Usually vector means 1d and matrix means 2d and tensor is term for any dimensional data
A simple way to access tensors is to think of the tensor as something with a few indices., The count of indices (the rank) that a tensor has is an important characteristic: a scalar has rank 0, a 4-vector has rank 1.
- Rank 2 tensors are composites of 16 numbers, multiplied by two contravariant 4-vectors: =
- The transformation matrix of a rank 2 tensor can be known as the multiplication of the transformation matrices of vectors A and B.
Tensor Notion
How a Computer Broke a 50-Year Math Record
Researchers at Google research lab DeepMind trained an AI system called AlphaTensor to find new, faster algorithms to tackle an age-old math problem: matrix multiplication. Advances in matrix multiplication could lead to breakthroughs in physics, engineering and computer science.
AlphaTensor quickly rediscovered - and surpassed, for some cases - the reigning algorithm discovered by German mathematician Volker Strassen in 1969. However, mathematicians soon took inspiration from the results of the game-playing neural network to make advances of their own.
Read the full article at Quanta Magazine: https://www.quantamagazine.org/ai-reveals-new-possibilities-in-matrix-multiplication-20221123/
Correction: At 2:53 in the video, it should say "67%" or "33% less."
00:00 What is matrix multiplication?
01:60 The standard algorithm for multiplying matrices
02:06 Strassen's faster algorithm for faster matrix multiplication methods
03:55 DeepMind AlphaGo beats a human
04:28 DeepMind uses AI system AlphaTensor to search for new algorithms
05:18 A computer helps prove the four color theorem
06:17 What is a tensor?
07:16 Tensor decomposition explained
08:48 AlphaTensor discovers new and faster faster matrix multiplication algorithms
11:09 Mathematician Manuel Kauers improves on AlphaTensor's results
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#math #AlphaTensor #matrices
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