Transcribed
DeepMind's AI Just SOLVED a 100-Year-Old 'IMPOSSIBLE' Math Problem
Sep 27, 2025 ·
32m 31s
Download and listen anywhere
Download your favorite episodes and enjoy them, wherever you are! Sign up or log in now to access offline listening.
DeepMind's AI Just SOLVED a 100-Year-Old 'IMPOSSIBLE' Math Problem
This automatic transcript is brought to you by
Sounder.AI
This is an automatically generated transcript. Please note that complete accuracy is not guaranteed.
Description
What happens when you unleash a powerful Artificial Intelligence on a math problem so difficult, it has stumped the greatest human minds for over a century and comes with a...
show more
What happens when you unleash a powerful Artificial Intelligence on a math problem so difficult, it has stumped the greatest human minds for over a century and comes with a million-dollar prize? You get a glimpse of the future.
Let's get this straight: the Navier-Stokes equations are the chaotic, beautiful, and ridiculously complex mathematical rules that govern everything that flows—from the weather on Earth to the stars in the cosmos. For 100 years, they've guarded a deep secret, a question about "singularities" or "blowups," that made them one of the seven "impossible" Millennium Prize Problems. Human mathematicians couldn't crack the code.
Until now. In this episode, we're unpacking the jaw-dropping scientific breakthrough where DeepMind's AI didn't just help—it became a "discovery engine." We explore how Physics-Informed Neural Networks (PINNs) were used to find entirely new solutions that were once thought impossible, solutions that have now been verified by human experts.
This isn't just a story about mathematics or fluid dynamics; it's about a fundamental shift in how science itself works. We're witnessing the dawn of a new era where AI isn't just a tool, but a creative partner in solving the universe's greatest mysteries.
Ready to witness the future of science unfold? Hit play, subscribe, and then tell us in the comments: what "impossible" problem should AI tackle next?
Become a supporter of this podcast: https://www.spreaker.com/podcast/tech-threads-sci-tech-future-tech-ai--5976276/support.
You May also Like:
🤖Nudgrr.com (🗣'nudger") - Your AI Sidekick for Getting Sh*t Done
Nudgrr breaks down your biggest goals into tiny, doable steps — then nudges you to actually do them.
show less
Let's get this straight: the Navier-Stokes equations are the chaotic, beautiful, and ridiculously complex mathematical rules that govern everything that flows—from the weather on Earth to the stars in the cosmos. For 100 years, they've guarded a deep secret, a question about "singularities" or "blowups," that made them one of the seven "impossible" Millennium Prize Problems. Human mathematicians couldn't crack the code.
Until now. In this episode, we're unpacking the jaw-dropping scientific breakthrough where DeepMind's AI didn't just help—it became a "discovery engine." We explore how Physics-Informed Neural Networks (PINNs) were used to find entirely new solutions that were once thought impossible, solutions that have now been verified by human experts.
This isn't just a story about mathematics or fluid dynamics; it's about a fundamental shift in how science itself works. We're witnessing the dawn of a new era where AI isn't just a tool, but a creative partner in solving the universe's greatest mysteries.
Ready to witness the future of science unfold? Hit play, subscribe, and then tell us in the comments: what "impossible" problem should AI tackle next?
Become a supporter of this podcast: https://www.spreaker.com/podcast/tech-threads-sci-tech-future-tech-ai--5976276/support.
You May also Like:
🤖Nudgrr.com (🗣'nudger") - Your AI Sidekick for Getting Sh*t Done
Nudgrr breaks down your biggest goals into tiny, doable steps — then nudges you to actually do them.
Information
| Author | Byte & Pieces |
| Organization | Byte & Pieces |
| Website | - |
| Tags |
Copyright 2026 - Spreaker Inc. an iHeartMedia Company
Comments