Elon Musk is back on Twitter, and he is talking about a lot of random things, but it also included something about the upcoming release of the Tesla FSD Beta version 10.13, which would be the latest ...

Over the last few issues, we've been talking about the math entity called a matrix. I've given examples of how matrices are useful and how matrix algebra can simplify complicated problems. A messy ...

The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren’t the only path forward. Almost all commercial machine ...

Performing math on multidimensional arrays very efficiently. For example, the Strassen algorithm uses fast matrix math on large matrices. See multidimensional array. THIS DEFINITION IS FOR PERSONAL ...

1. Strassen's method is an important milestone in Computer Science history, largely launching the study of time complexity of algorithms. As the poster child example of a "divide and conquer" ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

Integer linear programming can help find the answer to a variety of real-world problems. Now researchers have found a much faster way to do it. Inspired by the results of a game-playing neural network ...

Is the inclusion of specialized matrix engines in general-purpose processors truly motivated and merited, or is the silicon better invested in other parts? Dr. Satoshi Matsuoka is well-known in ...

In an ideal platform cloud, you would not know or care what the underlying hardware was and how it was composed to run your HPC – and now AI – applications. The underlying hardware in a cloud would ...