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The Singularity: Everyone’s Certain. Everyone’s Guessing

The Technological Singularity is the most overconfident idea in modern futurism: a prediction about the point where prediction breaks. It’s pitched like a destination, argued like a religion, funded like an arms race, and narrated like a movie trailer — yet the closer the conversation gets to specifics, the more it reveals something awkward and human. Almost nobody is actually arguing about “the Singularity.” They’re arguing about which future deserves fear, which future deserves faith, and who gets to steer the curve when it stops looking like a curve and starts looking like a cliff.

The Singularity begins as a definitional hack: a word borrowed from physics to describe a future boundary condition — an “event horizon” where ordinary forecasting fails. I. J. Good — British mathematician and early AI theorist — framed the mechanism as an “intelligence explosion,” where smarter systems build smarter systems and the loop feeds on itself. Vernor Vinge — computer scientist and science-fiction author — popularized the metaphor that, after superhuman intelligence, the world becomes as unreadable to humans as the post-ice age would have been to a trilobite.

In my podcast interviews, the key move is that “Singularity” isn’t one claim — it’s a bundle. Gennady Stolyarov II — transhumanist writer and philosopher — rejects the cartoon version: “It’s not going to be this sharp delineation between humans and AI that leads to this intelligence explosion.” In his framing, it’s less “humans versus machines” than a long, messy braid of tools, augmentation, and institutions catching up to their own inventions.

Scientific Notation Explained | Large & Small Numbers + Practice Questions

Scientific notation is a system developed to represent extremely large and extremely small numbers in a way that is easy to read, write, and understand. In chemistry and physics, many values such as the mass of an electron are too large or too small to be written conveniently in standard notation.

In this video, you will learn:

What scientific notation is and why it is used.
How to write numbers in the form a × 10ⁿ, where a is between 1 and 10
How to convert large numbers into scientific notation.
How to convert small numbers into scientific notation.

The LARS rule:
Left → Add to the exponent.
Right → Subtract from the exponent.

We also discuss how the direction of decimal movement affects the exponent and why the same rules apply to both very large and very small numbers.

📌 At the end of the video, you’ll find practice multiple-choice questions (MCQs) to test your understanding, including a real-life chemistry example involving the mass of an electron.

A possible first-ever Einstein probe observation of a black hole tearing apart a white dwarf

On July 2, 2025, the China-led Einstein Probe (EP) space telescope detected an exceptionally bright X-ray source whose brightness varied rapidly during a routine sky survey. Its unusual signal immediately set it apart from ordinary cosmic sources, triggering rapid follow-up observations by telescopes worldwide.

Study of the event was coordinated by the EP Science Center of the National Astronomical Observatories, Chinese Academy of Sciences (NAOC), with participation from multiple research institutions in China and abroad. Astrophysicists from the Department of Physics at The University of Hong Kong (HKU), who are integral members of the EP scientific team, worked together with the broader collaboration to interpret the event, proposing that it may mark the moment when an intermediate-mass black hole tears apart and consumes a white dwarf star.

If confirmed, this would be the first observational evidence of such an extreme black hole “feeding” process. The findings have been published as a cover article in Science Bulletin.

The origin of magic numbers: Why some atomic nuclei are unusually stable

For the first time, physicists have developed a model that explains the origins of unusually stable magic nuclei based directly on the interactions between their protons and neutrons. Published in Physical Review Letters, the research could help scientists better understand the exotic properties of heavy atomic nuclei and the fundamental forces that hold them together.

While every chemical element is defined by a fixed number of protons in its atomic nucleus, the number of neutrons it contains is far less constrained. For almost every known element, there are at least two different nuclear configurations, or isotopes, which vary only in their number of neutrons.

However, if the number of protons and neutrons becomes too unbalanced in either direction, the nucleus becomes unstable. Since heavier elements tend to have fewer stable isotopes, these radioactive nuclei grow increasingly rare as this imbalance increases. Yet for certain specific numbers of protons and neutrons (collectively known as “nucleons”), some isotopes are found to be exceptionally stable, for reasons that physicists have struggled to fully explain.

AI method accelerates liquid simulations by learning fundamental physical relationships

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the chemical potential—an indispensable quantity for describing liquids in thermodynamic equilibrium. The researchers present their findings in a new study published in Physical Review Letters.

Many common AI methods are based on the principle of supervised machine learning: a model—for instance, a neural network—is specifically trained to predict a particular target quantity directly. One example that illustrates this approach is image recognition, where the AI system is shown numerous images in which it is known whether or not a cat is depicted. On this basis, the system learns to identify cats in new, previously unseen images.

“However, such a direct approach is difficult in the case of the chemical potential, because determining it usually requires computationally expensive algorithms,” says Prof. Dr. Matthias Schmidt, Chair of Theoretical Physics II at the University of Bayreuth. He and his research associate Dr. Florian Sammüller address this challenge with their newly developed AI method. It is based on a neural network that incorporates the theoretical structure of liquids—and more generally, of soft matter—allowing it to predict their properties with great accuracy.

The surprise in physics: two electrons do not become entangled “all at once,” but rather the correlation forms first and then the temporal signature appears in the leak

Physicists show electron entanglement forms over attoseconds, with correlations appearing before timing signatures emerge.

It’s Official: Astronomers Detect Complex Sulfur Molecule in Interstellar Space

In the heart of our galaxy, scientists have discovered the largest sulfur-bearing molecule ever detected beyond Earth, with significant implications for the study of the cosmic origins of life.

The chemical is known as thiepine, or 2,5-cyclohexadiene-1-thione (C₆H₆S), a ring-shaped sulfur-bearing hydrocarbon produced in biochemical reactions.

When examining the molecular cloud G+0.693–0.027, a star-forming region about 27,000 light-years from Earth near the center of the Milky Way, astronomers from the Max Planck Institute for Extraterrestrial Physics (MPE) and the CSIC-INTA Centro de Astrobiología (CAB) detected this complex molecule in space for the first time.

Old galaxies in a young universe?

The standard cosmological model (present-day version of “Big Bang,” called Lambda-CDM) gives an age of the universe close to 13.8 billion years and much younger when we explore the universe at high-redshift. The redshift of galaxies is produced by the expansion of the universe, which causes emitted wavelengths to lengthen and move toward the red end of the electromagnetic spectrum.

The further away a galaxy is, the more rapidly it is moving with respect to us, and so the greater is its redshift; and, given that the speed of light is finite, the more we travel to the past. Hence, measuring the age of very high redshift galaxies would be a way to test the cosmological model. Galaxies cannot be older than the age of the universe in which they are; it would be absurd, like a son older than his mother.

In work carried out with my colleague, Carlos M. Gutiérrez, at the Canary Islands Astrophysics Institute (IAC; Spain), we analyzed 31 galaxies with average redshift 7.3 (when the universe was 700 Myr old, according to the standard model) observed with the most powerful available telescope available: the James Webb Space Telescope (JWST).

How charges invert a long-standing empirical law in glass physics

If you’ve ever watched a glass blower at work, you’ve seen a material behaving in a very special way. As it cools, the viscosity of molten glass increases steadily but gradually, allowing it to be shaped without a mold. Physicists call this behavior a strong glass transition, and silica glass is the textbook example. Most polymer glasses behave very differently, and are known as fragile glass formers. Their viscosity rises much more steeply as temperature drops, and therefore they cannot be processed without a mold or very precise temperature control.

There are other interesting differences between different glass formers. Most glasses exhibit relaxation behavior that deviates strongly from a single-exponential decay; this means that their relaxation is characterized by a broad spectrum of relaxation times, and is often associated with dynamic heterogeneities or cooperative rearrangements.

A long-standing empirical rule links the breadth of the relaxation spectrum to the fragility of the glass: strong glass formers such as silica tend to have a narrow relaxation spectrum, while fragile glass formers such as polymers have a much broader relaxation spectrum.

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