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From Worm to AI: How Control Theory Unlocks Neural Networks

In this video, Dr. Ardavan (Ahmad) Borzou will discuss the control theory in network science and its application in C. elegans \& artificial neural networks. A short history of network science and the basics of control theory will also be reviewed.

Comprehensive Python Checklist (machine learning and more advanced libraries will be covered on a different page):
https://compu-flair.com/blogs/program… Website: www.compu-flair.com Chapters: 00:00 — Introduction 01:52 — Application of control theory in the neural net of worm 03:23 — Networks in Data Science & Seven Bridges of Konigsberg Problem 05:00 — History of network science 06:22 — Basics of control theory 10:23 — Results of applying control theory to the neural net of worm 11:27 — Control theory for artificial neural networks 12:44 — Comprehensive Python checklist for data scientists.

CompuFlair Website:
www.compu-flair.com.

Chapters:
00:00 — Introduction.
01:52 — Application of control theory in the neural net of worm.
03:23 — Networks in Data Science \& Seven Bridges of Konigsberg Problem.
05:00 — History of network science.
06:22 — Basics of control theory.
10:23 — Results of applying control theory to the neural net of worm.
11:27 — Control theory for artificial neural networks.
12:44 — Comprehensive Python checklist for data scientists.

Researchers propose ‘copyleft’ rules for generative AI

The rise of generative artificial intelligence (AI) poses challenges for the free and open-source software (FOSS) community, a global network committed to creating and maintaining publicly available software that anyone can use, modify and share. Many AI models have been built on open-source software but do not reciprocate the transparency that the FOSS community’s principles require, leaving open-source developers uncertain about how these AI tools are using their code.

A study by researchers at Yale’s Digital Ethics Center (DEC) explores a potential solution to this problem based on a concept used in free and open-source software known as “copyleft” licenses—a twist on typical copyright rules that obliges works derived from open-source materials to remain as free and transparent as the original work, rather than relicensing it under more restrictive terms. The study is published in the International Journal Of Law And Information Technology.

The authors propose what they call a Contextual Copyleft AI License (CCAI)—a novel extension of copyleft licensing that would treat generative AI models as derivative works and require AI developers training models on open-source code to make their architecture and training data freely available.

Particle-Simulated Foam In Custom C++ Coastal System

Leonard Saalfrank, also known as OMYOG, has showcased a custom C++ coastal renderer created as a one-week rendering challenge, exploring real-time shoreline rendering, shallow-water simulation, and GPU-driven visual effects.

The project builds on his earlier water-rendering work for Ferocious and expands it with shallow-water waves, GPU-driven breaking waves, and particle-based foam supporting up to 300K GPU particles.

Above is a render handling over 6 million triangles across all passes, using 8K textures at 2K resolution, running at around 250 FPS on an RTX 4,090 Laptop GPU with GPU profiling enabled. Without capture and profiling overhead, performance reportedly increases to around 300 FPS.

Strange winds on seven hot Jupiters reveal strongest signs yet of exoplanet magnetic activity

A team of astronomers has found the strongest evidence yet that some planets outside our solar system may be magnetic. Using the European Southern Observatory’s Very Large Telescope (ESO’s VLT) and the GeminiNorth telescope, the researchers measured wind speeds on seven very hot, Jupiter-like exoplanets.

The observations reveal that the winds on these planets are most likely governed by magnetic fields, providing the first robust measurement of magnetism on planets outside the solar system.

“This breakthrough opens a completely new window on exoplanet research. It’s the first time we can compare the magnetic environments of other worlds—a key step toward ultimately understanding which planets can stay alive, keep their water, and perhaps even, one day, host life as we know it,” says Julia Seidel, an astronomer at the Laboratoire Lagrange, Observatoire de la Côte d’Azur, France and lead author of the study published in Nature Astronomy.

World-first spintronic p-bit on silicon chip points toward larger AI-ready p-computers

A Japan–U.S. collaborative research team has demonstrated the world’s first integrated spintronic probabilistic bit, or p-bit, fabricated on a silicon chip using semiconductor manufacturing processes. The team, consisting of researchers from Tohoku University and the National Institute of Standards and Technology, experimentally verified the operation of the p-bit, a key building block for probabilistic, or p-, computers. The achievement provides a pathway toward large-scale spintronic p-computers for applications such as AI and machine learning.

Many emerging computational problems require efficient exploration of enormous numbers of possible states. Conventional computers, which process binary information, 0 or 1, sequentially, are not always well suited to such highly parallel tasks. Probabilistic computers instead use probabilistic bits, or p-bits, which fluctuate stochastically between 0 and 1 by using intrinsic physical randomness.

Because p-computers can quickly take many states, they are attracting attention as a next-generation computing platform. Among several candidate technologies, spintronics is considered especially promising because nanoscale magnetic devices can naturally generate probabilistic behavior through magnetic fluctuations.

What Quantum Computers Just Proved About Time Is Terrifying

Time is something we experience every day, yet scientists still struggle to fully understand what it really is. Now, advances in quantum computing are allowing researchers to explore some of the deepest mysteries of physics—and the results are raising extraordinary questions about the nature of time itself.

By simulating complex quantum systems that were previously impossible to study, quantum computers are helping scientists test theories about causality, time reversal, and the strange behavior of particles at the quantum level. Some findings appear to challenge our most basic assumptions about how time works.

Researchers are investigating whether time is truly fundamental to the universe or whether it emerges from deeper physical processes we have yet to understand. These ideas may sound like science fiction, but they are being explored by some of the world’s leading physicists.

The implications are profound. If our understanding of time is incomplete, it could affect everything from cosmology and black holes to the future of computing and our understanding of reality itself.

In this video, we examine the groundbreaking quantum experiments, the theories they are testing, and why some scientists believe these discoveries could transform our view of the universe.

Watch until the end to uncover the most mind-bending implications of this research. Don’t forget to LIKE, SHARE, and SUBSCRIBE for more cutting-edge science, quantum mysteries, and incredible discoveries. Comment below: What do you think time really is?

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