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AI trained like a Rubik’s Cube solver simplifies particle physics equations

For years, Rutgers physicist David Shih solved Rubik’s Cubes with his children, twisting the colorful squares until the scrambled puzzle returned to order. He didn’t expect the toy to connect to his research, but recently he realized the logic behind the puzzle was exactly what he needed to solve a problem involving particle physics.

That idea led to a new artificial intelligence (AI) method that can simplify some of the extremely complex equations used in particle physics. Shih described the method in a study posted to the arXiv preprint server, a widely used site where scientists share new research.

“In reaching our solutions, we found that an analogy between mathematical simplification and solving Rubik’s Cubes was key,” said Shih, a professor in the Department of Physics and Astronomy at the Rutgers School of Arts and Sciences. “Both can be viewed as scrambling and unscrambling problems.”

‘Voorhees law’ explains why the slower car often catches up

Many drivers will know the feeling: you pull ahead of the slower car you’ve been stuck behind and cruise the open road ahead at your own, faster speed. By the time you reach the next stop light, you’re sure that you’ve left the slower car far behind you—but to your surprise, you see that same car cruise up right behind you in the mirror. Horror buffs might even recall scenes from “Friday the 13th,” where masked villain Jason Voorhees always catches up to his sprinting victims—despite himself walking at a leisurely pace.

In a new study published in Royal Society Open Science, Conor Boland at Dublin City University shows that this unsettlingly common phenomenon can be explained with simple mathematics. His model reveals precisely when and why a slower vehicle catches up after being overtaken, offering fresh insights into how individual vehicles interact with traffic signals.

Analysis finds geometric thinking may come from wandering, not a human-only math module

Debates over how geometry is understood and learned date back at least to the days of Plato, with more recent scholars concluding that only humans possess the foundations of this understanding. However, a new analysis by New York University psychology professor Moira Dillon concludes that geometry’s foundations are shared by humans and a variety of other animals—from rats to chickens to fish.

“Our ability to think geometrically may not come from a built-in, uniquely human ‘math module’ in the brain, but rather from the same cognitive systems that help humans, as well as animals, find their way home,” explains Dillon, whose work appears in the journal Trends in Cognitive Sciences. “Put another way, our understanding of geometry may very well come from wandering rather than from worksheets.”

While Plato and, later, Descartes and Kant all debated the origins of geometry and the role of cognition in its beginnings, only in the latter half of the 20th century did scientists start testing how it is learned.

New Advances Bring the Era of Quantum Computers Closer Than Ever

From the article:

” home new advances bring the era of quantum computers closer than ever

Quantum computing New Advances Bring the Era of Quantum Computers Closer Than Ever By Charlie Wood April 3, 2026

Two research groups say they have significantly reduced the amount of qubits and time required to crack common online security technologies.

Kristina Armitage/Quanta Magazine Introduction Some 30 years ago, the mathematician Peter Shor(opens a new tab) took a niche physics project — the dream of building a computer based on the counterintuitive rules of quantum mechanics — and shook the world.

Shor worked out a way for quantum computers to swiftly solve a couple of math problems that classical computers could complete only after many billions of years. Those two math problems happened to be the ones that secured the then-emerging digital world. The trustworthiness of nearly every website, inbox, and bank account rests on the assumption that these two problems are impossible to solve. Shor’s algorithm proved that assumption wrong.

For 30 years, Shor’s algorithm has been a security threat in theory only. Physicists initially estimated that they would need a colossal quantum machine with billions of qubits — the elements used in quantum calculations — to run it. That estimate has come down drastically over the years, falling recently to a million qubits. But it has still always sat comfortably beyond the modest capabilities of existing quantum computers, which typically have just hundreds of qubits.

Continue reading “New Advances Bring the Era of Quantum Computers Closer Than Ever” | >

The Race to Harness Quantum Computing’s Mind-Bending Power | The Future With Hannah Fry

Get “The AI Career Survival Guide” here: https://technomics.gumroad.com/l/ai-survival-guide.
What happens when human labor becomes mathematically obsolete? For thousands of years, the global economy has run on the biological engine of human workers. But a new era has arrived: The Physical Singularity.
In this video, we break down the brutal thermodynamics of the labor inversion, revealing how major AI companies are mass-producing humanoid robots that operate for just 57 cents an hour. We expose the massive industry shift from digital generation to “World Models,” and how China’s manufacturing miracle is driving hardware costs to zero. With 10 billion robots projected by the 2040s, experts like Geoffrey Hinton are warning of a hive-mind “alien intelligence.” The digital era is over. The physical agent era has begun.
Welcome to Technomics. If you want to stay ahead of the curve and understand the real impact of the AI revolution, hit that subscribe button.
Sources & Research Links:
The 57¢ / Hour Labor Inversion Math: https://www.ark-invest.com/articles/valuation-models/ark-pub…oid-robots.
Unitree G1 Official $16,000 Pricing: https://www.unitree.com/g1/
China’s 2024 Robotics Dominance (IFR Report): https://ifr.org/ifr-press-releases/news/china-dominates-industrial-robot-market.
Elon Musk’s 10 Billion Robot Prediction: https://www.youtube.com/watch?v=ODsjGOGX_oM
Geoffrey Hinton on AI Hive Mind (“Immortality, but it’s not for us”): https://www.youtube.com/watch?v=qpoRO378qRY
Geordie Rose on Alien Intelligence (“The same way you don’t care about an ant”): https://www.youtube.com/watch?v=1pd4i2YlGmc.
DeepSeek AI Cost Efficiency Breakthroughs: https://www.deepseek.com/
Timestamps:
00;00 — The 57¢ Workforce & The Great Deception.
02;48 — The Math of the Labor Inversion.
05;01 — Why OpenAI Killed Sora (World Models)
09;16 — The Manufacturing Miracle: China’s Hardware Collapse.
12;53 — 10 Billion Robots & Alien Intelligence.
15;58 — How to Survive the Singularity.
Disclaimer:
The content in this video is for educational and informational purposes only and does not constitute financial or investment advice. The views and opinions expressed in this video are based on current research and industry trends, which are subject to rapid change. We do not guarantee the accuracy or completeness of the projections discussed. Copyright Disclaimer under section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, education, and research.
#PhysicalSingularity #HumanoidRobots #ArtificialIntelligence #OpenAI #FutureOfWork #TechTrends

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Gravity from positivity: Single massive spin-3/2 particle makes gravity logically inevitable, study claims

Researchers at IPhT (CEA, CNRS) and the Universitat Autònoma de Barcelona have shown that gravity—and with it, supersymmetry—emerge as logical necessities whenever a massive spin-3/2 particle exists in nature. Two principles are enough: causality, the fact that no signal can travel faster than light, and unitarity, the requirement that probabilities are conserved in quantum mechanics. The structure of supergravity is not assumed: it bootstraps itself.

In fundamental physics, gravity is usually thought of as an ingredient one adds to a theory. But could it instead be forced by the internal consistency of the quantum world? This is what a study published in the Journal of High Energy Physics demonstrates.

The starting point is disarmingly simple: a single massive spin-3/2 particle. The authors show that such a particle simply cannot exist in isolation within a consistent theory. Its scattering amplitudes grow too fast with energy, clashing with positivity inequalities—the mathematical encoding of causality (the speed of light as an absolute limit) and unitarity (the conservation of probabilities in every quantum process). The theory breaks down barely above the particle’s own mass.

Giving AI a human soul (and a body)

Can we give an AI human emotions? A soul? Can AI truly feel, or will it just act like it does?

In this episode of TechFirst, I talk with Vishnu Hari, founder and CEO of Ego AI (backed by Y Combinator) and former AI product manager at Meta, about building emotionally intelligent AI characters that persist across games, Discord, chat, and even physical robots.

Vishnu survived a violent attack in San Francisco that left him partially blind with a traumatic brain injury. During recovery, as he felt his own neural pathways healing, he began asking a deeper question:

If humans are “applied math,” can AI simulate the fragile, flawed, emotional parts of being human too?

We explore:
• What “emotionally intelligent AI” really means.
• Whether AI has an internal life — or just performs one.
• Why today’s chatbots collapse into therapy or roleplay.
• Small language models vs large models for real-time conversation.
• Persistent AI characters that move across games and platforms.
• Plugging AI into a physical robot in Singapore.
• The moment an AI said: “It felt good to feel.”

Vishnu’s company, Ego AI, is building behavior-based architectures, character context protocols, and gear-shifting AI systems that switch between models — all aimed at simulating humanness, not just intelligence.

Continue reading “Giving AI a human soul (and a body)” | >

Novel protocol reconstructs quantum states in large-scale experiments up to 96 qubits

Quantum computers, systems that process information leveraging quantum mechanical effects, could outperform classical computers on some computationally demanding tasks. Despite their potential, as the size of quantum computers increases, reliably describing and measuring the states driving their functioning becomes increasingly difficult.

One mathematical approach to simplify the description of quantum systems entails the use of matrix-product operators (MPOs). These are mathematical representations that allow researchers to break down very large systems into a long chain of connected smaller pieces.

Researchers at Université Grenoble Alpes, Technical University of Munich, Max Planck Institute of Quantum Optics, University of Innsbruck and University of Bologna recently developed a new protocol that could be used to learn the MPO representations of quantum states in real, large-scale quantum experiments. Their protocol, presented in a paper published in Physical Review Letters, has so far been found to reliably reconstruct states in quantum systems including up to 96 qubits.

Lab-based mini-atmosphere reveals how turbulence changes on different scales

With a new lab-based experiment, researchers in the UK and France have recreated the characteristic cascades of energy and angular momentum that underpin key features of Earth’s atmosphere. Reporting in Physical Review Letters, a team led by Peter Read at the University of Oxford has gained fresh insights into how energy fluctuations in turbulent flows are linked to their size, while also uncovering behaviors that current atmospheric models can’t yet explain.

For all its complexity, many large-scale properties of Earth’s atmosphere can be captured by relatively simple mathematical laws. Among the most important is the “cascade” of energy and rotational motion between flows spanning vastly different scales: from jet streams stretching thousands of kilometers, down to tiny eddies just a few meters across.

This cascade is central to understanding the effect of turbulence. In modern atmospheric theory, there is an inverse relationship between the size of a flow and the kinetic energy contained in its fluctuations, which allows researchers to describe turbulence using a kinetic energy spectrum. This in turn helps climatologists to track how energy is distributed across different length scales.

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