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.
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.
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:
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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.
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.
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.
AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding. However, there have been limitations for the world of scientific discovery involving more curiosity-driven research. But that may soon change, thanks to Kolmogorov-Arnold networks (KANs).
A recent study, published in the journal Physical Review X, details how this new kind of neural network architecture might help scientists discover and understand the physical world in a way that other AI can’t.
Scientists in China have unveiled a new AI chip called LightGen that is 100 times faster and 100 times more energy efficient than NVIDIA chips, the leading supplier of AI chips worldwide. Instead of using electricity to move information, this new optical chip relies on light to perform complex generative tasks.
Traditional general AI models, such as ChatGPT and Stable Diffusion, run on everyday silicon chips and require massive amounts of computing power and electricity, which can generate significant heat. For particularly complex tasks, these chips can struggle with the workload, resulting in slow processing times.
“Feral” gossip spread via AI bots is likely to become more frequent and pervasive, causing reputational damage and shame, humiliation, anxiety, and distress, researchers have warned.
Chatbots like ChatGPT, Claude, and Gemini don’t just make things up—they generate and spread gossip, complete with negative evaluations and juicy rumors that can cause real-world harm, according to new analysis by philosophers Joel Krueger and Lucy Osler from the University of Exeter.
The research is published in the journal Ethics and Information Technology.