Lifeboat Foundation

Computer programmers may soon design the ultimate program: A program that designs programs.

Last week, a team led by Justin Gottschlich, director of the machine programming research group at Intel, announced the creation of a new machine learning system that designs its own code. They call the system MISIM, Machine Inferred Code Similarity.

Gottschlich explained, “Intel’s ultimate goal for machine programming is to democratize the creation of software. When fully realized, machine programming will enable everyone to create software by expressing their intention in whatever fashion that’s best for them, whether that’s code, natural language or something else. That’s an audacious goal, and while there’s much more work to be done, MISIM is a solid step toward it.”

A recently deployed DARPA CubeSat seeks to demonstrate technology that could improve imaging of distant objects in space and allow powerful space telescopes to fit into small satellites. DARPA’s Deformable Mirror (DeMi) CubeSat deployed from the International Space Station July 13, beginning the technology demonstration of a miniature space telescope with a small deformable mirror called a microelectromechanical systems (MEMS) mirror.

DeMi made first contact about a week following launch, demonstrating the expected power from its solar arrays, as well as correct spacecraft pointing and stable temperatures. The team will focus on payload checkout over the coming days.

Deformable mirrors can adjust the shape of their reflective surfaces to correct for the effects of temperature and mechanical changes on a space telescope, improving image quality. The experiment will measure how well a MEMS deformable mirror performs in space, from the rocket launch through its time in orbit experiencing the thermal and radiation environment.

DARPA’s Subterranean (SubT) Challenge focuses on discovering innovative approaches to map, navigate, and search complex underground environments across three diverse subdomains: human-made tunnels, urban underground, and natural cave systems. Two previous scored events – Tunnel and Urban Circuits – featured both Virtual and Systems Competitions. DARPA has made the difficult decision to proceed only with the Virtual Competition for the Cave Circuit, due to safety considerations surrounding COVID-19. The date for the Cave Circuit Virtual Competition webcast/public event will be announced in the coming weeks.

Teams must qualify by a September 15 deadline to participate in the Cave Circuit Virtual Competition, which includes team registration and registration on the SubT Challenge Virtual Portal. Additional details are available in the SubT Qualification Guide available on the program’s Resources Page. Interested teams also are encouraged to join the SubT Community Forum, where they can engage with other participants and ask any questions.

“We recognize and share the teams’ passion to compete and showcase the hard work they have completed since the Urban Circuit, and we also are committed to the safety of the global community and extended SubT Challenge family,” said Dr. Timothy Chung, program manager for the SubT Challenge in DARPA’s Tactical Technology Office. “Additionally, I know a significant aspect of the SubT Challenge is the opportunity to invite the public to experience the camaraderie and competition unique to DARPA challenges. We look forward to providing greater insight into the Virtual Competition Cave Circuit via an enhanced webcast and online experience, and offering additional opportunities to experience the SubT Challenge during the Final Event.”

DARPA has selected three performers to work on the Control of Revolutionary Aircraft with Novel Effectors (CRANE) program, which aims to demonstrate an aircraft design based on active flow control (AFC), an area not fully explored compared to traditional flight controls. The goal is to demonstrate significant efficiency benefits of AFC, as well as improvements in aircraft cost, weight, performance, and reliability.

“The performers are looking at using active flow control very early in the design scope. That’s the differentiating piece that hasn’t been done before,” said Alexander Walan, the program manager for CRANE in DARPA’s Tactical Technology Office. “AFC has been explored at a component level, but not as an integral piece of aircraft design. By altering the design approach, CRANE seeks to maximize the chance of a successful X-plane development while also integrating AFC into the aircraft’s stability and control.”

The program is kicking off Phase 0, a long conceptual design phase to give performers time to evaluate flow control options before solidifying their demonstration approaches. The performers selected for Phase 0 are:

Eyeing a launch in 2023, DARPA’s Robotic Servicing of Geosynchronous Satellites (RSGS) program will focus the remainder of this year on completing the elements of the robotic payload. The objective of RSGS is to create an operational dexterous robotic capability to repair satellites in geosynchronous Earth orbit (GEO), extending satellite life spans, enhancing resilience, and improving reliability for the current U.S. space infrastructure.

Earlier this year, DARPA partnered with Space Logistics LLC, a wholly owned subsidiary of Northrop Grumman, to provide the spacecraft bus, launch, and operations of the integrated spacecraft. DARPA will provide the payload that flies on the bus, including the robotic arms, through an agreement with the U.S. Naval Research Laboratory (NRL).

In 2021, NRL will integrate the robotic arms onto the payload structure, and then is expected to begin environmental tests by the end of same year. After launch in 2023, it will take approximately nine months to reach GEO, and the program anticipates servicing satellites in mid-2024.

Two California companies were selected for DARPA’s Gamma Ray Inspection Technology (GRIT) program and have begun work to develop a transportable, tunable source of gamma rays for a host of national security, industrial, and medical applications.

Lumitron Technologies and RadiaBeam Technologies started work on the GRIT program in April and are exploring novel approaches to achieve high-intensity, tunable, and narrow-bandwidth sources of gamma ray radiation in a compact, transportable form factor.

GRIT aims to provide a source of tunable, pure x-rays and gamma rays from tens of keV (kilo-electron volts) up through three MeV (mega-electron volts). Currently, tunable and narrow bandwidth gamma ray sources only exist at highly specialized user facilities best suited for basic research and are not able to support broad practical applications. Shrinking these photon sources to a transportable system is the major goal and challenge of the GRIT program.

Despite China’s considerable strides, industry analysts expect America to retain its current AI lead for another decade at least. But this is cold comfort: China is already developing powerful new surveillance tools, and exporting them to dozens of the world’s actual and would-be autocracies. Over the next few years, those technologies will be refined and integrated into all-encompassing surveillance systems that dictators can plug and play.

Xi Jinping is using artificial intelligence to enhance his government’s totalitarian control—and he’s exporting this technology to regimes around the globe.

The advent of DNA sequencing has given scientists a clearer insight into the interconnectedness of evolution and the web-like path that different organisms take, splitting apart and coming back together. Tony Capra, associate professor of biological sciences, has come to new conclusions about the influence of Neanderthal DNA on some genetic traits of modern humans.

The article “Neanderthal introgression reintroduced functional ancestral alleles lost in Eurasian populations” was published in the journal Nature Ecology & Evolution on July 27.

The ancestors of all modern humans lived across the African continent, until approximately 100,000 years ago when a subset of humans decided to venture further afield. Neanderthals, an extinct relative of modern humans, had been longtime residents of Europe and central and south Asia; their ancestors had already migrated there 700,000 years previously. The humans who moved into central Asia and the Middle East encountered and reproduced with Neanderthals. Neanderthal DNA is present in some modern humans, and now research shows that can sometimes be a good thing.

A quintillion calculations a second. That’s one with 18 zeros after it. It’s the speed at which an exascale supercomputer will process information. The Department of Energy (DOE) is preparing for the first exascale computer to be deployed in 2021. Two more will follow soon after. Yet quantum computers may be able to complete more complex calculations even faster than these up-and-coming exascale computers. But these technologies complement each other much more than they compete.

It’s going to be a while before quantum computers are ready to tackle major scientific research questions. While quantum researchers and scientists in other areas are collaborating to design quantum computers to be as effective as possible once they’re ready, that’s still a long way off. Scientists are figuring out how to build qubits for quantum computers, the very foundation of the technology. They’re establishing the most fundamental quantum algorithms that they need to do simple calculations. The hardware and algorithms need to be far enough along for coders to develop operating systems and software to do scientific research. Currently, we’re at the same point in quantum computing that scientists in the 1950s were with computers that ran on vacuum tubes. Most of us regularly carry computers in our pockets now, but it took decades to get to this level of accessibility.

In contrast, exascale computers will be ready next year. When they launch, they’ll already be five times faster than our fastest computer —Summit, at Oak Ridge National Laboratory’s Leadership Computing Facility, a DOE Office of Science user facility. Right away, they’ll be able to tackle major challenges in modeling Earth systems, analyzing genes, tracking barriers to fusion, and more. These powerful machines will allow scientists to include more variables in their equations and improve models’ accuracy. As long as we can find new ways to improve conventional computers, we’ll do it.