Lifeboat Foundation

A team of researchers affiliated with a host of institutions in Korea and one in Estonia has found a way to use math to study paintings to learn more about the evolution of art history in the western world. In their paper published in Proceedings of the National Academy of Sciences, the group describes how they scanned thousands of paintings and then used mathematical algorithms to find commonalities between them over time.

Beauty, as the saying goes, is in the eye of the beholder—and so it is also with art. Two people looking at the same painting can walk away with vastly different impressions. But art also serves, the researchers contend, as a barometer for visualizing the emotional tone of a given society. This suggests that the study of art history can serve as a channel of sorts—illuminating societal trends over time. The researchers further note that to date, most studies of art history have been qualitatively based, which has led to interpretive results. To overcome such bias, the researchers with this new effort looked to mathematics to see if it might be useful in uncovering features of paintings that have been overlooked by human scholars.

The work involved digitally scanning 14,912 paintings—all of which (except for two) were painted by Western artists. The data for each of the paintings was then sent through a mathematical algorithm that drew partitions on the digital images based on contrasting colors. The researchers ran the algorithm on each painting multiple times, each time creating more partitions. As an example, the first run of the algorithm might have simply created two partitions on a painting—everything on land, and everything in the sky. The second might have split the land into buildings in one partition and farmland in another.

RACER to focus on new autonomy algorithm technologies.

The Solar Mapper uses artificial intelligence algorithms that compile data extracted from satellite images. It can estimate site solar potential and indicate the most suitable technology.

Humans are innately able to adapt their behavior and actions according to the movements of other humans in their surroundings. For instance, human drivers may suddenly stop, slow down, steer or start their car based on the actions of other drivers, pedestrians or cyclists, as they have a sense of which maneuvers are risky in specific scenarios.

However, developing robots and autonomous vehicles that can similarly predict human movements and assess the risk of performing different actions in a given scenario has so far proved highly challenging. This has resulted in a number of accidents, including the tragic death of a pedestrian who was struck by a self-driving Uber vehicle in March 2018.

Researchers at Stanford University and Toyota Research Institute (TRI) have recently developed a framework that could prevent these accidents in the future, increasing the safety of autonomous vehicles and other robotic systems operating in crowded environments. This framework, presented in a paper pre-published on arXiv, combines two tools, a machine learning algorithm and a technique to achieve risk-sensitive control.

Scientists at the University of Hawaii’s Mānoa Institute for Astronomy (IfA) have used AI to produce the world’s largest 3D catalog of stars, galaxies, and quasars.

The team developed the map using an optical survey of three-quarters of the sky produced by the Pan-STARRS observatory on Haleakalā, Maui.

They trained an algorithm to identify celestial objects in the survey by feeding it spectroscopic measurements that provide definitive object classifications and distances.

The blockchain revolution, online gaming and virtual reality are powerful new technologies that promise to change our online experience. After summarizing advances in these hot technologies, we use the collective intelligence of our TechCast Experts to forecast the coming Internet that is likely to emerge from their application.

Here’s what learned:

Security May Arrive About 2027 We found a sharp division of opinion, with roughly half of our experts thinking there is little or no chance that the Internet would become secure — and the other half thinks there is about a 60% probability that blockchain and quantum cryptography will solve the problem at about 2027. After noting the success of Gilder’s previous forecasts, we tend to accept those who agree with Gilder.

Decentralization Likely About 2028–2030 We find some consensus around a 60% Probability and Most Likely Year About 2028–2030. The critical technologies are thought to focus on blockchain, but quantum, AI, biometrics and the Internet of things (IoT) also thought to offer localizing capabilities.

Continue reading “The Coming Internet: Secure, Decentralized and Immersive” »

The U.S. Defense Intelligence Agency awarded nearly $800 million in contacts to two major defense contractors to improve data storage and network modernization.

The DIA, a military intelligence agency, chose Northrop Grumman to deliver its Transforming All-Source Analysis with Location-Based Object Services (TALOS) program, which focuses on building new big data systems. The contract is worth $690 million. A spokesperson for Northrop Grumman declined to provide the performance period.

The DIA made two awards to Northrop Grumman and GDIT.

Continue reading “DIA awards nearly $800 million in work to major defense primes” »

This could be important!

A new algorithm that fast forwards simulations could bring greater use ability to current and near-term quantum computers, opening the way for applications to run past strict time limits that hamper many quantum calculations.

“Quantum computers have a limited time to perform calculations before their useful quantum nature, which we call coherence, breaks down,” said Andrew Sornborger of the Computer, Computational, and Statistical Sciences division at Los Alamos National Laboratory, and senior author on a paper announcing the research. “With a new algorithm we have developed and tested, we will be able to fast forward quantum simulations to solve problems that were previously out of reach.”

Continue reading “New quantum computing algorithm skips past time limits imposed by decoherence” »

With artificial intelligence (AI) tools and machine learning algorithms now making their way into a wide variety of settings, assessing their security and ensuring that they are protected against cyberattacks is of utmost importance. As most AI algorithms and models are trained on large online datasets and third-party databases, they are vulnerable to a variety of attacks, including neural Trojan attacks.

A neural Trojan attack occurs when an attacker inserts what is known as a hidden Trojan trigger or backdoor inside an AI model during its training. This trigger allows the attacker to hijack the model’s prediction at a later stage, causing it to classify data incorrectly. Detecting these attacks and mitigating their impact can be very challenging, as a targeted model typically performs well and in alignment with a developer’s expectations until the Trojan backdoor is activated.

Researchers at University of California, San Diego have recently created CLEANN, an end-to-end framework designed to protect embedded artificial neural networks from Trojan attacks. This framework, presented in a paper pre-published on arXiv and set to be presented at the 2020 IEEE/ACM International Conference on Computer-Aided Design, was found to perform better than previously developed Trojan shields and detection methods.