Lifeboat Foundation

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.

From the understated opulence of a Bentley to the stalwart family minivan to the utilitarian pickup, Americans know that the car you drive is an outward statement of personality. You are what you drive, as the saying goes, and researchers at Stanford have just taken that maxim to a new level.

Using computer algorithms that can see and learn, they have analyzed millions of publicly available images on Google Street View. The researchers say they can use that knowledge to determine the political leanings of a given neighborhood just by looking at the cars on the streets.

“Using easily obtainable visual data, we can learn so much about our communities, on par with some information that takes billions of dollars to obtain via census surveys. More importantly, this research opens up more possibilities of virtually continuous study of our society using sometimes cheaply available visual data,” said Fei-Fei Li, an associate professor of computer science at Stanford and director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab, where the work was done.

Scientists have developed the most accurate computing method to date to reconstruct the patchwork of genetic faults within tumors and their history during disease development, in new research funded by Cancer Research UK and published in Nature Genetics.

Their powerful approach combines artificial intelligence with the mathematical models of Charles Darwin’s theory of evolution to analyze genetic data more accurately than ever before, paving the way for a fundamental shift in how cancer’s genetic diversity is used to deliver tailored treatments to patients.

Applying these new algorithms to DNA data taken from patient samples revealed that tumors had a simpler genetic structure than previously thought. The algorithms showed that tumors had fewer distinct subpopulations of cells, called “subclones,” than previously suggested. The scientists, based at The Institute of Cancer Research, London, and Queen Mary University of London, could also tell how old each subclone was and how fast it was growing.

An artificial intelligence algorithm can detect subtle differences in the way people with Alzheimer’s use language.