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A study led by the University of Oxford has used the power of machine learning to overcome a key challenge affecting quantum devices. For the first time, the findings reveal a way to close the “reality gap”: the difference between predicted and observed behavior from quantum devices. The results have been published in Physical Review X.

Quantum computing could supercharge a wealth of applications, from climate modeling and financial forecasting to drug discovery and artificial intelligence. But this will require effective ways to scale and combine individual (also called qubits). A major barrier against this is inherent variability, where even apparently identical units exhibit different behaviors.

Functional variability is presumed to be caused by nanoscale imperfections in the materials from which quantum devices are made. Since there is no way to measure these directly, this internal disorder cannot be captured in simulations, leading to the gap in predicted and observed outcomes.

In recent years, engineers have developed a wide range of robotic systems that could soon assist humans with various everyday tasks. Rather than assisting with chores or other manual jobs, some of these robots could merely act as companions, helping older adults or individuals with different disabilities to practice skills that typically entail interacting with another human.

Researchers at Nara Institute of Science and Technology in Japan recently developed a new that can play video games with a human user. This robot, introduced in a paper presented at the 11th International Conference on Human-Agent Interaction, can play games with users while communicating with them.

“We have been developing robots that can chat while watching TV together, and interaction technology that creates empathy, in order to realize a partner robot that can live together with people in their daily life,” Masayuki Kanbara, one of the researchers who carried out the study, told Tech Xplore. “In this paper, we developed a robot that plays TV games together to provide opportunities for people to interact with the robot in their daily lives.”

Jan 9 (Reuters) — Microsoft (MSFT.O) has worked with a U.S. national laboratory to use artificial intelligence to rapidly identify a material that could mean producing batteries that require 70% less lithium than now, the company said on Tuesday.

The replacement of much of the lithium with sodium, a common element found in table salt, still needs extensive evaluation by scientists at Pacific Northwest National Laboratory (PNNL) in Richland, Washington to determine whether it will be suitable for mass production.

“Something that could have taken years, we did in two weeks,” Jason Zander, an executive vice president at Microsoft, told Reuters. “That’s the part we’re most excited about. … We just picked one problem. There are thousands of problems to go solve, and it’s applicable to all of them.”

Figure 101’s skills were developed through a 10-hour training period, with it gaining the knowledge simply by observing humans perform the task.


Significant progress

Figure first unveiled its initial humanoid creation in March 2023, the development of which took place in 12 months. This innovative robot, dubbed as the ‘world’s first commercially-viable autonomous humanoid robot’, integrates the agility of the human form with advanced AI, enabling it to execute a diverse array of tasks across multiple sectors, including manufacturing, logistics, warehousing, and retail, according to the firm.

Year 2021 face_with_colon_three


Creatine plays a pivotal role in cellular bioenergetics, acting as a temporal and spatial energy buffer in cells with high and fluctuating energy requirements (1). Jeopardizing delicate creatine homeostasis can be detrimental to many energy-demanding tissues, including the brain. For instance, cerebral creatine hypometabolism accompanies various neurological conditions, including a number of developmental disorders (2, 3), neurodegenerative and cerebrovascular diseases (4, 5), and brain cancer (6). A reduced creatine availability in the brain has been thus recognized as an apposite therapeutic target, and supplying exogenous creatine to compensate for a disease-driven shortfall emerged as a first possible approach. However, early success in animal models of neurological diseases was not corroborated in human trials, with the use of creatine supplementation proved largely disappointing in clinical studies with a number of symptomatic neurological disorders [for a detailed review, see (7)]. A meager delivery of creatine to the brain could be partly due to a low activity/density of creatine transporter (CT1 or SLC6A8), a transmembrane sodium-and chloride-dependent protein that mediates creatine uptake into the target cells (8). For that reason, the upregulation of CT1 function has been identified as an innovative course of action to facilitate creatine uptake, with several exotic agents and routes were cataloged so far, including glucocorticoid-regulated kinases, mammalian target of rapamycin, ammonia, and Klotho protein (9).

Besides other vehicles, Klotho protein (Clotho; HFTC3) is put forward as a possible stimulator of CT1 function that can uplift creatine allocation to the target tissues. This membrane-bound pleiotropic enzyme (also exists in a circulating form) participates in many metabolic pathways, including calcium-phosphate metabolism, nutrient sensing, and remyelination (10). Klotho is highly expressed in neuronal cells of the cerebral cortex, cerebellum, and spinal cord (11). The role of Klotho in high-phosphate energy metabolism modulation was revealed a few years ago when Amilaji et al. (12) found that the co-expression of Klotho protein increases a creatine-induced current in CT1-expressing cells. The authors reported that the current through CT1 was a function of the extracellular creatine levels, with the maximal creatine-induced current was higher in cells expressing CT1 together with Klotho than in cells expressing CT1 alone (29.5 vs. 20.2 nA).