The problem is not that today’s A.I. needs to get better at what it does. The problem is that today’s A.I. needs to try to do something completely different.
Computer systems need to understand time, space and causality. Right now they don’t.
A talk by Dave Bacon during the Industry session of the 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019), Day 3. TQC 2019 was hosted June 3–5, 2019 by the Joint Center for Quantum Information and Computer Science at the University of Maryland (QuICS). More information about TQC can be found at https://www.tqcconference.org.
Something odd is stirring in the depths of Canada’s Kidd Mine. The zinc and copper mine, 350 miles northwest of Toronto, is the deepest spot ever explored on land and the reservoir of the oldest known water. And yet 7,900 feet below the surface, in perpetual darkness and in waters that have remained undisturbed for up to two billion years, the mine is teeming with life.
Many scientists had doubted that anything could live under such extreme conditions. But in July, a team led by University of Toronto geologist Barbara Sherwood Lollar reported that the mine’s dark, deep water harbors a population of remarkable microbes.
The single-celled organisms don’t need oxygen because they breathe sulfur compounds. Nor do they need sunlight. Instead, they live off chemicals in the surrounding rock — in particular, the glittery mineral pyrite, commonly known as fool’s gold.
A team of scientists at Freie Universität Berlin has developed an Artificial Intelligence (AI) method that provides a fundamentally new solution of the “sampling problem” in statistical physics. The sampling problem is that important properties of materials and molecules can practically not be computed by directly simulating the motion of atoms in the computer because the required computational capacities are too vast even for supercomputers. The team developed a deep learning method that speeds up these calculations massively, making them feasible for previously intractable applications. “AI is changing all areas of our life, including the way we do science,” explains Dr. Frank Noé, professor at Freie Universität Berlin and main author of the study. Several years ago, so-called deep learning methods bested human experts in pattern recognition—be it the reading of handwritten texts or the recognition of cancer cells from medical images. “Since these breakthroughs, AI research has skyrocketed. Every day, we see new developments in application areas where traditional methods have left us stuck for years. We believe our approach could be such an advance for the field of statistical physics.” The results were published in Science.
Statistical Physics aims at the calculation of properties of materials or molecules based on the interactions of their constituent components—be it a metal’s melting temperature, or whether an antibiotic can bind to the molecules of a bacterium and thereby disable it. With statistical methods, such properties can be calculated in the computer, and the properties of the material or the efficiency of a specific medication can be improved. One of the main problems when doing this calculation is the vast computational cost, explains Simon Olsson, a coauthor of the study: “In principle we would have to consider every single structure, that means every way to position all the atoms in space, compute its probability, and then take their average. But this is impossible because the number of possible structures is astronomically large even for small molecules.
A research station that collected environmental data from the bottom of the Baltic Sea has disappeared in what German researchers believe was a bizarre incident of underwater burglary.
When Elon Musk and the team at Tesla unveiled the Tesla Roadster 2.0, a new stake was pounded into the tarmac, cementing the new Roadster and electric cars as the performance kings in nearly every meaningful category. It puts supercars to shame and at a fraction of the price.
With such a high bar being set at such a low price point, a no holds barred electric supercar seemed to be the only thing that could possibly top the high marks set by the new Tesla Roadster. Travel with me over to unlikely Sveta Nedelja, Croatia, where Mate Rimac and his motley crew of twisted engineering geniuses at Rimac Automobili assemble battery powered beasts that shake the boots off even the most seasoned track driver.
Continue reading “Rimac Bumps Tesla’s New Roadster To Second Place With New Concept_Two” »
Microsoft’s patent filing recently made public has juiced up curiosity over what Microsoft might debut sooner or later as its own version of a folding computing device.
MSPoweruser took the view that “Microsoft is trying hard to bring its first foldable device to the market, like every other big tech companies.” It’s apparent now that “the Redmond giant has filed yet another patent for its much-awaited foldable Windows 10 device.”
The patent “Multi-Sided Electromagnetic Coil Access Assembly” was filed in February last year but only recently made public. It is particularly drawing interest because, as TechRadar said, what would make this foldable idea work would be “a multi-sided electromagnetic coil for wireless charging.”
Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle in drug discovery.
University of Cambridge researchers have shown that an algorithm can predict the outcomes of complex chemical reactions with over 90% accuracy, outperforming trained chemists. The algorithm also shows chemists how to make target compounds, providing the chemical “map” to the desired destination. The results are reported in two studies in the journals ACS Central Science and Chemical Communications.
A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks. The problem is that those building blocks often react in unexpected ways.
Quantum many-body systems (QMBs), which are physical systems made up of multiple interacting particles, are among the most challenging structures to reproduce in numerical simulations. In the past, researchers have attempted to simulate these systems using a variety of techniques, including Monte Carlo simulations and even exact diagonalizations.
Methods involving tensor networks (TNs), mathematical concepts that can be applied in a variety of scientific fields, have also shown some potential for the simulation of QMBs. However, so far, these techniques have only been successfully applied to small systems or those with a simple geometry.
In a recent study, researchers at the University of Central Florida were able to simulate QMBs on Amazon Web Services using a TN-based method. Their paper, pre-published on arXiv, highlights some of the potential advantages and implications of using cloud services for research purposes.
On a cool, overcast morning in Kaohsiung City, Taiwan, a chunky and colorfully painted ferry waits at a dock as a stream of passengers clambers aboard. It’
