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Category: robotics/AI – Page 334

3D printing approach for shape-changing materials means better biomedical, energy, robotics devices

An Oregon State University researcher has helped create a new 3D printing approach for shape-changing materials that are likened to muscles, opening the door for improved applications in robotics as well as biomedical and energy devices.

The liquid crystalline elastomer structures printed by Devin Roach of the OSU College of Engineering and collaborators can crawl, fold and snap directly after printing. The study is published in the journal Advanced Materials.

“LCEs are basically soft motors,” said Roach, assistant professor of mechanical engineering. “Since they’re soft, unlike regular motors, they work great with our inherently soft bodies. So they can be used as implantable medical devices, for example, to deliver drugs at targeted locations, as stents for procedures in target areas, or as urethral implants that help with incontinence.”

A novel biomaterial for regenerative medicine: Scientists develop acellular nanocomposite living hydrogels

A biomaterial that can mimic certain behaviors within biological tissues could advance regenerative medicine, disease modeling, soft robotics and more, according to researchers at Penn State.

Materials created up to this point to mimic tissues and extracellular matrices (ECMs)—the body’s biological scaffolding of proteins and molecules that surrounds and supports tissues and cells—have all had limitations that hamper their practical applications, according to the team. To overcome some of those limitations, the researchers developed a bio-based, “living” material that encompasses self-healing properties and mimics the biological response of ECMs to .

They published their results in Materials Horizons, where the research was also featured on the cover of the journal.

Figure AI plans 100,000-strong humanoid robot army to counter China

🤖 100,000 bots?! 🤖

“It gives us potential to ship at high volumes which will drive cost reduction and AI data collection. Between both customers, we believe there is a path to 100,000 robots over the next four years.”

My opinion: Figure🤖 is superior to Tesla🤖

Figure AI had launched its first humanoid, Figure 1, just 31 months after incorporation and subsequently shipped Figure 02.

Organoid intelligence: training lab-grown mini-brains to learn and compute with AI

Recent research demonstrates that brain organoids can indeed “learn” and perform tasks, thanks to AI-driven training techniques inspired by neuroscience and machine learning. AI technologies are essential here, as they decode complex neural data from the organoids, allowing scientists to observe how they adjust their cellular networks in response to stimuli. These AI algorithms also control the feedback signals, creating a biofeedback loop that allows the organoids to adapt and even demonstrate short-term memory (Bai et al. 2024).

One technique central to AI-integrated organoid computing is reservoir computing, a model traditionally used in silicon-based computing. In an open-loop setup, AI algorithms interact with organoids as they serve as the “reservoir,” for processing input signals and dynamically adjusting their responses. By interpreting these responses, researchers can classify, predict, and understand how organoids adapt to specific inputs, suggesting the potential for simple computational processing within a biological substrate (Kagan et al. 2023; Aaser et al. n.d.).

Researchers combine holograms and AI to create uncrackable optical encryption system

WASHINGTON — As the demand for digital security grows, researchers have developed a new optical system that uses holograms to encode information, creating a level of encryption that traditional methods cannot penetrate. This advance could pave the way for more secure communication channels, helping to protect sensitive data.

“From rapidly evolving digital currencies to governance, healthcare, communications and social networks, the demand for robust protection systems to combat digital fraud continues to grow,” said research team leader Stelios Tzortzakis from the Institute of Electronic Structure and Laser, Foundation for Research and Technology Hellas and the University of Crete, both in Greece.

Optica is the leading society in optics and photonics. Quality information and inspiring interactions through publications, meetings, and membership.

An Information-Theoretic Approach for Detecting Edits in AI-Generated Text

Abstract: We propose a method to determine whether a given article was written entirely by a generative language model or perhaps contains edits by a different author, possibly a human. Our process involves multiple tests for the origin of individual sentences or other pieces of text and combining these tests using a method sensitive to alternatives in which non-null effects are few and scattered across the text in unknown locations. Interestingly, this method is also useful for identifying pieces of text suspected to contain edits. We demonstrate the effectiveness of the method in detecting edits through extensive evaluations using real data and provide an analysis of the factors affecting its success. In particular, we discuss optimality properties under a theoretical framework for text editing saying that sentences are generated mainly by the language model, except perhaps for a few sentences that might have originated via a different mechanism. Our analysis raises several interesting research questions at the intersection of information theory and data science.

Researchers used AI to build groundbreaking nanomaterials lighter and stronger than titanium

The research team, led by Professor Tobin Filleter, has engineered nanomaterials that offer unprecedented strength, weight, and customizability. These materials are composed of tiny building blocks, or repeating units, measuring just a few hundred nanometers – so small that over 100 lined up would barely match the thickness of a human hair.

The researchers used a multi-objective Bayesian optimization machine learning algorithm to predict optimal geometries for enhancing stress distribution and improving the strength-to-weight ratio of nano-architected designs. The algorithm only needed 400 data points, whereas others might need 20,000 or more, allowing the researchers to work with a smaller, high-quality data set. The Canadian team collaborated with Professor Seunghwa Ryu and PhD student Jinwook Yeo at the Korean Advanced Institute of Science & Technology for this step of the process.

This experiment was the first time scientists have applied machine learning to optimize nano-architected materials. According to Peter Serles, the lead author of the project’s paper published in Advanced Materials, the team was shocked by the improvements. It didn’t just replicate successful geometries from the training data; it learned from what changes to the shapes worked and what didn’t, enabling it to predict entirely new lattice geometries.

SoftBank to invest $500M in robotics startup Skild AI

SoftBank is negotiating a $500 million investment in Skild AI, a software company building a foundational model for robotics at a $4 billion valuation, Bloomberg and Financial Times reported.

The 2-year-old company raised its previous funding round of $300 million at a $1.5 billion valuation last July from investors, including Jeff Bezos, Lightspeed Venture Partners, and Coatue Management.

The company’s AI model can be applied to various types of robots, Skild founders Deepak Pathak and Abhinav Gupta told TechCrunch last July. They said the generalized model can be modified for a specific domain and use case.

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