Maxwell: A new kind of model that sits between code and hardware
The article explores the concept of 'Maxwell,' a theoretical model designed to bridge the gap between code execution and hardware. It argues that current AI is limited to writing code, whereas future systems should be able to dynamically optimize execution during runtime.
Why it matters
It proposes a paradigm shift in computing where AI models move from static code generators to active, self-optimizing participants in the execution layer.
All Thinking Things "What we want is a machine that can learn from experience. The possibility of letting the machine alter its own instructions provides the mechanism for this."
— Alan Turing, London Mathematical Society, February 1947
In 1952 Alan Turing played a game of chess against his colleague Alick Glennie. He did so by taking on the role of a computer: he had written a chess program on a few sheets of paper, and at every turn he followed the instructions of his program to the letter, until they produced a move. Every move took him about half an hour. He lost.
Throughout that game, Alan did exactly what a computer does. He carried out the program faithfully, without improving a line of it, however obvious the improvements must sometimes have looked.
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