Google is using AI to map the human brain, generate synthetic neurons, and speed up one of the most ambitious neuroscience projects ever attempted. But as brain mapping, connectomics, and AI brain decoding move forward, a terrifying question appears: what happens to mental privacy when machines can understand the brain better than we do?
This mini-documentary explores Google’s brain mapping research, synthetic neurons, AI mind decoding, neural privacy, and the future of human thought in the age of artificial intelligence.
CHAPTERS:
00:00 Google’s Brain Mapping Project.
02:00 The Scale of the Human Brain.
04:36 Synthetic Neurons Explained.
06:40 AI Is Already Decoding Thoughts.
10:15 The Rise of Neural Privacy.
14:51 Brain Maps and the Future of AI
17:11 Who Owns Your Mind?
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Welcome to AI Uncovered, your ultimate destination for exploring the fascinating world of artificial intelligence! Our channel delves deep into the latest AI trends and technology, providing insights into cutting-edge AI tools, AI news, and breakthroughs in artificial general intelligence (AGI). We simplify complex concepts, making AI explained in a way that is accessible to everyone.
At AI Uncovered, we’re passionate about uncovering the most captivating stories in AI, including the marvels of ChatGPT and advancements by organizations like OpenAI. Our content spans a wide range of topics, from science news and AI innovations to in-depth discussions on the ethical implications of artificial intelligence. Our mission is to enlighten, inspire, and inform our audience about the rapidly evolving technology landscape.
What exactly did DeepMind find?
Could this discovery help solve longstanding mathematical mysteries?
And what might it mean for cryptography, computing, and our understanding of mathematics itself?
In this video, we explore the science behind the discovery, the role of artificial intelligence in modern research, and why mathematicians around the world are paying close attention.
Whether this breakthrough leads to a revolutionary new theorem or simply a deeper understanding of prime numbers, it demonstrates the growing power of AI to accelerate scientific progress.
👇 What do YOU think?
Will AI help solve the greatest unsolved problems in mathematics?
💬 COMMENT below, 👍 LIKE the video, and 🔔 SUBSCRIBE for more AI breakthroughs, mathematical mysteries, and cutting-edge science discoveries!
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.
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.
In time for the FIFA World Cup 2026, step behind the scenes to see how Atlas mastered the difficult Ghost Rabona kick.
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.
At least 15 malicious plugins found on the JetBrains Marketplace were designed to steal AI API keys from developers.
The campaign, discovered by Aikido Security, includes plugins that act as AI coding assistants, code-review tools, and Git utilities powered by popular AI services such as OpenAI, DeepSeek, and SiliconFlow.
“We detected a coordinated malware campaign on the JetBrains Marketplace,” warns Aikido.