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Penrose vs EWOG: Consciousness and Quantum Collapse

Consciousness beyond penrose quantum microtubules?utm_source=share&utm_medium=member_android&rcm=ACoAADcXNX8BNm6vE2wHF7V91czmcuYXcuPHhY4.

đŸ§ âš›ïž Beyond Penrose: Can Consciousness Be Derived from Geometry? For more than 30 years, Roger Penrose and Stuart Hameroff proposed that consciousness emerges through Objective Reduction (OR) inside neuronal microtubules. Penrose’s key equation is remarkably simple: τ_OR = ℏ / E_G where: τ_OR = collapse time ℏ = reduced Planck constant E_G = gravitational self-energy of the spacetime superposition The idea is: 🌌 Spacetime superposition ⟶ Gravitational instability ⟶ Wavefunction collapse ⟶ Conscious event But a major question remained: ❓ What is the mathematical mechanism that actually causes collapse? The EWOG framework attempts to provide one.

Maths is Cooked: AI’s Latest Breakthrough — And What’s Next

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As AI continues to improve its reasoning abilities, mathematicians are increasingly worried about the computer algorithms replacing them. In late May, those fears got even worse when OpenAI revealed that one of its general-purpose reasoning models had written a proof solving a math problem that’s sat unsolved for more than 80 years. But should they actually be worried? Let’s take a look.

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Google DeepMind AI Discovered a Mathematical Pattern Hidden in Prime Numbers

What exactly did DeepMind find?
Could this discovery help solve longstanding mathematical mysteries?
And what might it mean for cryptography, computing, and our understanding of mathematics itself?

In this video, we explore the science behind the discovery, the role of artificial intelligence in modern research, and why mathematicians around the world are paying close attention.

Whether this breakthrough leads to a revolutionary new theorem or simply a deeper understanding of prime numbers, it demonstrates the growing power of AI to accelerate scientific progress.

👇 What do YOU think?
Will AI help solve the greatest unsolved problems in mathematics?

💬 COMMENT below, 👍 LIKE the video, and 🔔 SUBSCRIBE for more AI breakthroughs, mathematical mysteries, and cutting-edge science discoveries!

Most precise measurement of the force that binds nuclear matter achieved

Trinity’s Prof. Stefan Sint, along with collaborators from Germany, Spain and Italy, has published the most precise determination to date of the strong coupling constant. This parameter governs the interactions between quarks and gluons, the fundamental components of nuclear matter. The new result halves the error of all previous experimental measurements combined, setting a new benchmark for the Standard Model, which summarizes our current knowledge of elementary particle physics.

This advance will improve our understanding of how quarks and gluons behave inside protons and enable high-precision measurements of the Higgs boson and its properties. More generally, improved quantitative control of the strong interactions increases the likelihood of discovering effects of yet unknown physics at CERN’s Large Hadron Collider (LHC).

Prof. Sint from Trinity’s School of Mathematics was one of the researchers whose landmark results were published in Nature.

Alonzo Church

His revolutionary idea? Before “computer science” was even a field, Church invented the lambda calculus (λ-calculus)—an elegant, abstract system for expressing computation through pure mathematical functions. In 1936, he used it to prove that no universal algorithm could ever decide the truth of all mathematical statements, solving Hilbert’s famous Entscheidungsproblem in the negative. This became known as Church’s Theorem, and it revealed something profound: there are hard limits to what any machine can compute.

That same year, Church articulated what we now call the Church–Turing thesis: any problem that can be “effectively calculated” can be computed by a Turing machine—or equivalently, expressed in lambda calculus. When Alan Turing learned of Church’s work, he traveled to Princeton to study under him. Together, they proved their two seemingly different models of computation were fundamentally equivalent, laying the bedrock for all future computer science.

Alonzo Church was born on June 14, 1903, in Washington, D.C., where his father, Samuel Robbins Church, was a justice of the peace [ 5 ] and the judge of the Municipal Court for the District of Columbia. He was the grandson of Alonzo Webster Church (1829−1909), United States Senate Librarian from 1881 to 1901, and great-grandson of Alonzo Church, a professor of Mathematics and Astronomy and 6th President of the University of Georgia. [ 6 ] As a young boy, Church was partially blinded by an air gun accident. [ 7 ] The family later moved to Virginia after his father lost his position at the university because of failing eyesight. With help from his uncle, also named Alonzo Church, the son attended the private Ridgefield School for Boys in Ridgefield, Connecticut. [ 8 ] After graduating from Ridgefield in 1920, Church attended Princeton University, where he was an exceptional student. He published his first paper on Lorentz transformations [ 9 ] in 1924 and graduated the same year with a degree in mathematics. He stayed at Princeton for graduate work, earning a Ph. D. in mathematics in three years under Oswald Veblen.

He married Mary Julia Kuczinski in 1925. The couple had three children: Alonzo Jr. (1929), Mary Ann (1933), and Mildred (1938).

After receiving his Ph.D., he taught briefly as an instructor at the University of Chicago. [ 10 ] He received a two-year National Research Fellowship that enabled him to attend Harvard University in 1927–1928, and the University of Göttingen and University of Amsterdam the following year.

Researchers identify brain ‘entrapment’ patterns associated with depression

Researchers at the Icahn School of Medicine at Mount Sinai have identified distinctive patterns in how the brain transitions between activity states in people with depression, providing new insight into why depressive symptoms can feel persistent and difficult to overcome.

Published online in Nature Communications, the study combined advanced neuroimaging techniques with mathematical modeling to examine how the brain moves between functional activity states over time. The findings suggest that depression may involve a form of “brain-state entrapment,” in which the brain becomes more likely to enter certain patterns of activity and less likely to transition out of them.

“Many patients describe depression as feeling stuck in negative patterns of thought, mood and behavior,” said Yael Jacob, Ph.D., assistant professor of psychiatry at the Dennis S. Charney, MD, Depression and Anxiety Discovery Center at the Icahn School of Medicine at Mount Sinai and senior author of the paper. “Our findings suggest that this experience of being ‘stuck’ may reflect measurable changes in the brain’s underlying dynamics.”

Diffusion model links foam physics to voting shifts and market behavior

A drop of dye added to a glass of water undergoes ordinary diffusion. However, when placed on the surface of a foam, the dye spreads differently—diffusion becomes anomalous. An example of this is the pattern on the froth of a cup of cappuccino. Interestingly, recent research suggests that diffusion equations in a heterogeneous environment can also describe social phenomena, such as election results or the behavior of stock market traders. The study is published in the Chaos: An Interdisciplinary Journal of Nonlinear Science.

The movement of particles in complex media—such as porous materials, gels or foams—bears more resemblance to a random journey through an irregular maze than to a leisurely stroll through a homogeneous space. The presence of local “traps” alongside narrow passages or branches causes the transport of matter or energy to be significantly slowed down or accelerated. Such deviations from classical diffusion are referred to as anomalous diffusion. It is also observed in media with a nonuniform structure.

An international team of physicists from Poland, Croatia, Macedonia and Hungary has undertaken a mathematical description of diffusion in such systems; the Polish side was represented by scientists from the Institute of Nuclear Physics of the Polish Academy of Sciences (IFJ PAN) in Cracow.

Are We the Bootloader for Superintelligence?

A 90 minute interview about AI and our human future.

Dr. Hugo de Garis is a computer scientist, AI researcher, and former professor known for his early work on evolvable hardware, artificial brains, and the long-term risks of superintelligent machines. He coined and popularized the idea of the “Artilect War,” a future conflict between those who want to build godlike artificial intellects and those who believe such systems pose an existential threat to humanity. In the interview, he describes himself as trained in pure mathematics and theoretical physics, formerly a computer science professor, and now focused on broader questions about AI, cosmology, civilization, and the future of humanity.

The interview with Prof. Hugo de Garis centers on his long-standing warning that humanity may face an “Artilect War,” a civilizational conflict over whether to build godlike artificial intellects vastly superior to humans. De Garis argues that future computation, potentially extending from nanotech to femtotech and beyond, could produce minds trillions of trillions of times more capable than ours. He distinguishes between Cosmists, who want to build such beings to expand intelligence into the universe, and Terrans, who oppose them because superintelligence may eliminate or marginalize humanity. He personally remains torn, admiring the cosmic grandeur of posthuman intelligence while recognizing the existential danger.

The conversation also covers AI timelines, recursive self-improvement, AI alignment, the U.S.-China race, the Fermi paradox, simulation theory, cyborgs, cryonics, AI-generated content, the decline of universities, and the future of work. De Garis is impressed by current AI systems, treating them almost as intellectual companions, but he doubts that humanity can guarantee long-term control over recursively improving machines. The central theme is that the question “Should humanity build artilects?” may become the defining political and moral problem of the twenty-first century.

Website https://profhugodegaris.wordpress.com
 is Roman Yampolskiy: https://grokipedia.com/page/roman_yam
 Research papers: https://scholar.google.com/citations?
 Books: AI: Unexplainable, Unpredictable, Uncontrollable https://www.amazon.com/Unexplainable-?tag=lifeboatfound-20
 Considerations on the AI Endgame https://www.amazon.com/Considerations?tag=lifeboatfound-20
 Artificial Superintelligence: A Futuristic Approach https://www.amazon.com/Artificial-Sup?tag=lifeboatfound-20
 Artificial Intelligence Safety and Security https://www.amazon.com/Artificial-Int?tag=lifeboatfound-20
 Social Media X https://twitter.com/romanyam FB / roman.yampolskiy IN / romanyam Ask Roman to speak at your event: https://www.romanyampolskiy.com/

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