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Category: mathematics

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.

The simulation hypothesis: Mathematical framework redefines what it means for one universe to simulate another

The simulation hypothesis—the idea that our universe might be an artificial construct running on some advanced alien computer—has long captured the public imagination. Yet most arguments about it rest on intuition rather than clear definitions, and few attempts have been made to formally spell out what “simulation” even means.

A new paper by SFI Professor David Wolpert aims to change that. In Journal of Physics: Complexity, Wolpert introduces the first mathematically precise framework for what it would mean for one universe to simulate another—and shows that several longstanding claims about simulations break down once the concept is defined rigorously.

His results point to a far stranger landscape than previous arguments suggest, including the possibility that a universe capable of simulating another could itself be perfectly reproduced inside that very simulation.

AI is solving ‘impossible’ math problems. Can it best the world’s top mathematicians?

Despite these potential limitations, Lackenby sees AI’s promise in mathematical hypothesis generation. “So many different areas of mathematics are connected to each other, but spotting new connections is really of interest and this process is a good way of seeing new connections that you couldn’t see before,” he said.

Lackenby’s work demonstrates that AI can be helpful in suggesting conjectures that mathematicians can then go on to prove. And despite Saunders’ reservations, Tao thinks AI could be useful in proving existing conjectures.

The most immediate payoff might not be in tackling the hardest problems but in picking off the lowest-hanging fruit, Tao said.

Functions in Hyperspace

Functions describe the world. Join me on a tour of hyperspace, and see the many strange creatures that live there. They are just functions with lots of inputs and outputs. They are parametric surfaces that take inputs u and v, and output spatial x, y, z coordinates, and r, g, b, a color outputs. This produces a colored 3D surface. Then you can add additional inputs and visualize a single slice of each input parameter, and slide through different parameter values to see different slices of the function over time. This causes the colored surface to evolve over time.
I take my time to build up the mathematical intuitions behind visualizing functions, starting with 1-in-1-out functions, and pushing it up to 7-in-7-out functions, and beyond.

Enter Hyperspace: https://evolvecode.io/hyperspace/inde… Code: https://github.com/MaxRobinsonTheGrea… Discord: / discord ~SUPPORT ME~ Scrimba: https://scrimba.com/?via=EmergentGarden Patreon: / emergentgarden Ko-fi: https://ko-fi.com/emergentgarden Twitter: / max_romana Bluesky: https://bsky.app/profile/emergentgard… ~SOURCES~ Functions Describe the World: • On Mathematical Maturity Thomas Garrity Hyperspace animation: • Blender Hyperspace Jump Shell Surfaces: https://www.geogebra.org/m/twfwsxb9 Music: / @acolyte-compositions Most come from this new album: • Stellar Nurseries (Full Album) AI Disclaimer: I used AI code tools for the website and animations. No AI video, images, script, voice, or music were used. ~TIMESTAMPS~ (0:00) Functions in Hyperspace (2:47) Visualizing Functions (5:37) Parametric Surfaces (10:01) Slices of Slices (14:55) More Parameters (18:28) Exploring the Zoo.
Source Code: https://github.com/MaxRobinsonTheGrea…
Discord: / discord.

~SUPPORT ME~
Scrimba: https://scrimba.com/?via=EmergentGarden.
Patreon: / emergentgarden.
Ko-fi: https://ko-fi.com/emergentgarden.
Twitter: / max_romana.
Bluesky: https://bsky.app/profile/emergentgard…

~SOURCES~
Functions Describe the World: • On Mathematical Maturity Thomas Garrity.
Hyperspace animation: • Blender Hyperspace Jump.
Shell Surfaces: https://www.geogebra.org/m/twfwsxb9

Music: / @acolyte-compositions.
Most come from this new album: • Stellar Nurseries (Full Album)

AI Disclaimer:

Continue reading “Functions in Hyperspace” | >

Number’s up: Calculators hold out against AI

The humble pocket calculator may not be able to keep up with the mathematical capabilities of new technology, but it will never hallucinate.

The device’s enduring reliability equates to millions of sales each year for Japan’s Casio, which is even eyeing expansion in certain regions.

Despite lightning-speed advances in artificial intelligence, chatbots still sometimes stumble on basic addition.

Math, Inc.

The Math Inc. team is excited to introduce Gauss, a first-of-its-kind autoformalization agent for assisting human expert mathematicians at formal verification. Using Gauss, we have completed a challenge set by Fields Medallist Terence Tao and Alex Kontorovich in January 2024 to formalize the strong Prime Number Theorem (PNT) in Lean (GitHub).

The translation of human mathematics into verifiable machine code has long been a grand challenge. However, the cost of doing so is prohibitive, requiring scarce human expertise. In particular, after 18 months, Tao and Kontorovich recently announced intermediate progress in July 2025 toward their goal, obstructed by core difficulties in the field of complex analysis.

In light of such difficulties, we are pleased to announce that with Gauss, we have completed the project after three weeks of effort. Gauss can work autonomously for hours, dramatically compressing the labor previously reserved for top formalization experts. Along the way, Gauss formalized the key missing results in complex analysis, which opens up future initiatives previously considered unapproachable.

The Universal Law Behind Market Price Swings

Analysis of a large dataset from the Tokyo Stock Exchange validates a universal power law relating the price of a traded stock to the traded volume.

One often hears that economics is fundamentally different from physics because human behavior is unpredictable and the economic world is constantly changing, making genuine “laws” impossible to establish. In this view, markets are never in a stable state where immutable laws could take hold. I beg to differ. The motion of particles is also unpredictable, and many physical systems operate far from equilibrium. Yet, as Phil Anderson argued in a seminal paper [1], universal laws can still emerge at the macroscale from the aggregation of widely diverse microscopic behaviors. Examples include not only crowds in stadiums or cars on highways but also economic agents in markets.

Now Yuki Sato and Kiyoshi Kanazawa of Kyoto University in Japan have provided compelling evidence that one such universal law governs financial markets. Using an unprecedentedly detailed dataset from the Tokyo Stock Exchange, they found that a single mathematical law describes how the price of every traded stock responds to trading volume [2] (Fig. 1). The result is a striking validation of physics-inspired approaches to social sciences, and it might have far-reaching implications for how we understand market dynamics.

String Theory Inspires a Brilliant, Baffling New Math Proof

When the team posted their proof in August, many mathematicians were excited. It was the biggest advance in the classification project in decades, and hinted at a new way to tackle the classification of polynomial equations well beyond four-folds.

But other mathematicians weren’t so sure. Six years had passed since the lecture in Moscow. Had Kontsevich finally made good on his promise, or were there still details to fill in?

And how could they assuage their doubts, when the proof’s techniques were so completely foreign — the stuff of string theory, not polynomial classification? “They say, ‘This is black magic, what is this machinery?’” Kontsevich said.

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