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In recent years, computer chip performance has bumped up against the physical limitations of the space available on integrated circuits.
Now researchers think they’ve found a solution: Start building upwards.
The innovation could help extend or even exceed the Moore’s Law hypothesis established in the 1960s by Intel chairman Gordon Moore.
Explore the University of Tokyo’s creation: a walking robot powered by muscle tissue.
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A research project led by the Institute for Research in Nutrition and Food Safety (INSA) and the Faculty of Pharmacy and Food Sciences at the University of Barcelona, together with the Molecular Biology Institute of Barcelona (IBMB) of the CSIC (which stands for Consejo Superior de Investigaciones Científicas), has successfully designed and tested a gluten-degrading molecule that is a promising ally in the management of celiac disease, an autoimmune disease whose symptoms are triggered by the consumption of gluten and other prolamins found in cereals.
At present, there is a complete lack of treatment options beyond a diet free from gluten, which is difficult to maintain in Western societies where diets rely heavily on wheat products.
The major breakthrough is that the molecule is effective at very low concentrations and at a pH of 2—the pH of the stomach—a condition that none of the molecules currently available or under development had previously achieved with efficiency. Although some of them are marketed as nutritional supplements, they are not an effective alternative to gluten-free diets.
Over the past few decades, roboticists worldwide have introduced increasingly advanced robots that can understand human instructions, move in their surroundings and reliably complete basic manual tasks. While they perform well in some scenarios, many of these robots still struggle to translate the instructions of users into precise and executable actions that would allow them to successfully complete desired tasks.
Recently, computer scientists have been trying to improve how robots respond to user commands or queries using vision-language models (VLMs), artificial intelligence (AI) systems trained to process both images and texts. These models can typically interpret basic requests such as “place the bottle onto the plate,” yet they often do not exhibit the spatial reasoning capabilities required to interpret more elaborate instructions and translate them into executable actions in real-world settings.
Researchers at the Chinese University of Hong Kong, the Zhejiang Humanoid Robot Innovation Center Co. Ltd and other institutes recently introduced Retrieval-Augmented Manipulation (RAM), a framework that could improve the ability of robots to connect abstract instructions with three-dimensional (3D) representations of the space around them. The new framework, presented in a Science Robotics paper, was found to improve the spatial reasoning capabilities of robots, allowing them to reliably follow more elaborate instructions, without requiring task-specific training.