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Math Professor Wrote Wrong Equation on the Board to Test a Black Student—But He Was a Genius Student

What if creativity wasn’t magic—but math?
In this video, we explore the mathematics of creativity through psychology, philosophy, and science. From Dean Keith Simonton’s law of large numbers, Margaret Boden’s theory of combinational creativity, Zipf’s Law, Malcolm Gladwell’s 10,000-hour curve, and even cellular automata—we break down how imagination follows hidden equations.

Whether you’re a student, teacher, scientist, engineer, or philosopher, this video will change how you think about art, science, and human innovation.

Chapters:
00:00 – Intro: Is Creativity Random?
00:34 – The Law of Large Numbers
01:42 – Zipf’s Law of Ideas
02:33 – Combinational Creativity (Boden)
03:15 – Time & Growth (Gladwell)
03:58 – Edge of Chaos (Complexity Theory)
04:48 – The Formula for Creativity.

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Sofia Kovalevskaya: The Girl Who Wouldn’t Give Up on Math

“It is impossible to be a mathematician without being a poet in soul.”-Sofia Kovalevskaya

We don’t often think of math as something that’s “dangerous” or “forbidden”; after all, what could be so dangerous about numbers? Russian-born Sofia Kovalevskaya was told at numerous points during her life that she had to stop studying math, that girls weren’t good enough, they weren’t allowed to go to school, or teach classes, edit magazines or win awards. Sofia Kovalevskaya never gave in to the couldn’t’s or wouldn’t’s. She fought time and again for her right to continue learning and teaching, eventually becoming one of the most celebrated mathematicians of her century and the first woman professor of a northern European University. Today, we celebrate Sofia and all the young mathematicians who overcome great odds!

When Sofia Kovalevskaya was a little girl in the early 1850’s, her room wasn’t wallpapered with flowers or meadowscapes, it was covered in pages and pages of math lecture not es. She would stare at the pages filled with differential and integral analysis, and while she didn’t understand exactly what she saw, Sofia saw beauty in the calculations.

Universal surface-growth law confirmed in two dimensions after 40 years

Crystals, bacterial colonies, flame fronts: the growth of surfaces was first described in the 1980s by the Kardar–Parisi–Zhang equation. Since then, it has been regarded as a fundamental model in physics, with implications for mathematics, biology, and computer science.

Now—40 years later—a Würzburg-based research team from the Cluster of Excellence ctd.qmat has achieved the first experimental demonstration of KPZ behavior on 2D surfaces in space and time.

This was made possible by sophisticated materials engineering and a bold experimental approach: researchers injected polaritons—hybrid particles composed of light and matter—into the material. The results have been published in Science.

Living buildings are now a reality. Swiss scientists unveil a self-healing material that breathes

Can a wall get stronger the more it breaks, and greener the more it stands? Swiss scientists say buildings are about to start breathing and devouring carbon, and the concrete status quo will not like the math.

From a Zurich lab comes a building skin that inhales carbon, knits its own cracks and grows sturdier with time. Researchers at ETH Zurich embedded photosynthetic cyanobacteria in a 3D printed hydrogel, creating a living material that draws down CO₂ and strengthens over time, its chlorophyll tinting it green. Across 400 days of testing, a prototype matched the yearly uptake of a 20-year-old pine, pulling in up to 18 kilograms of CO₂, while each gram of the base material fixes about 26 milligrams. Detailed in Nature Communications on April 6, 2026 and co-authored by Mark Tibbitt, the work points to facades that do carbon duty as part of everyday architecture.

Some breakthroughs feel both surprising and oddly familiar, like rediscovering a tool nature kept in plain sight. Swiss scientists have blended biology with architecture to shape a new kind of material that lives with its surroundings. It repairs small cracks, it sips CO2 from the air, and it quietly strengthens with time. The promise is simple, and bold: buildings that help clean the sky.

New AI method flags fluid flow tipping points before simulations break down

David J. Silvester, a mathematics professor at the University of Manchester, has developed a novel machine-learning method to detect sudden changes in fluid behavior, improving speed and the cost of identifying these instabilities and overcoming one of the major obstacles faced when using machine learning to simulate physical systems. The findings are published in the Journal of Computational Physics.

Computational simulations of mathematical models of fluid flow are essential for everyday applications ranging from predicting the weather to the assessment of nuclear reactor safety. The advent of this simulation capability over the past 50 years has revolutionized the development of fuel-efficient airplanes, and sail configurations on racing yachts can now be optimized in real time, providing the marginal gains needed to win races in the America’s Cup.

Optimized aerodynamics means that modern day cyclists can ride faster, golf balls fly further and Olympic swimmers consistently set world records. Computational fluid dynamics also enables the modeling of the flow of blood in the human heart, making the provision of patient-specific surgery possible.

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.

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