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US Army Researchers Creating Robot Tech Directly Inspired by T-1000 Villain from “Terminator 2”

By Elias Marat

Researchers for the U.S. Army are hoping to formulate a new shape-shifting material that can heal itself on its own in hopes to achieve the kind of futuristic killing technology famously depicted in the 1991 science-fiction film, Terminator 2.

In fact, the film’s villain, the T-1000, directly provided the inspiration to one of the Army engineers working on a project to develop “soft robotic” drones and unmanned aircraft based on flexible, self-repairing and self-reconfiguring materials, reports Military.com.

The robot revolution has arrived

Even before the COVID crisis added its impetus, technological trends were accelerating the creation of robots that could fan out into our lives. Mechanical parts got lighter, cheaper, and sturdier. Electronics packed more computing power into smaller packages. Breakthroughs let engineers put powerful data-crunching tools into robot bodies. Better digital communications let them keep some robot “brains” in a computer elsewhere—or connect a simple robot to hundreds of others, letting them share a collective intelligence, like a beehive’s.


Machines now perform all sorts of tasks: They clean big stores, patrol borders, and help autistic children. But will they make life better for humans?

Artificial Intelligence Defeats Human F-16 Pilot In Virtual Dogfight

The plan in the next big war will probably be to let waves of AI fighters wipe out all the enemies targets, Anti aircraft systems, enemy fighters, enemy air fields etc…, however many waves that takes. And, then human pilots come in behind that.


An artificial intelligence algorithm defeated a human F-16 fighter pilot in a virtual dogfight sponsored by the Defense Advanced Research Projects Agency Thursday.

Virtual Event: Cracking Covid-19’s Code with AI

Editor’s note: A recording of this virtual event is embedded above.

Artificial intelligence is proving a potent weapon against the pandemic, enabling researchers to comb through massive data sets to understand the virus and how to combat it. From drug development to immune response, STAT’s Casey Ross will talk to researchers and AI experts about how AI is accelerating a worldwide effort to crack Covid-19’s molecular code.

Featured Speakers:

Deep learning will help future Mars rovers go farther, faster, and do more science

NASA’s Mars rovers have been one of the great scientific and space successes of the past two decades.

Four generations of rovers have traversed the red planet gathering , sending back evocative photographs, and surviving incredibly harsh conditions—all using on-board computers less powerful than an iPhone 1. The latest , Perseverance, was launched on July 30, 2020, and engineers are already dreaming of a future generation of rovers.

While a major achievement, these missions have only scratched the surface (literally and figuratively) of the planet and its geology, geography, and atmosphere.

Philosophers Win Artificial Intelligence Award

The Tetrad Automated Causal Discovery Platform, a software and text project developed by Peter Spirtes, Clark Glymour, Richard Scheines and Joe Ramsey of Carnegie Mellon University’s Department of Philosophy, earned the “Leader” Award at the 2020 World Artificial Intelligence Conference this past July.

The Leader Award is one of four awards presented at the conference that aim to recognize “the best in terms of impact and innovation in AI”. There were over 800 nominees for the awards, including projects by Amazon, Bosch, Huawei, Nvidia, Open AI Lab, and Siemens, among others.

AI automatic tuning delivers step forward in quantum computing

Researchers at Oxford University, in collaboration with DeepMind, University of Basel and Lancaster University, have created a machine learning algorithm that interfaces with a quantum device and ‘tunes’ it faster than human experts, without any human input. They are dubbing it “Minecraft explorer for quantum devices.”

Classical computers are composed of billions of transistors, which together can perform complex calculations. Small imperfections in these transistors arise during manufacturing, but do not usually affect the operation of the computer. However, in a quantum computer similar imperfections can strongly affect its behavior.

In prototype semiconductor quantum computers, the standard way to correct these imperfections is by adjusting input voltages to cancel them out. This process is known as tuning. However, identifying the right combination of voltage adjustments needs a lot of time even for a single quantum . This makes it virtually impossible for the billions of devices required to build a useful general-purpose quantum computer.

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