One of the most defining scientific discoveries in recent decades is the development of induced pluripotent stem cells, which lets scientists revert adult cells back into an embryonic-like blank state and then manipulating them to become a particular kind of tissue.
But now a new model could do away with this time-consuming process, taking out the middle step and directly programming cells to become whatever we want them to be.
A lobbying group representing top artificial-intelligence companies including Amazon.com Inc., Facebook Inc. and Google issued a warning to lawmakers on Tuesday: hands off our algorithms.
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 molecular behavior, 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.
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.
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.
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.
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.
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.
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.
