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AI Discovers Geophysical Turbulence Model

One of the biggest challenges in climate science and weather forecasting is predicting the effects of turbulence at spatial scales smaller than the resolution of atmospheric and oceanic models. Simplified sets of equations known as closure models can predict the statistics of this “subgrid” turbulence, but existing closure models are prone to dynamic instabilities or fail to account for rare, high-energy events. Now Karan Jakhar at the University of Chicago and his colleagues have applied an artificial-intelligence (AI) tool to data generated by numerical simulations to uncover an improved closure model [1]. The finding, which the researchers subsequently verified with a mathematical derivation, offers insights into the multiscale dynamics of atmospheric and oceanic turbulence. It also illustrates that AI-generated prediction models need not be “black boxes,” but can be transparent and understandable.

The team trained their AI—a so-called equation-discovery tool—on “ground-truth” data that they generated by performing computationally costly, high-resolution numerical simulations of several 2D turbulent flows. The AI selected the smallest number of mathematical functions (from a library of 930 possibilities) that, in combination, could reproduce the statistical properties of the dataset. Previously, researchers have used this approach to reproduce only the spatial structure of small-scale turbulent flows. The tool used by Jakhar and collaborators filtered for functions that correctly represented not only the structure but also energy transfer between spatial scales.

They tested the performance of the resulting closure model by applying it to a computationally practical, low-resolution version of the dataset. The model accurately captured the detailed flow structures and energy transfers that appeared in the high-resolution ground-truth data. It also predicted statistically rare conditions corresponding to extreme-weather events, which have challenged previous models.

Machine learning reveals hidden landscape of robust information storage

In a new study published in Physical Review Letters, researchers used machine learning to discover multiple new classes of two-dimensional memories, systems that can reliably store information despite constant environmental noise. The findings indicate that robust information storage is considerably richer than previously understood.

For decades, scientists believed there was essentially one way to achieve robust memory in such systems—a mechanism discovered in the 1980s known as Toom’s rule. All previously known two-dimensional memories with local order parameters were variations on this single scheme.

The challenge lies in the sheer scale of possibilities. The number of potential local update rules for a simple two-dimensional cellular automaton is astronomically large, far greater than the estimated number of atoms in the observable universe. Traditional methods of discovery through exhaustive search or hand-design are therefore impractical at this scale.

Deep learning detects foodborne bacteria within three hours by eliminating debris misclassifications

Researchers have significantly enhanced an artificial intelligence tool used to rapidly detect bacterial contamination in food by eliminating misclassifications of food debris that looks like bacteria. Current methods to detect contamination of foods such as leafy greens, meat and cheese, which typically involve cultivating bacteria, often require specialized expertise and are time-consuming—taking several days to a week.

Luyao Ma, an assistant professor at Oregon State University, and her collaborators from the University of California, Davis, Korea University and Florida State University, have developed a deep learning-based model for rapid detection and classification of live bacteria using digital images of bacteria microcolonies. The method enables reliable detection within three hours. The findings are published in the journal npj Science of Food.

Their latest breakthrough involves training the model to distinguish bacteria from microscopic food debris to improve its accuracy. A model trained only on bacteria misclassified debris as bacteria more than 24% of the time. The enhanced model, trained on both bacteria and debris, eliminated misclassifications.

Anthropic’s ‘anonymous’ interviews cracked with an LLM

In December, the artificial intelligence company Anthropic unveiled its newest tool, Interviewer, used in its initial implementation “to help understand people’s perspectives on AI,” according to a press release. As part of Interviewer’s launch, Anthropic publicly released 1,250 anonymized interviews conducted on the platform.

A proof-of-concept demonstration, however, conducted by Tianshi Li of the Khoury College of Computer Sciences at Northeastern University, presents a method for de-anonymizing anonymized interviews using widely available large language models (LLMs) to associate responses with the real people who participated. The paper is published on the arXiv preprint server.

Why the next 25 years could surpass anything in modern memory | Peter Leyden: Full Interview

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“Old systems of the past are collapsing, and new systems of the future are still to be born. I call this moment the great progression.”

Up next, We are living through a slowdown in human progress | Jason Crawford ► • We are living through a slowdown in human…

We are at a tipping point. In the next 25 years, technologies like AI, clean energy, and bioengineering are poised to reshape society on a scale few can imagine.

Peter Leyden draws on decades of observing technological revolutions and historical patterns to show how old systems collapse, new ones rise, and humanity faces both extraordinary risk and unprecedented opportunity.

0:00 We’re on the cusp of an era of progress.

Why AGI Is Impossible

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Humans will merge with AI in 10 years | Ray Kurzweil and Lex Fridman

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Ray Kurzweil is an author, inventor, and futurist.

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Brave New Biology: Intelligence Trumps DNA — with Dr. Michael Levin and Dr. John Vervaeke

Dr. Michael Levin is a professor in the Department of Biology at Tufts University and an associate faculty member at the Wyss Institute at Harvard. He directs the Allen Discovery Center at Tufts, where his team integrates biophysics, computational modeling, and behavioral science to study how cellular collectives make decisions during embryogenesis, regeneration, and cancer.

Levin’s research centers on diverse forms of intelligence and unconventional embodied minds, bridging conceptual theory, experimental biology, and translational work aimed at regenerative medicine. His lab also pioneers efforts in artificial intelligence and the bioengineering of novel living machines.

Read more about Dr. Michael Levin’s work: https://drmichaellevin.org/
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John Vervaeke’s YouTube channel: ‪@johnvervaeke

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Join Fr. Stephen De Young in his Jubilees and the Nephilim course, now streaming live on The Symbolic World: https://www.thesymbolicworld.com/cour… 00:00 — Coming up 01:14 — Intro music 01:40 — Introduction 02:23 — What Michael does 06:19 — Example experiments 07:51 — Memories outside the brain 12:46 — Terminology: memory 13:59 — Communicate to biological cells 15:54 — Limitations? 17:39 — Platonic patterns 34:06 — Incarnation and constraints 39:26 — Causes 49:28 — New beings in new spaces 52:25 — What the Enlightenment dismissed 55:32 — Molecular medicine 57:36 — Subtle bodies 01:00:45 — Ethics 01:03:37 — Medical and meaning applications 01:11:42 — Frightening 01:14:31 — Against the status quo 01:19:03 — Should we dabble in this technology? 💻 Website and blog: http://www.thesymbolicworld.com 🔗 Linktree: https://linktr.ee/jonathanpageau 🔒 BECOME A PATRON: https://thesymbolicworld.com/subscribe Our website designers: https://www.resonancehq.io/ My intro was arranged and recorded by Matthew Wilkinson: https://matthewwilkinson.net/

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