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What if we told you AI just created the strongest light material known to humanity? This groundbreaking discovery could revolutionize everything from aerospace to everyday tech. In this video, we break down how artificial intelligence engineered this ultra-light, ultra-strong material—and why it changes the game forever.

Scientists have long searched for the perfect balance of strength and weight, and now, AI has cracked the code. Using advanced algorithms, researchers developed a material that’s lighter than carbon fiber but stronger than steel. Imagine planes, cars, and even buildings becoming more efficient and durable than ever before.

We’ll explore how this AI-designed material works, its potential real-world applications, and what it means for the future of engineering. From military tech to consumer products, this innovation could redefine entire industries. The best part? This is just the beginning of AI-driven material science breakthroughs.

How was this material invented? What makes it so strong yet so light? How will this impact future technology? Can AI design even better materials? This video answers all these questions and more. Don’t miss out on the science behind the next big leap in material engineering—watch now!

#ai.
#artificialintelligence.
#ainews.

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Quantum computers have recently demonstrated an intriguing form of self-analysis: the ability to detect properties of their own quantum state—specifically, their entanglement— without collapsing the wave function (Entangled in self-discovery: Quantum computers analyze their own entanglement | ScienceDaily) (Quantum Computers Self-Analyze Entanglement With Novel Algorithm). In other words, a quantum system can perform a kind of introspection by measuring global entanglement nonlocally, preserving its coherent state. This development has been likened to a “journey of self-discovery” for quantum machines (Entangled in self-discovery: Quantum computers analyze their own entanglement | ScienceDaily), inviting comparisons to the self-monitoring and internal awareness associated with human consciousness.

How might a quantum system’s capacity for self-measurement relate to models of functional consciousness?

Key features of consciousness—like the integration of information from many parts, internal self-monitoring of states, and adaptive decision-making—find intriguing parallels in quantum phenomena like entanglement, superposition, and observer-dependent measurement.

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Anyone who develops an AI solution sometimes goes on a journey into the unknown. At least at the beginning, researchers and designers do not always know whether their algorithms and AI models will work as expected or whether the AI will ultimately make mistakes.

Sometimes, AI applications that work well in theory perform poorly under real-life conditions. In order to gain the trust of users, however, an AI should work reliably and correctly. This applies just as much to popular chatbots as it does to AI tools in research.

Any new AI tool has to be tested thoroughly before it is deployed in the real world. However, testing in the real world can be an expensive, or even risky endeavor. For this reason, researchers often test their algorithms in computer simulations of reality. However, since simulations are approximations of reality, testing AI solutions in this way can lead researchers to overestimate an AI’s performance.

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Together with an international team of researchers from the Universities of Southern California, Central Florida, Pennsylvania State and Saint Louis, physicists from the University of Rostock have developed a novel mechanism to safeguard a key resource in quantum photonics: optical entanglement. Their discovery is published in Science.

Declared as the International Year of Quantum Science and Technology by the United Nations, 2025 marks 100 years since the initial development of quantum mechanics. As this strange and beautiful description of nature on the smallest scales continues to fascinate and puzzle physicists, its quite tangible implications form the basis of modern technology as well as , and are currently in the process of revolutionizing information science and communications.

A key resource to quantum computation is so-called entanglement, which underpins the protocols and algorithms that make quantum computers exponentially more powerful than their classical predecessors. Moreover, entanglement allows for the secure distribution of encryption keys, and entangled photons provide increased sensitivity and noise resilience that dramatically exceed the classical limit.

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Similar to humans going on journeys of self-discovery, quantum computers are also capable of deepening their understanding of their own foundations.

Researchers from Tohoku University and St. Paul’s School, London, have developed a that allows quantum computers to analyze and protect quantum entanglement—a fundamental underpinning of quantum computing. These findings will advance our understanding of quantum entanglement and quantum technologies.

The study was published in Physical Review Letters on March 4, 2025.

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In an unprecedented move, precision medicine provider Human Longevity, Inc. (HLI) has effectively guaranteed its Executive Health Program members that it will prevent them from developing late stage prostate cancer. Such is the company’s belief in its preventive approach, it has announced it is committing $1 million for advanced treatment of any member diagnosed with stage four of the disease or higher while under its care.

Founded in 2013 by genomics pioneer Dr J Craig Venter, San Francisco-based Human Longevity Inc. (HLI) aims to extend human health and performance beyond the traditional focus on treating illness. By continuously analyzing health data from its clients, HLI seeks to identify potential health risks – such as prostate cancer – early, enabling targeted interventions to extend both healthspan and lifespan.

Leveraging data collected from more than 5,000 men over the past decade, HLI claims it has developed what it believes to be the most advanced algorithm for early prostate cancer detection. As preventive medicine continues to demonstrate its capacity to mitigate previously life-threatening conditions, will we see commitments of this nature emerging for more diseases?

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Joscha Bach is a cognitive scientist focusing on cognitive architectures, consciousness, models of mental representation, emotion, motivation and sociality.

Patreon: / curtjaimungal.
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iTunes: https://podcasts.apple.com/ca/podcast… https://pdora.co/33b9lfP Spotify: https://open.spotify.com/show/4gL14b9… Subreddit r/TheoriesOfEverything: / theoriesofeverything Merch: https://tinyurl.com/TOEmerch 0:00:00 Introduction 0:00:17 Bach’s work ethic / daily routine 0:01:35 What is your definition of truth? 0:04:41 Nature’s substratum is a “quantum graph”? 0:06:25 Mathematics as the descriptor of all language 0:13:52 Why is constructivist mathematics “real”? What’s the definition of “real”? 0:17:06 What does it mean to “exist”? Does “pi” exist? 0:20:14 The mystery of something vs. nothing. Existence is the default. 0:21:11 Bach’s model vs. the multiverse 0:26:51 Is the universe deterministic 0:28:23 What determines the initial conditions, as well as the rules? 0:30:55 What is time? Is time fundamental? 0:34:21 What’s the optimal algorithm for finding truth? 0:40:40 Are the fundamental laws of physics ultimately “simple”? 0:50:17 The relationship between art and the artist’s cost function 0:54:02 Ideas are stories, being directed by intuitions 0:58:00 Society has a minimal role in training your intuitions 0:59:24 Why does art benefit from a repressive government? 1:04:01 A market case for civil rights 1:06:40 Fascism vs communism 1:10:50 Bach’s “control / attention / reflective recall” model 1:13:32 What’s more fundamental… Consciousness or attention? 1:16:02 The Chinese Room Experiment 1:25:22 Is understanding predicated on consciousness? 1:26:22 Integrated Information Theory of consciousness (IIT) 1:30:15 Donald Hoffman’s theory of consciousness 1:32:40 Douglas Hofstadter’s “strange loop” theory of consciousness 1:34:10 Holonomic Brain theory of consciousness 1:34:42 Daniel Dennett’s theory of consciousness 1:36:57 Sensorimotor theory of consciousness (embodied cognition) 1:44:39 What is intelligence? 1:45:08 Intelligence vs. consciousness 1:46:36 Where does Free Will come into play, in Bach’s model? 1:48:46 The opposite of free will can lead to, or feel like, addiction 1:51:48 Changing your identity to effectively live forever 1:59:13 Depersonalization disorder as a result of conceiving of your “self” as illusory 2:02:25 Dealing with a fear of loss of control 2:05:00 What about heart and conscience? 2:07:28 How to test / falsify Bach’s model of consciousness 2:13:46 How has Bach’s model changed in the past few years? 2:14:41 Why Bach doesn’t practice Lucid Dreaming anymore 2:15:33 Dreams and GAN’s (a machine learning framework) 2:18:08 If dreams are for helping us learn, why don’t we consciously remember our dreams 2:19:58 Are dreams “real”? Is all of reality a dream? 2:20:39 How do you practically change your experience to be most positive / helpful? 2:23:56 What’s more important than survival? What’s worth dying for? 2:28:27 Bach’s identity 2:29:44 Is there anything objectively wrong with hating humanity? 2:30:31 Practical Platonism 2:33:00 What “God” is 2:36:24 Gods are as real as you, Bach claims 2:37:44 What “prayer” is, and why it works 2:41:06 Our society has lost its future and thus our culture 2:43:24 What does Bach disagree with Jordan Peterson about? 2:47:16 The millennials are the first generation that’s authoritarian since WW2 2:48:31 Bach’s views on the “social justice” movement 2:51:29 Universal Basic Income as an answer to social inequality, or General Artificial Intelligence? 2:57:39 Nested hierarchy of “I“s (the conflicts within ourselves) 2:59:22 In the USA, innovation is “cheating” (for the most part) 3:02:27 Activists are usually operating on false information 3:03:04 Bach’s Marxist roots and lessons to his former self 3:08:45 BONUS BIT: On societies problems.
Pandora: https://pdora.co/33b9lfP
Spotify: https://open.spotify.com/show/4gL14b9…
Subreddit r/TheoriesOfEverything: / theoriesofeverything.
Merch: https://tinyurl.com/TOEmerch.

0:00:00 Introduction.
0:00:17 Bach’s work ethic / daily routine.
0:01:35 What is your definition of truth?
0:04:41 Nature’s substratum is a \.

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This is the Fourier Transform. You can thank it for providing the music you stream every day, squeezing down the images you see on the Internet into tiny little JPG files, and even powering your noise-canceling headphones. Here’s how it works.

The equation owes its power to the way that it lets mathematicians quickly understand the frequency content of any kind of signal. It’s quite a feat. But don’t just take my word for it—in 1867, the physicist Lord Kelvin expressed his undying love for this fine piece of mathematics, too. He wrote, “Fourier’s theorem is not only one of the most beautiful results of modern analysis, but it may be said to furnish an indispensable instrument in the treatment of nearly every recondite question in modern physics.” And so it remains.

Math Will Tear Us Apart

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A drug-resistant type of bacteria that has adapted to health care settings evolved in the past several years to weaponize an antimicrobial genetic tool, eliminating its cousins and replacing them as the dominant strain. University of Pittsburgh School of Medicine scientists made the discovery when combing through local hospital data—and then confirmed that it was a global phenomenon.

The finding, published in Nature Microbiology, may be the impetus for new approaches in developing therapeutics against some of the world’s deadliest . It also validates a new use for a system developed at Pitt and UPMC that couples genomic sequencing with computer algorithms to rapidly detect infectious disease outbreaks.

“Our lab has a front row seat to the parade of pathogens that move through the ,” said senior author Daria Van Tyne, Ph.D., associate professor of medicine in Pitt’s Division of Infectious Diseases. “And when we took a step back and zoomed out, it quickly became apparent that big changes were afoot with one of the world’s more difficult-to-treat bacteria.”

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AlphaTensor–Quantum addresses three main challenges that go beyond the capabilities of AlphaTensor25 when applied to this problem. First, it optimizes the symmetric (rather than the standard) tensor rank; this is achieved by modifying the RL environment and actions to provide symmetric (Waring) decompositions of the tensor, which has the beneficial side effect of reducing the action search space. Second, AlphaTensor–Quantum scales up to large tensor sizes, which is a requirement as the size of the tensor corresponds directly to the number of qubits in the circuit to be optimized; this is achieved by a neural network architecture featuring symmetrization layers. Third, AlphaTensor–Quantum leverages domain knowledge that falls outside of the tensor decomposition framework; this is achieved by incorporating gadgets (constructions that can save T gates by using auxiliary ancilla qubits) through an efficient procedure embedded in the RL environment.

We demonstrate that AlphaTensor–Quantum is a powerful method for finding efficient quantum circuits. On a benchmark of arithmetic primitives, it outperforms all existing methods for T-count optimization, especially when allowed to leverage domain knowledge. For multiplication in finite fields, an operation with application in cryptography34, AlphaTensor–Quantum finds an efficient quantum algorithm with the same complexity as the classical Karatsuba method35. This is the most efficient quantum algorithm for multiplication on finite fields reported so far (naive translations of classical algorithms introduce overhead36,37 due to the reversible nature of quantum computations). We also optimize quantum primitives for other relevant problems, ranging from arithmetic computations used, for example, in Shor’s algorithm38, to Hamiltonian simulation in quantum chemistry, for example, iron–molybdenum cofactor (FeMoco) simulation39,40. AlphaTensor–Quantum recovers the best-known hand-designed solutions, demonstrating that it can effectively optimize circuits of interest in a fully automated way. We envision that this approach can accelerate discoveries in quantum computation as it saves the numerous hours of research invested in the design of optimized circuits.

AlphaTensor–Quantum can effectively exploit the domain knowledge (provided in the form of gadgets with state-of-the-art magic-state factories12), finding constructions with lower T-count. Because of its flexibility, AlphaTensor–Quantum can be readily extended in multiple ways, for example, by considering complexity metrics other than the T-count such as the cost of two-qubit Clifford gates or the qubit topology, by allowing circuit approximations, or by incorporating new domain knowledge. We expect that AlphaTensor–Quantum will become instrumental in automatic circuit optimization with new advancements in quantum computing.

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