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Check out the math & physics courses that I mentioned (many of which are free!) and support this channel by going to https://brilliant.org/Sabine/ where you can create your Brilliant account. The first 200 will get 20% off the annual premium subscription. You have probably heard people saying that the problem with quantum mechanics is that it’s non-local or that it’s impossible to understand or that it defies common sense. But the problem is much simpler, it’s that quantum mechanics is a linear theory and therefore doesn’t correctly reproduce chaos. Physicists have known this for a long time but it’s rarely discussed. In this video I explain what the problem is, what physicists have done to try and solve it, and why that solution doesn’t work. Subscribe to my weekly science newsletter: https://sabinehossenfelder.com/ You find the estimate for Saturn’s moon Hyperion in Zurek’s review https://arxiv.org/abs/quant-ph/0105127 A much easier to digest and more readable review by Michael Berry is here: https://michaelberryphysics.files.wor… you can find a brief summary on Sean Carroll’s blog https://www.preposterousuniverse.com/… 0:00 Intro 0:27 The trouble with Hyperion 4:04 The alleged solution 6:02 The trouble with the solution 7:46 What a real solution requires 10:31 Sponsor message.

Try out my quantum mechanics course (and many others on math and science) on Brilliant using the link https://brilliant.org/sabine. You can get started for free, and the first 200 will get 20% off the annual premium subscription.

Physicists have known that it’s possible to control chaotic systems without just making them even more chaotic since the 1990s. But in the past 10 years this field has really exploded thanks to machine learning.

The full video from TU Wien with the inverted double pendulum is here: • Double Pendulum on a Cart.

The video with the AI-trained racing car is here: • NeuroRacer.

And the full Boston Dynamics video is here: • Do You Love Me?

👉 Transcript and References on Patreon ➜ / sabine.

As discussed. if we went by definition of AI 20 years back we d probably say we are at Agi now. but goal posts are constantly bein moved to superior to humans in all areas. 20 years back would of called it ASI.


Stand back and take a look at the last two years of AI progress as a whole… AI is catching up with humans so quickly, in so many areas, that frankly, we need new tests.