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Category: robotics/AI – Page 294

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.

Test of ‘Poisoned Dataset’ shows Vulnerability of LLMs to Medical Misinformation

For their study published in the journal Nature Medicine, the group generated thousands of articles containing misinformation and inserted them into an AI training dataset and conducted general LLM queries to see how often the misinformation appeared.

Prior research and anecdotal evidence have shown that the answers given by LLMs such as ChatGPT are not always correct and, in fact, are sometimes wildly off-base. Prior research has also shown that misinformation planted intentionally on well-known internet sites can show up in generalized chatbot queries. In this new study, the research team wanted to know how easy or difficult it might be for malignant actors to poison LLM responses.

Stunning: Deep Seek R1 on Fundamental Physics

When I said “Deep Mind”, “Deep Seek” was intended of course.
The recent development of AI presents challenges, but also great opportunities. In this clip I discuss G and other constants with Deep Seek R1.

Want to attend the Demysticon Conference? Go to https://demystifysci.com/demysticon-2025

Mind also my backup channel:
https://odysee.com/@TheMachian: c.
My books: www.amazon.com/Alexander-Unzicker/e/B00DQCRYYY/

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