Lifeboat Foundation

Although universal fault-tolerant quantum computers – with millions of physical quantum bits (or qubits) – may be a decade or two away, quantum computing research continues apace. It has been hypothesized that quantum computers will one day revolutionize information processing across a host of military and civilian applications from pharmaceuticals discovery, to advanced batteries, to machine learning, to cryptography. A key missing element in the race toward fault-tolerant quantum systems, however, is meaningful metrics to quantify how useful or transformative large quantum computers will actually be once they exist.

To provide standards against which to measure quantum computing progress and drive current research toward specific goals, DARPA announced its Quantum Benchmarking program. Its aim is to re-invent key quantum computing metrics, make those metrics testable, and estimate the required quantum and classical resources needed to reach critical performance thresholds.

“It’s really about developing quantum computing yardsticks that can accurately measure what’s important to focus on in the race toward large, fault-tolerant quantum computers,” said Joe Altepeter, program manager in DARPA’s Defense Sciences Office. “Building a useful quantum computer is really hard, and it’s important to make sure we’re using the right metrics to guide our progress towards that goal. If building a useful quantum computer is like building the first rocket to the moon, we want to make sure we’re not quantifying progress toward that goal by measuring how high our planes can fly.”

Fueled by the need for faster life sciences and healthcare research, especially in the wake of the deadly COVID-19 pandemic, IBM and the 100-year-old Cleveland Clinic are partnering to bolster the Clinic’s research capabilities by integrating a wide range of IBM’s advanced technologies in quantum computing, AI and the cloud.

Access to IBM’s quantum systems has so far been primarily cloud-based, but IBM is providing the Cleveland Clinic with IBM’s first private-sector, on-premises quantum computer in the U.S. Scheduled for delivery next year, the initial IBM Quantum System One will harness between 50 to 100 qubits, according to IBM, but the goal is to stand up a more powerful, more advanced, next-generation 1000+ qubit quantum system at the Clinic as the project matures.

For the Cleveland Clinic, the 10-year partnership with IBM will add huge research capabilities and power as part of an all-new Discovery Center being created at the Clinic’s campus in Cleveland, Ohio. The Accelerator will serve as the technology foundation for the Clinic’s new Global Center for Pathogen Research & Human Health, which is being developed to drive research in areas including genomics, single-cell transcriptomics, population health, clinical applications and chemical and drug discovery, according to the Clinic.

TAE Technologies, the California, USA-based fusion energy technology company, has announced that its proprietary beam-driven field-reversed configuration (FRC) plasma generator has produced stable plasma at over 50 million degrees Celsius. The milestone has helped the company raise USD280 million in additional funding.

Norman — TAE’s USD150 million National Laboratory-scale device named after company founder, the late Norman Rostoker — was unveiled in May 2017 and reached first plasma in June of that year. The device achieved the latest milestone as part of a “well-choreographed sequence of campaigns” consisting of over 25000 fully-integrated fusion reactor core experiments. These experiments were optimised with the most advanced computing processes available, including machine learning from an ongoing collaboration with Google (which produced the Optometrist Algorithm) and processing power from the US Department of Energy’s INCITE programme that leverages exascale-level computing.

Plasma must be hot enough to enable sufficiently forceful collisions to cause fusion and sustain itself long enough to harness the power at will. These are known as the ‘hot enough’ and ‘long enough’ milestone. TAE said it had proved the ‘long enough’ component in 2015, after more than 100000 experiments. A year later, the company began building Norman, its fifth-generation device, to further test plasma temperature increases in pursuit of ‘hot enough’.

The question cryogenics raises is, can we freeze and then recover consciousness itself as opposed to simply saving imprints of a person’s memories as an AI?

Elon Musk finally got to show off his monkey.

Neuralink, a company founded by Musk that is developing artificial-intelligence-powered microchips to go in people’s brains, released a video Thursday appearing to show a macaque using the tech to play video games, including “Pong.”

Continue reading “Elon Musk’s brain-chip company, Neuralink, released a video of a monkey playing video games with its mind” »

NGAD is the Navy’s effort to replace the Super Hornet. Note: It’s a completely separate program from the Air Force’s own NGAD—which recently designed, tested, and flew a secret new fighter jet—and will produce a completely separate plane. The two aircraft will almost certainly be quite different, with the Air Force’s jet more optimized for air superiority. It’s likely the two fighters, developed roughly within the same time period, will share much of the same technology.

The U.S. Navy elaborated on its plans to replace the F/A-18E/F Super Hornet, saying the service’s next strike fighter will “most likely be manned.” The jet will probably fly alongside robotic allies, and remotely crewed aircraft could eventually account for six out of 10 planes on a carrier flight deck.

“As we look at it right now, the Next-Gen Air Dominance [NGAD] is a family of systems, which has as its centerpiece the F/A-XX—which may or may not be manned—platform. It’s the fixed-wing portion of the Next-Gen Air Dominance family of systems,” said Rear Adm. Gregory Harris, the head of the Chief of Naval Operations’ air warfare directorate, during a Navy League event.

Continue reading “The Navy Reveals Plans for Its New Fighter Jet” »

Rice University computer scientists have demonstrated artificial intelligence (AI) software that runs on commodity processors and trains deep neural networks 15 times faster than platforms based on graphics processors.

“The cost of training is the actual bottleneck in AI,” said Anshumali Shrivastava, an assistant professor of computer science at Rice’s Brown School of Engineering. “Companies are spending millions of dollars a week just to train and fine-tune their AI workloads.”

Shrivastava and collaborators from Rice and Intel will present research that addresses that bottleneck April 8 at the machine learning systems conference MLSys.

Automated matchmakers do what no human can.

Slick, viral videos from Boston Dynamics are impressive but teaching a robot to walk by itself is a lot harder.