Lifeboat Foundation

Circa 2020 awesome 😃

Now, in a world’s first, Daniel Robinson, a veteran F-22 pilot, climbed inside a real aircraft and battled an AI virtual fighter.

These virtual war games open up new doors for training in the U.S. military. Often, the only way to train airmen is with real pilots who oppose them in air-to-air combat training. The U.S. military is increasingly relying on contractors to provide “red air” adversary support. But physically flying against adversary aircraft pilots is costly and inefficient. Earlier this year, the Air Force hired several companies — in a multi-billion dollar contract — to get the support they needed to help pilots train across the U.S.

The human brain has always been under study for inspiration of computing systems. Although there’s a very long way to go until we can achieve a computing system that matches the efficiency of the human brain for cognitive tasks, several brain-inspired computing paradigms are being researched. Convolutional neural networks are a widely used machine learning approach for AI-related applications due to their significant performance relative to rules-based or symbolic approaches. Nonetheless, for many tasks machine learning requires vast amounts of data and training to converge to an acceptable level of performance.

A Ph.D. student from Khalifa University, Eman Hasan, is investigating another AI computation methodology called ‘hyperdimensional computing, which can possibly take AI systems a step closer toward human-like cognition. The work is supervised by Dr. Baker Mohammad, Associate Professor and Director of the System on Chip Center (SOCC), and Dr. Yasmin Halawani, Postdoctoral Fellow.

Hasan’s work, which was published recently in the journal IEEE Access, analyzes different models of hyperdimensional computing and highlights the advantages of this computing paradigm. Hyperdimensional computing, or HDC, is a relatively new paradigm for computing using large vectors (like 10000 bits each) and is inspired by patterns of neural activity in the human brain. The means by which can allow AI-based computing systems to retain memory can reduce their computing and power demands.

Recently, scientists designed an AI agent that learns 60% faster than its peers by combining quantum and classical computing. 📈

This week, an international collaboration led by Dr. Philip Walther at the University of Vienna took the “classic” concept of reinforcement learning and gave it a quantum spin. They designed a hybrid AI that relies on both quantum and run-of-the-mill classic computing, and showed that—thanks to quantum quirkiness—it could simultaneously screen a handful of different ways to solve a problem.

The result is a reinforcement learning AI that learned over 60 percent faster than its non-quantum-enabled peers. This is one of the first tests that shows adding quantum computing can speed up the actual learning process of an AI agent, the authors explained.

Continue reading “Quantum Computing and Reinforcement Learning Are Joining Forces to Make Faster AI” »

In its preparation for great power competition, the US military is modernizing its artificial intelligence and machine learning techniques and testing them in West Africa.

by Scott Timcke

Continue reading “West Africa is the Latest Testing Ground for US Military Artificial Intelligence” »

It’s likely that safety drivers will remain in cabs for years to come as companies hone their sensor technology and train their software for every highway scenario. It’s expensive and painstaking work that can overwhelm even the best-run start-ups. The consensus within the industry is that three contestants stand the best chance to make it to the finish line: “It’s TuSimple, Aurora and Waymo,” says Grayson Brulte, co-founder of Brulte & Co., a consulting firm focused on transportation. TuSimple, a San Diego based-company that raised $1.35 billion in an initial public offering in April, is in the pole position, as Brulte sees it, because of its singular focus on trucking and its partnership, begun three years ago, with Navistar International to build autonomous trucks. “They’ve got the head start on it,” says Brulte.

These are the companies set to dominate the highways of tomorrow.

Continue reading “The Race to Build Self-Driving Trucks Has Four Horses and Three Jockeys” »

Fully autonomous exploration and mapping of the unknown is a cutting-edge capability for commercial drones.

Drone autonomy is getting more and more impressive, but we’re starting to get to the point where it’s getting significantly more difficult to improve on existing capabilities. Companies like Skydio are selling (for cheap!) commercial drones that have no problem dynamically path planning around obstacles at high speeds while tracking you, which is pretty amazing, and it can also autonomously create 3D maps of structures. In both of these cases, there’s a human indirectly in the loop, either saying “follow me” or “map this specific thing.” In other words, the level of autonomous flight is very high, but there’s still some reliance on a human for high-level planning. Which, for what Skydio is doing, is totally fine and the right way to do it.

Continue reading “Exyn Brings Level 4 Autonomy to Drones” »

The silicon Ouroboros.

TSMC produces chips for AMD, but it also now uses AMD’s processors to control the equipment that it uses to make chips for AMD (and other clients too). Sounds like a weird circulation of silicon, but that’s exactly what happens behind the scenes at the world’s largest third-party foundry.

There are hundreds of companies that use AMD EPYC-based machines for their important workloads, sometimes business-critical workloads. Yet, when it comes to mission-critical work, Intel Xeon (and even Intel Itanium and mainframes) rule the world. Luckily for AMD, things have begun to change, and TSMC has announced that it is now using EPYC-based servers for its mission-critical fab control operations.

Continue reading “TSMC Uses AMD’s EPYC Chips to Make Chips” »

Seems very biased, but worth a glance.

“Misfired” neurons might be a brain feature, not a bug — and that’s something AI research can’t take into account.

SpaceX and NASA are targeting Saturday, May 1 at 8:35 p.m. EDT, or 00:35 UTC on May 2, for Dragon to autonomously undock from the International Space Station (ISS) and splashdown off the coast of Florida on Sunday, May 2 at approximately 2:57 a.m. EDT, 6:57 UTC, completing its first six-month operational mission to the Station.

A series of departure burns will move Dragon away from the orbiting laboratory, followed by the vehicle jettisoning the trunk to reduce weight and mass to help save propellant for the deorbit burn. Once complete, Dragon will re-enter Earth’s atmosphere and deploy its two drogue and four main parachutes in preparation for a soft water landing.

Continue reading “Crew-1 Mission | Return” »