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Watch the first AI vs. human dogfight using military jets

For the first time, an AI fighter pilot faced off against a human pilot in a “dogfight” using actual jets in the air — a huge milestone in autonomous flight and military automation.

“The X-62A team demonstrated that cutting-edge machine learning-based autonomy could be safely used to fly dynamic combat maneuvers,” said Frank Kendall, secretary of the Air Force. “The team accomplished this while complying with American norms for safe and ethical use of autonomous technology.”

The challenge: AI-controlled planes could be a boon to the military. Not only could they reduce injuries and accidents to pilots, AIs also have the potential to rapidly analyze a lot of data — allowing for more informed decisions, more quickly.

The science of static shock jolted into the 21st century

Now Princeton researchers have sparked new life into static. Using millions of hours of computational time to run detailed simulations, the researchers found a way to describe static charge atom-by-atom with the mathematics of heat and work. Their paper appeared in Nature Communications on March 23.

The study looked specifically at how charge moves between materials that do not allow the free flow of electrons, called insulating materials, such as vinyl and acrylic. The researchers said there is no established view on what mechanisms drive these jolts, despite the ubiquity of static: the crackle and pop of clothes pulled from a dryer, packing peanuts that cling to a box.

“We know it’s not electrons,” said Mike Webb, assistant professor of chemical and biological engineering, who led the study. “What is it?”

Tesla Megapack to power new massive record-breaking 1.3 GWh battery system

Tesla Megapack has been selected to power a massive new record-breaking 13 GWh battery system from Neoen in Australia.

This project is the second stage of the Collie Battery project, which is named after a town in Western Australia where the project is located.

As we previously reported, France’s Neoen is already building the first stage with Tesla Megapack 2XLs.

Machine learning and theory

Theoretical physicists employ their imaginations and their deep understanding of mathematics to decipher the underlying laws of the universe that govern particles, forces and everything in between. More and more often, theorists are doing that work with the help of machine learning.

As might be expected, the group of theorists using machine learning includes people classified as “computational” theorists. But it also includes “formal” theorists, the people interested in the self-consistency of theoretical frameworks, like string theory or quantum gravity. And it includes “phenomenologists,” the theorists who sit next to experimentalists, hypothesizing about new particles or interactions that could be tested by experiments; analyzing the data the experiments collect; and using results to construct new models and dream up how to test them experimentally.

In all areas of theory, machine-learning algorithms are speeding up processes, performing previously impossible calculations, and even causing theorists to rethink the way theoretical physics research is done.

New AI Circuitry That Mimics Human Brains Makes Models Smarter

A new kind of transistor allows AI hardware to remember and process information more like the human brain does.

By Anna Mattson

Artificial intelligence and human thought both run on electricity, but that’s about where the physical similarities end. AI’s output arises from silicon and metal circuitry; human cognition arises from a mass of living tissue. And the architectures of these systems are fundamentally different, too. Conventional computers store and compute information in distinct parts of the hardware, shuttling data back and forth between memory and microprocessor. The human brain, on the other hand, entangles memory with processing, helping to make it more efficient.