Lifeboat Foundation

Researchers at Paderborn University have developed a new method of distance measurement for systems such as GPS, which achieves more precise results than ever before. Using quantum physics, the team led by Leibniz Prize winner Professor Christine Silberhorn has successfully overcome the so-called resolution limit, which causes the ‘noise’ we may see in photos, for example. Their findings have just been published in the academic journal Physical Review X Quantum (PRX Quantum).

Physicist Dr. Benjamin Brecht explains the problem of the resolution limit: “In laser distance measurements a detector registers two light pulses of different intensities with a time difference. The more precise the time measurement is, the more accurately the distance can be determined. Providing the time separation between the pulses is greater than the length of the pulses, this works well.” Problems arise, however, as Brecht explains, if the pulses overlap: “Then you can no longer measure the time difference using conventional methods. This is known as the ‘resolution limit’ and is a well-known effect in photos. Very small structures or textures can no longer be resolved. That’s the same problem—just with position rather than time.”

A further challenge, according to Brecht, is to determine the different intensities of two light pulses, simultaneously with their time difference and the arrival time. But this is exactly what the researchers have managed to do—” with quantum-limited precision,” adds Brecht. Working with partners from the Czech Republic and Spain, the Paderborn physicists were even able to measure these values when the pulses overlapped by 90 per cent. Brecht says: “This is far beyond the resolution limit. The precision of the measurement is 10000 times better. Using methods from quantum information theory, we can find new forms of measurement which overcome the limitations of established methods.”

Frias-Martinez says CloudBank has allowed her to stretch her research dollars and, as a result, improve the quality and scope of her analyses. “For example, we started to do some experiments with an AWS database and the costs were much higher than we had expected,” she explains. “We submitted a ticket to their helpdesk and they quickly responded” with a full explanation of expenses and some money-saving alternatives.

Going the last mile

CloudBank was created to serve NSF grantees, starting with those funded by select CISE programs who have requested cloud computing. That pool is now tiny by design, but Norman expects demand to increase rapidly once NSF begins to make awards from this year’s program solicitations, the first that include CloudBank as an option. CloudBank could also serve as a template for a far larger, national cloud computing resource, part of a massive scale-up in cloud computing and artificial intelligence outlined in a law passed by Congress last week.

Multi-domain operations, the Army’s future operating concept, requires autonomous agents with learning components to operate alongside the warfighter. New Army research reduces the unpredictability of current training reinforcement learning policies so that they are more practically applicable to physical systems, especially ground robots.

These learning components will permit autonomous agents to reason and adapt to changing battlefield conditions, said Army researcher Dr. Alec Koppel from the U.S. Army Combat Capabilities Development Command, now known as DEVCOM, Army Research Laboratory.

The underlying adaptation and re-planning mechanism consists of reinforcement learning-based policies. Making these policies efficiently obtainable is critical to making the MDO operating concept a reality, he said.

The SN9 vehicle’s three engines lit up for about one second today (Jan. 6) at 5:07 p.m. EST (2200 GMT) during a static-fire test at SpaceX’s South Texas facilities, near the Gulf Coast village of Boca Chica.

Static fires, in which rocket engines blaze while a vehicle remains anchored to the ground, are a routine preflight checkout. And SN9 (“Serial No. 9”) will indeed get off the ground soon, if all goes according to plan: SpaceX is prepping the vehicle for a test flight that’s expected to be similar to the epic one made last month by its predecessor.

Chinese scientists have established the world’s first integrated quantum communication network, combining over 700 optical fibers on the ground with two ground-to-satellite links to achieve quantum key distribution over a total distance of 4600 kilometers for users across the country. The team, led by Jianwei Pan, Yuao Chen, Chengzhi Peng from the University of Science and Technology of China in Hefei, reported in Nature their latest advances towards the global, practical application of such a network for future communications.

Unlike conventional encryption, quantum communication is considered unhackable and therefore the future of secure information transfer for banks, power grids and other sectors. The core of quantum communication is quantum key distribution (QKD), which uses the quantum states of particles—e.g. photons—to form a string of zeros and ones, while any eavesdropping between the sender and the receiver will change this string or key and be noticed immediately. So far, the most common QKD technology uses optical fibers for transmissions over several hundred kilometers, with high stability but considerable channel loss. Another major QKD technology uses the free space between satellites and ground stations for thousand-kilometer-level transmissions. In 2016, China launched the world’s first quantum communication satellite (QUESS, or Mozi/Micius) and achieved QKD with two ground stations which are 2600 km apart.

“I can feel touching my daughter’s hand or touching my wife’s hand, or picking up a hollow eggshell without crushing it,” Anderson says of his work with Psyonic, a startup operating out of the University of Illinois’ Research Park, in Urbana-Champaign. Psyonic expects to provide commercial prostheses with pressure sensing next year, and ones with sensory feedback sometime after that.

Technology is on the threshold of turning the unthinkable into reality. Awkward, unfeeling prostheses are morphing into mind-controlled extensions of the human body that give their wearers a sense of touch and a greater range of motion.

Along with sensory feedback, Psyonic’s rubber and silicone prosthesis uses machine learning to give its wearers intuitive control. The Modular Prosthetic Limb from Johns Hopkins University promises to deliver “humanlike” strength, thought-controlled dexterity and sensation. It’s currently in the research phase. And Icelandic company Ossur is conducting preclinical trials on mind-controlled leg and foot prostheses. These and other advances could make it dramatically easier for amputees to perform the sorts of tasks most people take for granted.

“Reverse osmosis membranes are widely used for cleaning water, but there’s still a lot we don’t know about them,” said in a statement Manish Kumar, an associate professor in the Department of Civil, Architectural and Environmental Engineering at UT Austin, who co-led the research. “We couldn’t really say how water moves through them, so all the improvements over the past 40 years have essentially been done in the dark.”

The researchers discovered that the problem with desalination membranes was that they were inconsistent in density and mass distribution. By giving the membranes a uniform density at the nanoscale, they were able to improve their performance.

The researchers’ new membranes are 30% to 40% more efficient, requiring less energy to clean more water. Although more efficient than non-membrane desalination processes, reverse osmosis membranes still use plenty of energy, a problematic aspect the researchers are working on.

As we enter the next chapter of the digital age, data traffic continues to grow exponentially. To further enhance artificial intelligence and machine learning, computers will need the ability to process vast amounts of data as quickly and as efficiently as possible.

Conventional computing methods are not up to the task, but in looking for a solution, researchers have seen the light—literally.

Light-based processors, called photonic processors, enable computers to complete complex calculations at incredible speeds. New research published this week in the journal Nature examines the potential of photonic processors for artificial intelligence applications. The results demonstrate for the first time that these devices can process information rapidly and in parallel, something that today’s electronic chips cannot do.

A team of researchers at OpenAI, a San Francisco artificial intelligence development company, has added a new module to its GPT-3 autoregressive language model. Called DALL·E, the module excerpts text with multiple characteristics, analyzes it and then draws a picture based on what it believes was described. On their webpage describing the new module, the team at OpenAI describe it as “a simple decoder-only transformer” and note that they plan to provide more details about its architecture and how it can be used as they learn more about it themselves.

GPT-3 was developed by the company to demonstrate how far neural networks could take text processing and creation. It analyzes user-selected text and generates new text based on that input. For example, if a user types “tell me a story about a dog that saves a child in a fire,” GPT-3 can create such a story in a human-like way. The same input a second time results in the generation of another version of the story.

In this new effort, the researchers have extended this ability to graphics. A user types in a sentence and DALL·E attempts to generate what is described using graphics and other imagery. As an example, if a user types in “a dog with cat claws and a bird tail,” the system would produce a cartoon-looking image of a dog with such features—and not just one. It would produce a whole line of them, each created using slightly different interpretations of the original text.