Summary: A new machine-learning algorithm which videos of echocardiograms is able to accurately predict patients who will die within a year.
Source: Geisinger Health System
Circa 2013 o.o
Quantum entanglement, one of the odder aspects of quantum theory, links the properties of particles even when they are separated by large distances. When a property of one of a pair of entangled particles is measured, the other “immediately” settles down into a state compatible with that measurement. So how fast is “immediately”? According to research by Prof. Juan Yin and colleagues at the University of Science and Technology of China in Shanghai, the lower limit to the speed associated with entanglement dynamics – or “spooky action at a distance” – is at least 10000 times faster than light.
Despite playing a vital role in the development of quantum theory, Einstein felt philosophically at odds with its description of how the universe works. His famous quote that “God does not play dice” hints at his level of discomfort with the role of probability in quantum theory. He believed there exists another level of reality in which all of physics would be deterministic, and that quantum mechanics would turn out to be a description that emerges from the workings of that level – rather like a traffic jam emerges from the independent motions of a large number of cars.
Each year, billions of tons of goods are transported globally using cargo containers. Currently, there are concerns that this immense volume of traffic could be exploited to transport illicit nuclear materials, with little chance of detection. One promising approach to combating this issue is to measure how goods interact with charged particles named muons—which form naturally as cosmic rays interact with Earth’s atmosphere. Studies worldwide have now explored how this technique, named “muon tomography,” can be achieved through a variety of detection technologies and reconstruction algorithms. In this article of EPJ Plus, a team headed by Francesco Riggi at the University of Catania, Italy, build on these results to develop a full-scale muon tomograph prototype.
Attacks on vulnerable computer networks and cyber-infrastructure—often called zero-day attacks—can quickly overwhelm traditional defenses, resulting in billions of dollars of damage and requiring weeks of manual patching work to shore up the systems after the intrusion.
Computer-aided calculations have played a crucial part in producing the proofs of several high-profile results. And more recently, some mathematicians have made progress towards AI that doesn’t just perform repetitive calculations, but develops its own proofs. Another growing area has been software that can go over a mathematical proof written by humans and check that it is correct.
Algorithm named after mathematician Srinivasa Ramanujan suggests interesting formulae, some of which are difficult to prove true.
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Continue reading “Is Quantum Tunneling the Key to Life and existence of the Universe?” »
Amazon is getting involved in the start-up, Databricks, at a later stage than it usually does.
For all its comparisons to the human brain, AI still isn’t much like us. Maybe that’s alright. In the animal kingdom, brains come in all shapes and sizes. So, in a new machine learning approach, engineers did away with the human brain and all its beautiful complexity—turning instead to the brain of a lowly worm for inspiration.
Turns out, simplicity has its benefits. The resulting neural network is efficient, transparent, and here’s the kicker: It’s a lifelong learner.
Whereas most machine learning algorithms can’t hone their skills beyond an initial training period, the researchers say the new approach, called a liquid neural network, has a kind of built-in “neuroplasticity.” That is, as it goes about its work—say, in the future, maybe driving a car or directing a robot—it can learn from experience and adjust its connections on the fly.
Continue reading “New ‘Liquid’ AI Learns Continuously From Its Experience of the World” »
But the human eye can only see so much, even with the help of a microscope; despite embryologists’ efforts to select the “best” embryo, success rates are still relatively low. “Many decisions are based on gut feeling or personal experience,” said Embryonics founder and CEO Yael Gold-Zamir. “Even if you go to the same IVF center, two experts can give you different opinions on the same embryo.”
This is where Embryonics’ technology comes in. They used 8,789 time-lapse videos of developing embryos to train an algorithm that predicts the likelihood of successful embryo implantation. A little less than half of the embryos from the dataset were graded by embryologists, and implantation data was integrated when it was available (as a binary “successful” or “failed” metric).
The algorithm uses geometric deep learning, a technique that takes a traditional convolutional neural network—which filters input data to create maps of its features, and is most commonly used for image recognition—and applies it to more complex data like 3D objects and graphs. Within days after fertilization, the embryo is still at the blastocyst stage, essentially a microscopic clump of just 200–300 cells; the algorithm uses this deep learning technique to spot and identify patterns in embryo development that human embryologists either wouldn’t see at all, or would require massive collation of data to validate.
Continue reading “How an Israeli Startup Is Using AI to Help People Make Babies” »
