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Scientists develop machine-learning method to predict the behavior of molecules

An international, interdisciplinary research team of scientists has come up with a machine-learning method that predicts molecular behavior, a breakthrough that can aid in the development of pharmaceuticals and the design of new molecules that can be used to enhance the performance of emerging battery technologies, solar cells, and digital displays.

The work appears in the journal Nature Communications.

“By identifying patterns in , the learning algorithm or ‘machine’ we created builds a knowledge base about atomic interactions within a molecule and then draws on that information to predict new phenomena,” explains New York University’s Mark Tuckerman, a professor of chemistry and mathematics and one of the paper’s primary authors.

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Intel Accelerates Its Quantum Computing Efforts With 17-Qubit Chip

Intel says it is shipping an experimental quantum computing chip to research partners in The Netherlands today. The company hopes to demonstrate that its packaging and integration skills give it an edge in the race to produce practical quantum computers.

The chip contains 17 superconducting qubits—the quantum computer’s fundamental component. According to Jim Clarke, Intel’s director of quantum hardware, the company chose 17 qubits because it’s the minimum needed to perform surface code error correction, an algorithm thought to be necessary to scaling up quantum computers to useful sizes.

Intel’s research partners, at the TU Delft and TNO research center Qutech, will be testing the individual qubits’ abilities as well as performing surface code error correction and other algorithms.

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Photographic Age Biomarker in Mice

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MouseAGE is working to develop the first photographic biomarker of aging in mice to help validate potential anti-aging interventions, save animal lives, and greatly speed up the pace of longevity research.

To create it we will harness the power of an area of artificial intelligence called Machine Learning, and in particular Deep Learning.

Machine Learning, where a computer system can train itself to become better at a task without explicit programming, has already showed great performance in areas such as human facial recognition, autonomous driving, medical image processing, recommendation engines and many others. While these results are powerful, building up the necessary Neural Networks, or algorithms inspired by the human brain, requires a large dataset of images to use for training: thousands of them.

Due to this, the first stage of the project will be to build a simple instrument for data collection, implemented in a mobile application. This will be distributed among numerous mouse breeding facilities and research universities all over the world to rapidly collect and properly annotate image data for analysis.

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Are Space, Time, And Gravity All Just Illusions?

Pioneered by Erik Verlinde, the idea is that gravity emerges from a more fundamental phenomenon in the Universe, and that phenomenon is entropy.

“Sound waves emerge from molecular interactions; atoms emerge from quarks, gluons and electrons and the strong and electromagnetic interactions; planetary systems emerge from gravitation in General Relativity. But in the idea of entropic gravity — as well as some other scenarios (like qbits) — gravitation or even space and time themselves might emerge from other entities in a similar fashion. There are well-known, close relationships between the equations that govern thermodynamics and the ones that govern gravitation. It’s known that the laws of thermodynamics emerge from the more fundamental field of statistical mechanics, but is there something out there more fundamental from which gravity emerges? That’s the idea of entropic gravity.”

Entropic gravity, also known as emergent gravity, is a theory in modern physics that describes gravity as an entropic force—a force with macro-scale homogeneity but which is subject to quantum-level disorder—and not a fundamental interaction. The theory, based on string theory, black hole physics, and quantum information theory, describes gravity as an emergent phenomenon that springs from the quantum entanglement of small bits of spacetime information. As such, entropic gravity is said to abide by the second law of thermodynamics under which the entropy of a physical system tends to increase over time.

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Robots learn to walk naturally

The challenge with bipedal robots isn’t so much getting them to walk at all (although that’s sometimes a problem) as it is getting them to walk naturally. They tend to either step cautiously or quickly run into trouble. Swiss researchers think they can do better, though: they’re working on COMAN (Compliant Humanoid), a headless robot designed to master walking. The automaton is more graceful through a combination of more flexible, elastic joints and a control algorithm that helps the bot understand its own body.

COMAN is aware of the symmetries in its dynamics and structure, which helps it not only walk with a natural gait but carry objects, navigate uneven surfaces like stairs, and react to surprises. If you push the robot, for instance, it knows exactly where to place its foot so that it doesn’t tip over like some of its peers. And thanks to that added flexibility, it’s more likely to survive that rudeness.

The technology is a long way from reaching robots you can interact with. For one thing, these machines would need ‘heads’ or some other way of exploring the world on their own. They may be particularly clever when they arrive, though. The team is also exploring the possibility of teaching bipedal robots t coordinate in shared actions, such as carrying a table. You could eventually see moving robots that have no problems hauling your couch down a flight of stairs.

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Can the US Military Re-Invent the Microchip for the AI Era?

Trying to outrun the expiration of Moore’s Law.


As conventional microchip design reaches its limits, DARPA is pouring money into the specialty chips that might power tomorrow’s autonomous machines.

The coming AI revolution faces a big hurdle: today’s microchips.

It’s one thing to get a bunch of transistors on an integrated circuit to crunch numbers, even very large ones. But what the brain does is far more difficult. Processing vast amounts of visual data for use by huge, multi-cellular organism is very different from the narrow calculations of conventional math. The algorithms that will drive tomorrow’s autonomous cars, planes, and programs will be incredibly data-intensive, with needs well beyond what conventional chips were ever designed for. This is one reason for the hype surrounding quantum computing and neurosynaptic chips.

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Facebook opens an A.I. research outpost in Canada

In addition to opening the lab, Facebook has committed about $5.75 million to support AI research at McGill, the University of Montreal, the Montreal Institute of Learning Algorithms and the Canadian Institute for Advanced Research, the company said in a Facebook blog post on Friday. Alphabet and Microsoft also have invested in AI at McGill and the University of Montreal.

The move comes a week after IBM said it would spend $240 million on a new AI lab in partnership with the Massachusetts Institute of Technology.


Facebook will support Canadian AI research in addition to setting up a lab in Montreal.

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Robots will become smarter than humans by 2029, says HP Chief Technology Officer Shane Wall

The ‘singularity’ event that scientists talk about in artificial intelligence (AI) — when robots would outsmart human beings in reasoning — has just been moved up, according to a top scientist at HP Inc. The progress in AI and machine learning has been so rapid that scientists have upped the estimate for the ‘singularity’ to happen in 2029 from 2040, shaving off 11 years of development time, says Shane Wall, Chief Technology Officer at HP, who also heads the HP Labs which is at the centre of innovation within the company.

Wall, who was speaking at the HP Reinvent Partner Forum here, said there may be some who watch with fear for that event to happen but taken adequate precautions, this change would bring in much good for everyone — be it in manufacturing, health, innovation or elsewhere. He said AI handles huge amount of data and can discern patterns to take decisions. “Machine learning uses AI and big data to learn and it can find things that no humans can see,” Wall noted.

According to him, already there are massive data farms which are crunching big numbers and there are research labs and companies where machines are taught how to use data to managing things around us. Wall, who joined HP over a decade ago, drives the company’s technology vision and its strategy and helms the innovation community within. According to him, machines have become smart enough to predict failures within a system and 3D manufacturing is a massive revolution in the making. “Already, 3D printing is handling intricate products and in the future this will bring about a disruptive change,” Wall said.

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Artificial Intelligence: The Quest for Universal Beauty Could Also Help Aging Research

Humanity has been attempting to measure and assess beauty long before anyone even knew about computers and algorithms. Surprisingly, a new technology may help to solve the ancient question that mankind has struggled to answer: what is universal beauty? And perhaps even more intriguingly, it might help us in aging research.

Leonardo da Vinci attempted to capture the essence of beauty in his famous drawing, Vitruvian Man, through the use of geometrically equal proportions. This drawing was based on the writing of Roman architect Vitruvius in his treatise De Architectura.

According to Plato, beauty was an idea or form of which beautiful things were a consequence. He suggested that beauty was found when the sum of parts became a harmonious whole.

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AutoX demonstrates how to build an autonomous car without expensive laser sensors

Jianxiong Xiao aims to make self-driving cars as widely accessible as computers are today. He’s the founder and CEO of AutoX, which recently demonstrated an autonomous car built not with expensive laser sensors but with ordinary webcams and some sophisticated computer-vision algorithms. Remarkably, the vehicle can navigate even at night and in bad weather.

AutoX hasn’t revealed details of its software, but Xiao is an expert at using deep learning, an AI technique that lets machines teach themselves to perform difficult tasks such as recognizing pedestrians from different angles and in different lighting.

Growing up without much money in Chaozhou, a city in eastern China, Xiao became mesmerized by books about computers—fantastic-sounding machines that could encode knowledge, logic, and reason. Without access to the real thing, he taught himself to touch-type on a keyboard drawn on paper.

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