Summary: Researchers leveraged a tracking algorithm from video games to study molecules’ behavior within live brain cells.
They adapted the fast and accurate algorithm used to track bullets in combat games for use in super-resolution microscopy. The innovative approach enables scientists to observe how molecules cluster together to perform specific functions in space and time within the brain cells.
The data obtained could shed light on molecular functions’ disruption during aging and disease.
Ultra-high pressure can have strange effects in physics and chemistry, and in a new study, high-pressure modeling has led to the prediction of four new compounds: compounds that don’t form in normal ways, have crystal structures we’ve never seen before, and can even act as superconductors in certain temperatures.
Those compounds are Li14 Cs, Li8Cs, Li7Cs, and Li6Cs, and they’re all formed from lithium (Li) and cesium (Cs) – though not in a conventional way. All four are superconductors, which means electricity can flow through them without resistance or energy loss.
The scientists behind the study used a special crystal structure prediction algorithm called USPEX (Universal Structure Predictor: Evolutionary Xtallography) to find these new compounds. It’s known as an evolutionary algorithm, using a range of methods to figure out the probability of how atoms will link together.
Dr. Ben Goertzel shares his thoughts on where we are at the end of 2021, beginning of 2022 — how progress toward AGI looks in retrospect, and looking into the future — updates on the ecosystem…
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Understanding the behavior of nuclear matter—including the quarks and gluons that make up the protons and neutrons of atomic nuclei—is extremely complicated. This is particularly true in our world, which is three dimensional. Mathematical techniques from condensed matter physics that consider interactions in just one spatial dimension (plus time) greatly simplify the challenge.
Using this two-dimensional approach, scientists solved the complex equations that describe how low-energy excitations ripple through a system of dense nuclear matter. This work indicates that the center of neutron stars, where such dense nuclear matter exists in nature, may be described by an unexpected form.
Being able to understand the quark interactions in two dimensions opens a new window into understanding neutron stars, the densest form of matter in the universe. The approach could help advance the current “golden age” for studying these exotic stars. This surge in research success was triggered by recent discoveries of gravitational waves and electromagnetic emissions in the cosmos.
Computer scientists have, for decades, been optimizing how computers sort data to shave off crucial milliseconds in returning search results or alphabetizing contact lists. Now DeepMind, based in London, has vastly improved sorting speeds by applying the technology behind AlphaZero — its artificial-intelligence system for playing the board games chess, Go and shogi — to a game of building sorting algorithms. “This is an exciting result,” said Emma Brunskill, a computer scientist at Stanford University, California.
The system, AlphaDev, is described in a paper in Nature1, and has invented faster algorithms that are already part of two standard C++ coding libraries, so are being used trillions of times per day by programmers around the world.
One nebulous aspect of the poll, and of many of the headlines about AI we see on a daily basis, is how the technology is defined. What are we referring to when we say “AI”? The term encompasses everything from recommendation algorithms that serve up content on YouTube and Netflix, to large language models like ChatGPT, to models that can design incredibly complex protein architectures, to the Siri assistant built into many iPhones.
IBM’s definition is simple: “a field which combines computer science and robust datasets to enable problem-solving.” Google, meanwhile, defines it as “a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more.”
It could be that peoples’ fear and distrust of AI comes partly from a lack of understanding of it, and a stronger focus on unsettling examples than positive ones. The AI that can design complex proteins may help scientists discover stronger vaccines and other drugs, and could do so on a vastly accelerated timeline.
“Intelligence supposes goodwill,” Simone de Beauvoir wrote in the middle of the twentieth century. In the decades since, as we have entered a new era of technology risen from our minds yet not always consonant with our values, this question of goodwill has faded dangerously from the set of considerations around artificial intelligence and the alarming cult of increasingly advanced algorithms, shiny with technical triumph but dull with moral insensibility.
In De Beauvoir’s day, long before the birth of the Internet and the golden age of algorithms, the visionary mathematician, philosopher, and cybernetics pioneer Norbert Wiener (November 26, 1894–March 18, 1964) addressed these questions with astounding prescience in his 1954 book The Human Use of Human Beings, the ideas in which influenced the digital pioneers who shaped our present technological reality and have recently been rediscovered by a new generation of thinkers eager to reinstate the neglected moral dimension into the conversation about artificial intelligence and the future of technology.
A decade after The Human Use of Human Beings, Wiener expanded upon these ideas in a series of lectures at Yale and a philosophy seminar at Royaumont Abbey near Paris, which he reworked into the short, prophetic book God & Golem, Inc. (public library). Published by MIT Press in the final year of his life, it won him the posthumous National Book Award in the newly established category of Science, Philosophy, and Religion the following year.
“It’s an interesting new approach,” says Peter Sanders, who studies the design and implementation of efficient algorithms at the Karlsruhe Institute of Technology in Germany and who was not involved in the work. “Sorting is still one of the most widely used subroutines in computing,” he says.
DeepMind published its results in Nature today. But the techniques that AlphaDev discovered are already being used by millions of software developers. In January 2022, DeepMind submitted its new sorting algorithms to the organization that manages C++, one of the most popular programming languages in the world, and after two months of rigorous independent vetting, AlphaDev’s algorithms were added to the language. This was the first change to C++’s sorting algorithms in more than a decade and the first update ever to involve an algorithm discovered using AI.
Sorting algorithms are basic functions used constantly by computers around the world, so an improved one created by an artificial intelligence could make millions of programs run faster.
Digital society is driving increasing demand for computation, and energy use. For the last five decades, we relied on improvements in hardware to keep pace. But as microchips approach their physical limits, it’s critical to improve the code that runs on them to make computing more powerful and sustainable. This is especially important for the algorithms that make up the code running trillions of times a day.
In our paper published today in Nature, we introduce AlphaDev, an artificial intelligence (AI) system that uses reinforcement learning to discover enhanced computer science algorithms – surpassing those honed by scientists and engineers over decades.
Nature Publication.
