Lifeboat Foundation

“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.

Single-objective light sheet fluorescence microscopes are driving innovation in volumetric imaging.

Polaron formation in perovskite solar cells has been indicated in scientific research as a possible factor for making this kind of cell particularly efficient, although the mechanism behind polarons’ action is completely unknown. A U.S. research group has now observed how polaron distortions form and grow.

In a new study published in the journal Nature Medicine, individuals with brown fat had lower prevalences of cardiometabolic diseases, and the presence of brown fat was independently correlated with lower odds of type 2 diabetes, dyslipidemia, coronary artery disease, cerebrovascular disease, congestive heart failure and hypertension.

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.