Lifeboat Foundation

Like a comic book come to life, researchers at Stanford University have developed a kind of X-ray vision—only without the X-rays. Working with hardware similar to what enables autonomous cars to “see” the world around them, the researchers enhanced their system with a highly efficient algorithm that can reconstruct three-dimensional hidden scenes based on the movement of individual particles of light, or photons. In tests, detailed in a paper published Sept. 9 in Nature Communications, their system successfully reconstructed shapes obscured by 1-inch-thick foam. To the human eye, it’s like seeing through walls.

“A lot of imaging techniques make images look a little bit better, a little bit less noisy, but this is really something where we make the invisible visible,” said Gordon Wetzstein, assistant professor of electrical engineering at Stanford and senior author of the paper. “This is really pushing the frontier of what may be possible with any kind of sensing system. It’s like superhuman vision.”

This technique complements other vision systems that can see through barriers on the microscopic scale —for applications in medicine—because it’s more focused on large-scale situations, such as navigating self-driving cars in fog or heavy rain and satellite imaging of the surface of Earth and other planets through hazy atmosphere.

A pair of Danish computer scientists have solved a longstanding mathematics puzzle that lay dormant for decades, after researchers failed to make substantial progress on it since the 1990s.

The abstract problem in question is part of what’s called graph theory, and specifically concerns the challenge of finding an algorithm to resolve the planarity of a dynamic graph. That might sound a bit daunting, so if your graph theory is a little rusty, there’s a much more fun and accessible way of thinking about the same inherent ideas.

Going as far back as 1913 – although the mathematical concepts can probably be traced back much further – a puzzle called the three utilities problem was published.

How do you calculate the coordinated movements of two robot arms so they can accurately guide a highly flexible tool? ETH researchers have integrated all aspects of the optimisation calculations into an algorithm. A hot-wire cutter will be used, among other things, to develop building blocks for a mortar-free structure.

A newborn moves its arms and hands largely in an undirected and random manner. It has to learn how to coordinate them step by step. Years of practice are required to master the finely balanced movements of a violinist or calligrapher. It is therefore no surprise that the advanced calculations for the optimal movement of two robot arms to guide a tool precisely involve extremely challenging optimisation tasks. The complexity also increases greatly when the tool itself is not rigid, but flexible in all directions and bends differently depending on its position and movement.

Continue reading “A robot that controls highly flexible tools” »

An alert pops up in your email: The latest spacecraft observations are ready. You now have 24 hours to scour 84 hours-worth of data, selecting the most promising split-second moments you can find. The data points you choose, depending on how you rank them, will download from the spacecraft in the highest possible resolution; researchers may spend months analyzing them. Everything else will be overwritten like it was never collected at all.

These are the stakes facing the Scientist in the Loop, one of the most important roles on the Magnetospheric Multiscale, or MMS, mission team. Seventy-three volunteers share the responsibility, working weeklong shifts at a time to ensure the very best data makes it to the ground. It takes a keen and meticulous eye, which is why it’s always been left to a carefully-trained human – at least until now.

A paper published recently in Frontiers in Astronomy and Space Sciences describes the first artificial intelligence algorithm to lend the Scientist in the Loop a (virtual) hand.

Artificial Intelligence research is making big strides. But in practice?

There are several buckets you can use to categorize AI, one of which is the BS bucket. Within, you’ll find simple statistical algorithms people have been using forever. But there’s another bucket of things that actually weren’t possible a decade ago.

“The vast majority of businesses are still in the early phases of collecting and using data. Most companies looking for data scientists are looking for people to collect, manage, and calculate basic statistics over normal business processes.”

Continue reading “AI in the enterprise: Prepare to be disappointed – oversold but under appreciated, it can help… just not too much” »

Amazon’s quantum computing service is currently good for learning about quantum computing and developing NISQ-regime quantum algorithms, but stay tuned.

Scientists on an experiment at the Large Hadron Collider see massive W particles emerging from collisions with electromagnetic fields. How can this happen?

The Large Hadron Collider plays with Albert Einstein’s famous equation, E = mc², to transform matter into energy and then back into different forms of matter. But on rare occasions, it can skip the first step and collide pure energy—in the form of electromagnetic waves.

Last year, the ATLAS experiment at the LHC observed two photons, particles of light, ricocheting off one another and producing two new photons. This year, they’ve taken that research a step further and discovered photons merging and transforming into something even more interesting: W bosons, particles that carry the weak force, which governs nuclear decay.

The emergence of artificial intelligence and machine learning techniques is changing the world dramatically with novel applications such as internet of things, autonomous vehicles, real-time imaging processing and big data analytics in healthcare. In 2020, the global data volume is estimated to reach 44 Zettabytes, and it will continue to grow beyond the current capacity of computing and storage devices. At the same time, the related electricity consumption will increase 15 times by 2030, swallowing 8% of the global energy demand. Therefore, reducing energy consumption and increasing speed of information storage technology is in urgent need.

Berkeley researchers led by HKU President Professor Xiang Zhang when he was in Berkeley, in collaboration with Professor Aaron Lindenberg’s team at Stanford University, invented a new data storage method: They make odd numbered layers slide relative to even-number layers in tungsten ditelluride, which is only 3nm thick. The arrangement of these atomic layers represents 0 and 1 for data storage. These researchers creatively make use of quantum geometry: Berry curvature, to read information out. Therefore, this material platform works ideally for memory, with independent ‘write’ and ‘read’ operation. The energy consumption using this novel data storage method can be over 100 times less than the traditional method.

This work is a conceptual innovation for non-volatile storage types and can potentially bring technological revolution. For the first time, the researchers prove that two-dimensional semi-metals, going beyond traditional silicon material, can be used for information storage and reading. This work was published in the latest issue of the journal Nature Physics^[1]. Compared with the existing non-volatile (NVW) memory, this new material platform is expected to increase storage speed by two orders and decrease energy cost by three orders, and it can greatly facilitate the realization of emerging in-memory computing and neural network computing.