Lifeboat Foundation

Re-shring video of China’s autonomous KFC truck. 😃

Craving fried chicken during quarantine?

This robot will help people disinfect rooms with UV lights.

😃

Germicidal irradiation.

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.

“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.

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.

By Victor Omondi

Trip Planning

With their AI approximation for psychedelic trips in place, the team says they can start to probe for similarities with how the human brain processes drugs, citing the structural similarity between neural nets and the human visual cortices.

“The process of generating natural images with deep neural networks can be perturbed in visually similar ways and may offer mechanistic insights into its biological counterpart — in addition to offering a tool to illustrate verbal reports of psychedelic experiences,” Schartner told PsyPost.