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Category: robotics/AI

Promising new superconducting material discovered with the help of AI

Tohoku University and Fujitsu Limited have successfully used AI to derive new insights into the superconductivity mechanism of a new superconducting material.

Their findings demonstrate an important use case for AI technology in new materials development and suggest that the technology has the potential to accelerate research and development. This could drive innovation in various industries such as the environment and energy, drug discovery and health care, and electronic devices.

The AI technology was used to automatically clarify causal relationships from measurement data obtained at NanoTerasu Synchrotron Light Source. This achievement was published in Scientific Reports.

Lowering barriers to explainable AI: Control technique for LLMs reduces resource demands by over 90%

Large language models (LLMs) such as GPT and Llama are driving exceptional innovations in AI, but research aimed at improving their explainability and reliability is constrained by massive resource requirements for examining and adjusting their behavior.

To tackle this challenge, a Manchester research team led by Dr. Danilo S. Carvalho and Dr. André Freitas have developed new software frameworks—LangVAE and LangSpace—that significantly reduce both hardware and energy resource needs for controlling and testing LLMs to build explainable AI. Their paper is published on the arXiv preprint server.

Their technique builds compressed language representations from LLMs, making it possible to interpret and control these models using geometric methods (essentially treating the model’s internal language patterns as points and shapes in space that can be measured, compared and adjusted), without altering the models themselves. Crucially, their approach reduces computer resource usage by more than 90% compared with previous techniques.

MIT Engineers Create 3D-Printable Aluminum 5 Times Stronger Than Conventional Alloys

By applying machine learning techniques, engineers at MIT have created a new method for 3D printing metal alloys that produce parts far stronger than those made using traditional manufacturing approaches. MIT engineers have created a new aluminum alloy designed for 3D printing that holds up under

AMD and Google Tap Samsung’s Texas Fab for AI Chips

Advanced Micro Devices AMD-0.09% ▼ and Alphabet GOOGL +1.61% ▲ are in talks with Samsung Electronics SSNLF +54.05% ▲ to build next-gen chips in Texas. Both companies are exploring the use of Samsung’s new factory in Taylor, Texas, to manufacture 2-nanometer chips. These chips are expected to be the most advanced available when the plant is ready in 2026.

From Decoherence to Coherent Intelligence: A Framework for the Emergence of AI Structure through Recursive Reasoning

This paper develops a thermodynamic framework for understanding the coherence of both biological and artificial cognition. We formalize thermodynamic coherence as an expression of information processing constrained by entropy and temperature, establishing a quantitative link between physical energy states and cognitive stability. Building on foundational concepts from statistical mechanics, quantum biology, and information theory, we argue that intelligence emerges as an ordered process, one that locally resists entropy through orderly reasoning work that generates coherent structure. The resulting framework is applied to wave function collapse, consciousness models, and machine reasoning, showing that coherence serves as a universal condition for stable cognition across domains.

Inventor and futurist talks his hopes for the advancement of AI and technology

Ray Kurzweil is an acclaimed inventor, futurist and author. In his newest book, “The Singularity is Nearer,” he dives into the future date where humans and machines eventually merge. Jeff Glor has more from their conversation. “CBS Saturday Morning” co-hosts Jeff Glor, Michelle Miller and Dana Jacobson deliver two hours of original reporting and breaking news, as well as profiles of leading figures in culture and the arts. Watch “CBS Saturday Morning” at 7 a.m. ET on CBS and 8 a.m. ET on the CBS News app. Subscribe to “CBS Mornings” on YouTube: / cbsmornings Watch CBS News 24/7: https://cbsnews.com/live/ Download the CBS News app: https://cbsnews.com/mobile/ Follow “CBS Mornings” on Instagram: / cbsmornings Like “CBS Mornings” on Facebook: / cbsmornings Follow “CBS Mornings” on Twitter: / cbsmornings Subscribe to our newsletter: https://cbsnews.com/newsletters/ Try Paramount+ free: https://paramountplus.com/?ftag=PPM-0… For video licensing inquiries, contact: [email protected]

Introducing TinyAleph: Revolutionizing How Computers Understand Meaning with Primes and Oscillators

Imagine if meaning — the elusive essence of language and thought — could be broken down into mathematical building blocks as fundamental as prime numbers. What if computers could “reason” by synchronizing oscillators, much like neurons firing in harmony in our brains?

That’s the bold idea behind TinyAleph, a new framework and library I’ve developed for semantic computing. Unlike today’s AI models that gobble up massive datasets to mimic understanding, TinyAleph grounds meaning in pure math: primes, hypercomplex algebra, and dynamic oscillators.

In this article, I’ll walk you through the core ideas of TinyAleph, stripping away the academic jargon to show why this could be a game-changer for AI, cryptography, and even quantum-inspired simulations. No PhD required — just an open mind.

AI Bathroom Monitors? Welcome To America’s New Surveillance High Schools

This isn’t a high-security government facility. It’s Beverly Hills High School.

District superintendent Alex Cherniss says the striking array of surveillance tools is a necessity, and one that ensures the safety of his students. “We are in the hub of an urban setting of Los Angeles, in one of the most recognizable cities on the planet. So we are always a target and that means our kids are a target and our staff are a target,” he said. In the 2024–2025 fiscal year, the district spent $4.8 million on security, including staff. The surveillance system spots multiple threats per day, the district said.

Beverly Hills’ apparatus might seem extreme, but it’s not an outlier. Across the U.S., schools are rolling out similar surveillance systems they hope will keep them free of the horrific and unceasing tide of mass shootings. There have been 49 deaths from gunfire on school property this year. In 2024, there were 59, and in 2023 there were 45, per Everytown for Gun Safety. Between 2000 and 2,022,131 people were killed and 197 wounded at schools in the U.S., most of them children. Given those appalling metrics, allocating a portion of your budget to state of the art AI-powered safety and surveillance tools is a relatively easy decision.

Reining In a Chaotic Fluid

Fluid flows mimicking biological flows can be controlled in the lab using a feedback system, which could be useful in robotics and other technologies.

Ordinary fluids can flow when driven by pressure or gravity, but biological fluids, such as those inside cells, generate complex flows through internal sources of chemical energy. Flows of such “active fluids” could be extremely useful in robotics and other areas of engineering, but controlling them remains difficult. Now researchers have demonstrated a method of control that maintains a constant fluid speed despite changing conditions [1]. They hope that the approach can be used to stabilize active-matter flows in future technologies.

Life depends on biochemical processes that respond to many situations while maintaining fixed chemical conditions despite external and internal disruptions. Inspired by this impressive stability, researchers have been developing analogous artificial systems by assembling active fluids from key biochemical components akin to those inside cells. For example, they have created fluids that can generate their own bulk contractions or undergo spontaneous flows. Although these rudimentary designs mimic some features of living matter, researchers have so far failed to demonstrate techniques that keep properties such as fluid flow speeds stable over time.

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