science to pilot its sensor tech on farms, while also experimenting with using bio-batteries to power lighting installations. Eventually, Bioo envisions a future where biology may even help power our largest cities.
face_with_colon_three circa 2021.
Microsoft has developed an AI program called BugLab, which can help you find and fix bugs in your code. Here’s how it works!
It took scientists 375 years to discover the eighth continent of the world, which had been hiding in plain sight all along. But mysteries still remain.
Improving the efficiency of algorithms for fundamental computations is a crucial task nowadays as it influences the overall pace of a large number of computations that might have a significant impact. One such simple task is matrix multiplication, which can be found in systems like neural networks and scientific computing routines. Machine learning has the potential to go beyond human intuition and beat the most exemplary human-designed algorithms currently available. However, due to the vast number of possible algorithms, this process of automated algorithm discovery is complicated. DeepMind recently made a breakthrough discovery by developing AplhaTensor, the first-ever artificial intelligence (AI) system for developing new, effective, and indubitably correct algorithms for essential operations like matrix multiplication. Their approach answers a mathematical puzzle that has been open for over 50 years: how to multiply two matrices as quickly as possible.
AlphaZero, an agent that showed superhuman performance in board games like chess, go, and shogi, is the foundation upon which AlphaTensor is built. The system expands on AlphaZero’s progression from playing traditional games to solving complex mathematical problems for the first time. The team believes this study represents an important milestone in DeepMind’s objective to improve science and use AI to solve the most fundamental problems. The research has also been published in the established Nature journal.
Matrix multiplication has numerous real-world applications despite being one of the most simple algorithms taught to students in high school. This method is utilized for many things, including processing images on smartphones, identifying verbal commands, creating graphics for video games, and much more. Developing computing hardware that multiplies matrices effectively consumes many resources; therefore, even small gains in matrix multiplication efficiency can have a significant impact. The study investigates how the automatic development of new matrix multiplication algorithms could be advanced by using contemporary AI approaches. In order to find algorithms that are more effective than the state-of-the-art for many matrix sizes, AlphaTensor further leans on human intuition. Its AI-designed algorithms outperform those created by humans, which represents a significant advancement in algorithmic discovery.
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Science-fiction author Neal Stephenson predicted cryptocurrencies and virtual worlds — even coining the term metaverse — in his 1992 novel Snow Crash. Now, 30 years later, he is combining the two ideas by launching a blockchain-powered open metaverse. It is a move that sets him against big tech firms with their own metaverse plans, so why is he doing it and what even is an open metaverse?
Developers have pushed various versions of the metaverse for decades, but nothing has stuck.
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Neal Stephenson was contacted for comment.
Circa 2015 face_with_colon_three
From driving water wheels to turning turbines, waterhas been used as the prime mover of machinery and the powerhouse of industry for many centuries. In ancient times, the forces of flowing water were even harnessed to power the first rudimentaryclocks. Now, engineers at Stanford University have created the world’s first water-operated computer. Using magnetized particles flowing through a micro-miniature network ofchannels, the machine runs like clockwork and is claimed to be capable ofperforming complex logical operations.
Using poppy-seed sizeddroplets of water impregnated with magnetic nanoparticles (those handy little elementsbeing used in everything from drug delivery inhumans to creating e-paper whiteboards), the new fluidic computer uses electromagnetic fields to accurately pump thesedroplets around a set of physical gates to perform logical operations. Suspendedin oil and timed to move in very specific steps, the droplets in the system cantheoretically be used to accomplish any process that a normal electroniccomputer can, albeit at considerably slower speeds.