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How can programmed failure protocols help improve sheet-based fluidic devices, the latter of which have become a cornerstone in enhancing soft robotics worldwide? This is what a recent study published in Cell Reports Physical Science hopes to address as an international team of researchers have developed a method for overcoming common failures of sheet-based systems, specifically due to their lightweight and flexible characteristics. This study has the potential to help engineers develop more efficient sheet-based devices, resulting in improved soft robotics designs.

For the study, the researchers examined how pressure changes could damage heat-sealable textiles that are used in sheet-based devices. Once they determined specific failure thresholds, the team incorporated programmed failures into the design, enabling the device to determine specific failure points and prevent further damage.

“Put simply, we are making soft, flexible machines smarter by designing their internal components to fail intentionally in a well-understood manner,” said Dr. Daniel J. Preston, who is an assistant professor of mechanical engineering at Rice University and a co-author on the study. “In doing so, the resulting systems can recover from pressure surges and even complete multiple tasks using a single control input.” Going forward, the team hopes their research will lead to improved sheet-based fluidic systems, which, as noted, have become a cornerstone of soft robotics.

Researchers, including those from the University of Tokyo, developed Deep Nanometry, an analytical technique combining advanced optical equipment with a noise removal algorithm based on unsupervised deep learning.

Deep Nanometry can analyze nanoparticles in medical samples at high speed, making it possible to accurately detect even trace amounts of rare particles. This has proven its potential for detecting indicating early signs of colon cancer, and it is hoped that it can be applied to other medical and industrial fields.

The body is full of smaller than cells. These include extracellular vesicles (EVs), which can be useful in early disease detection and also in drug delivery.

Imagine being able to speed up evolution – hypothetically – to learn which genes might have a harmful or beneficial effect on human health. Imagine, further, being able to rapidly generate new genetic sequences that could help cure disease or solve environmental challenges.

Now, scientists have developed a generative AI tool that can predict the form and function of proteins coded in the DNA of all domains of life, identify molecules that could be useful for bioengineering and medicine, and allow labs to run dozens of other standard experiments with a virtual query – in minutes or hours instead of years (or millennia).


Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life.

Plastic that conducts electricity might sound impossible. But there is a special class of materials known as “electronic polymers” that combines the flexibility of plastic with the functionality of metal. This type of material opens the door for breakthroughs in wearable devices, printable electronics and advanced energy storage systems.

Yet, making thin films from electronic polymers has always been a difficult task. It takes a lot of fine-tuning to achieve the right balance of physical and . Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have created an innovative solution to this challenge with artificial intelligence (AI).

They used an AI-driven, automated materials laboratory, a tool called Polybot, to explore processing methods and produce high-quality films. Polybot is located at the Center for Nanoscale Materials, a DOE Office of Science user facility at Argonne.

Last video: Why Tesla Energy Will Take Over in 2025

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In today’s AI news, Codeium, an AI-powered coding startup, is raising a new round of funding at a $2.85 billion valuation. The round is being led by returning investor Kleiner Perkins, the people said. The new round comes just six months after Silicon Valley-based Codeium announced that it had closed a $150 million Series C at a $1.25 billion post-money valuation.

In other advancements, a couple of weeks after the initial release of Mistral’s AI assistant, Le Chat, the company told Le Parisien that it has reached one million downloads. “Go and download Le Chat, which is made by Mistral, rather than ChatGPT by OpenAI — or something else,” French president Emmanuel Macron said in a TV interview ahead of the recent AI Action Summit in Paris.

And, Google is launching a new experiment that uses AI to help people explore more career possibilities. The company announced in a blog post on Wednesday that a new “Career Dreamer” tool can find patterns between your experiences, educational background, skills, and interests to connect you with careers that might be a good fit.

Meanwhile, Forbes’ Lance Eliot analyzes a popular mantra right now. The recent AI-industry groupthink that says we merely need to increase the so-called “thinking time” of generative AI and LLMs to get better responses. AI makers are allowing users to stipulate that the AI can expend more time and effort doing various processing before displaying a generated answer.

In videos, Microsoft’s Satya Nadella sits down with Dwarkesh Patel to talk about their new Majorana Quantum chip breakthrough, plans for artificial general intelligence, topological qubits, gaming world models, and whether Microsoft Office commoditizes LLMs, or the other way around.

Then, dive into the world of Model Context Protocol and learn how to seamlessly connect AI agents to databases, APIs, and more. IBM’s Roy Derks breaks down its components, from hosts to servers, and showcases real-world applications. Gain the knowledge to revolutionize your AI projects.