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Google Just Dropped The Singularity Bomb

Google DeepMind’s Demis Hassabis says humanity may already be standing in the foothills of the singularity. AI agents are now coding, researching, planning, paying, helping with science, and cutting real work from days to minutes. The big question is no longer whether AI is perfect. It’s whether imperfect AI has already become useful enough to speed up everything around it.

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📌 What You’ll See:
Google DeepMind’s warning that we are entering the foothills of the singularity.
SOURCE: https://www.axios.com/2026/05/26/deep… new Gemini for Science tools built to speed up scientific discovery SOURCE: https://blog.google/innovation-and-ai… AWS letting autonomous AI agents make payments and complete transactions SOURCE: https://aws.amazon.com/about-aws/what… AxiomProver helping prove new math results in Lean and Mathlib SOURCE: https://arxiv.org/abs/2602.05090 Biohub’s new world model of protein biology trained across billions of sequences SOURCE: https://biohub.ai/esm/protein ARC-AGI-3 showing the huge gap between today’s frontier AI and human reasoning SOURCE: https://aiforautomation.io/news/2026-… 🚨 Why It Matters This is bigger than another AI model update. Google DeepMind is now openly talking about the singularity, while AI agents are already starting to speed up coding, science, business, and research. Some experts think AGI may be closer than expected, while others say current AI still lacks true intelligence. Either way, the AI race is shifting fast from chatbots into agents that can plan, act, build, discover, and change real workflows. #google #singularity #ai.
Google’s new Gemini for Science tools built to speed up scientific discovery.
SOURCE: https://blog.google/innovation-and-ai…
AWS letting autonomous AI agents make payments and complete transactions.
SOURCE: https://aws.amazon.com/about-aws/what…
AxiomProver helping prove new math results in Lean and Mathlib.
SOURCE: https://arxiv.org/abs/2602.05090
Biohub’s new world model of protein biology trained across billions of sequences.
SOURCE: https://biohub.ai/esm/protein.
ARC-AGI-3 showing the huge gap between today’s frontier AI and human reasoning.
SOURCE: https://aiforautomation.io/news/2026-…

🚨 Why It Matters.
This is bigger than another AI model update. Google DeepMind is now openly talking about the singularity, while AI agents are already starting to speed up coding, science, business, and research. Some experts think AGI may be closer than expected, while others say current AI still lacks true intelligence. Either way, the AI race is shifting fast from chatbots into agents that can plan, act, build, discover, and change real workflows.

#google #singularity #ai

Coral study could help explain infertility and ovarian cancer by decoding cilia-driven fluid flows

A study by researchers at The University of Manchester, carried out alongside the Universities of Melbourne and Copenhagen, could hold the key to understanding the causes of long-term health problems, such as infertility and ovarian cancer.

The study, published in PRX Life, used a combination of high-resolution imaging, flow measurements, and mathematical modeling to examine fluid flows around corals that are driven by cilia—densely packed tiny hairs on the coral’s surface. The collective beating of the cilia contributes to the movement of fluid around the surface of the coral, regulating the animal’s immediate environment through the transport of particles such as oxygen.

The researchers found that heterogeneity in ciliary orientation —small variations in the direction individual cilia beat—can significantly boost transport efficiency. For substances that diffuse slowly through the fluid, this natural variability increased particle transport by more than 50% compared to perfectly aligned cilia. This contrasts with other biological systems, highlighting how coral cilia are uniquely adapted to their environment.

Paul Vitányi

Consider teaching a computer how to read by giving it billions of books. You don’t teach it grammar rules or logic; you simply ask it to play a game: “Look at these words, and guess what word comes next.” To win this game at a world-class level, the computer can’t just memorize phrases. It has to start figuring out how the world works. If it’s reading a mystery novel, it needs to deduce who the killer is to guess the final sentence. If it’s reading a math textbook, it has to understand addition to predict the answer to a problem. This is the core idea explored in a recent scientific paper titled “Algorithmic Compression via Pretrained Neural Networks.”*The researchers look under the hood of today’s Large Language Models (LLMs)—like the AI assistants we use every day—to explain a fascinating mystery: Why does a machine trained merely to predict the next word end up looking like it can think, reason, and solve complex problems? Think about how a ZIP file works on your computer. If you have a massive text file filled with the word “apple” repeated a million times, a compression program won’t save all million words. It will compress it into a short rule: “Repeat ‘apple’ 1,000,000 times.” It turns a massive mountain of data into a tiny, elegant recipe. (learning how to learn). Because the AI is fed a massive, diverse diet of information, it can’t just memorize everything. Instead, it is forced to find the underlying “recipes” or rules behind the data it sees. When you type a prompt into an AI, it doesn’t just look up an answer in a database. It looks at your text, infers the “generative algorithm” (the underlying pattern or logic of what you are asking), and uses that pattern to compress the problem and generate the correct response. In essence, it deduces the hidden rules of the game on the fly. * Discover Complex Logic: When given a sequence of chess moves, the AI doesn’t just guess random moves; it actually reconstructs the abstract rules and evaluations of a chessboard in its digital “mind.” While this framework helps explain why AI is getting so smart, it also opens up big new questions. We know these models are compressing data and finding rules, but we still don’t fully understand the absolute limits of this approach. How close can a practical AI get to that theoretical “perfect” intelligence? What happens when the AI runs out of human data to learn from?

Vitányi was appointed professor of computer science at the University of Amsterdam, and researcher at the National Research Institute for Mathematics and Computer Science in the Netherlands (CWI, initially Mathematical Centre [MC]) where he is currently a CWI Fellow. He was guest professor at the University of Copenhagen in 1978; research associate at the Massachusetts Institute of Technology in 1985/1986; Gaikoku-Jin Kenkyuin (councilor professor) at INCOCSAT at the Tokyo Institute of Technology in 1998; visiting professor at Boston University in 2004, at Monash University in 1996 and at the National ICT of Australia NICTA at University of New South Wales in 2004/2005; visiting professor at and adjunct professor of computer science at the University of Waterloo from 2005.

The universal theory of structure: a fundamental ontology for ontic structural realism

Universal nature of structure.

Ontic Structural Realism (OSR) holds that structure is ontologically fundamental, yet it lacks a precise metaphysical account of structure. Returning to the insight that originally motivated structural realism, I develop a new basis for OSR grounded in the metaphysical foundations of mathematics. This approach draws on the principles of ante rem structuralism and their formal axiomatizations to define Structure Theory (ST), the view that structures exist sui generis and constitute the subject matter of mathematics. ST compels OSR to confront its “collapse problem” of distinguishing physical from mathematical structure. I argue for embracing the collapse by adopting the Mathematical Universe Hypothesis (MUH), which identifies our physical universe as an ante rem structure.

AI makes a major breakthrough in a math problem that had stumped experts for decades

For nearly 80 years, mathematicians have struggled to solve a classic geometry puzzle first posed by Paul Erdős in 1946: the planar unit distance problem. The question posed by the legendary Hungarian mathematician was, on the surface, deceptively simple.

It asks: if you take a piece of paper and add some dots, how many pairs can be exactly the same distance apart? Erdős himself proposed that the maximum number grows only slightly faster than the number of dots. Although many mathematicians agreed with him, no one could find a way to mathematically prove it.

Quantum supremacy just ran into an unexpected rival: An ordinary laptop armed with new math

Using a conventional computer and cutting-edge mathematical tools and code, physicists at the Center for Computational Quantum Physics (CCQ) at the Simons Foundation’s Flatiron Institute and collaborators at Boston University have cracked a daunting quantum physics problem previously claimed to be solvable only by quantum computers.

The technique is so groundbreaking in its efficiency that the researchers were even able to use a personal laptop to solve the problem.

By enabling scientists to squeeze extra problem-solving power from classical computers, the breakthrough methodology is opening new avenues for research on quantum dynamics and may be useful as a protocol for solving problems about finding the optimal solution amid an abundance of feasible ones.

Crystals of space and time: A structural phenomenon that may collapse into tiny black holes

A team from Vienna and Frankfurt has found a formula describing a strange phenomenon: Space and time can form a kind of “crystal” that may turn into a black hole. The results are described in Physical Review Letters.

Alongside the famous gigantic black holes, physics also allows for microscopic versions. They emerge from so-called critical states, when spacetime organizes itself into a regular, crystal-like structure during a process known as critical collapse. A team from Goethe University Frankfurt and TU Wien has now succeeded, for the first time, in describing this phenomenon with an exact mathematical formula using an unusual mathematical trick.

Black holes usually form in spectacular events, such as the death of a massive star. But in theory, arbitrarily small black holes are also possible: tiny microscopic objects that can emerge from special critical states after the slightest addition of energy. Such states may have existed shortly after the Big Bang, when the universe was still a chaotic mixture of particles, potentially giving rise to so-called primordial black holes.

An OpenAI model has disproved a central conjecture in discrete geometry

Today, we share a breakthrough on the unit distance problem. Since Erdős’s original work, the prevailing belief has been that the “square grid” constructions depicted further below were essentially optimal for maximizing the number of unit-distance pairs. An internal OpenAI model has disproved this longstanding conjecture, providing an infinite family of examples that yield a polynomial improvement. The proof has been checked by a group of external mathematicians. They have also written a companion paper explaining the argument and providing further background and context for the significance of the result.

The result is also notable for how it was found. The proof came from a new general-purpose reasoning model, rather than from a system trained specifically for mathematics, scaffolded to search through proof strategies, or targeted at the unit distance problem in particular. As part of a broader effort to test whether advanced models can contribute to frontier research, we evaluated it on a collection of Erdős problems. In this case, it produced a proof resolving the open problem.

This proof is an important milestone for the math and AI communities. It marks the first time that a prominent open problem, central to a subfield of mathematics, has been solved autonomously by AI. It also demonstrates the depth of reasoning these systems now support. Mathematics provides a particularly clear testbed for reasoning: the problems are precise, potential proofs can be checked, and a long argument only works if the reasoning holds together from beginning to end. The method by which the problem was solved is also notable. The proof brings unexpected, sophisticated ideas from algebraic number theory to bear on an elementary geometric question.

Is materialism holding science back? | Adam Frank, Lisa Feldman Barrett, Michael Levin

Lisa Feldman Barrett, Michael Levin and Adam Frank discuss whether science should abandon its materialist framework.

Could a different metaphysics help science to progress further?

With a free trial, you can watch the full debate NOW at https://iai.tv/video/science-beyond-t… centuries, we’ve assumed that science has banished the transcendent and established that reality is entirely physical. But critics argue there are signs that a rigorous materialism might be holding science back. Increasingly, “emergence” is used to account for everything from consciousness to spacetime – a convenient placeholder for what materialist science may be unable to explain. Physicists like Heisenberg and Hawking concluded that science gives us models of reality, rather than final descriptions of its true nature, while there are scientists working in everything from biology to computer science who suggest that dualism is a productive metaphysical framework for their research. Materialism may have enabled science to reach beyond the dogmas of religion, but there are now those who are restlessly probing the limits of materialism itself. Does science need to assume a materialist account of the world or might this have fundamental limitations? Could a different metaphysics help science make progress on key questions, from the origin of life to the mysteries of quantum gravity? Or would abandoning materialism risk returning us to the myths of superstition and religion? #science #materialism #metaphysics Lisa Feldman Barrett is among the most cited scientists in the world for her research on the psychology and neuroscience of emotions. Adam Frank is an astrophysicist who explores the origins of stars, civilizations and consciousness, and is a leading figure in astrobiology and the search for alien life. Michael Levin is a synthetic biologist whose pioneering work in regenerative biology involves building biological robots to probe the nature of life, intelligence and evolution. Güneş Taylor hosts. The Institute of Art and Ideas features videos and articles from cutting edge thinkers discussing the ideas that are shaping the world, from metaphysics to string theory, technology to democracy, aesthetics to genetics. Subscribe today! https://iai.tv/subscribe?utm_source=Y… 0:00 Intro 1:34 Science cannot reveal objective reality 5:28 — History shows that materialism is one of many philosophies of science 8:59 There are some mathematical facts which are discovered, not chosen 12:14 Does materialism prevent mythical and superstitious views of reality? 14:56 There is no 3rd person view of the universe 18:05 Is science truly reproducible? For debates and talks: https://iai.tv For articles: https://iai.tv/articles For courses: https://iai.tv/iai-academy/courses.

For centuries, we’ve assumed that science has banished the transcendent and established that reality is entirely physical. But critics argue there are signs that a rigorous materialism might be holding science back. Increasingly, “emergence” is used to account for everything from consciousness to spacetime – a convenient placeholder for what materialist science may be unable to explain. Physicists like Heisenberg and Hawking concluded that science gives us models of reality, rather than final descriptions of its true nature, while there are scientists working in everything from biology to computer science who suggest that dualism is a productive metaphysical framework for their research. Materialism may have enabled science to reach beyond the dogmas of religion, but there are now those who are restlessly probing the limits of materialism itself.

Does science need to assume a materialist account of the world or might this have fundamental limitations? Could a different metaphysics help science make progress on key questions, from the origin of life to the mysteries of quantum gravity? Or would abandoning materialism risk returning us to the myths of superstition and religion?

#science #materialism #metaphysics.

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