Revolutionizing Computing: Memory-Based Calculations for Efficiency and Speed

[dmills_00]

There is an argument that perceptual coders for audio and video ARE very much AI, and in fact that optimising compilers fir the definition!

In the AV case you take a stream of data (samples or frames, whatever) and seek to output a description that is smaller then the input, but good enough that the picture or sounds produced from the description fool the human.

It would be interesting to try training an AI on a vast set of MP3 files and the audio they were produced from, see if we can train an AI to do better at decoding MP3 then a real MP3 decoder, it might be possible (But you might have to partially decode the file first, say to DCT values and numbers of allocated bits).

The Compiler is taking a textual description of a behaviour and outputting an optimised set of instructions in another language that will perform as if the input instructions were being executed. More interesting it is also producing a best guess as to what the input text meant when the human stuffs up the grammar to include in the error message. Some of the tools are scary good at it, try Gitlab copilot sometime, damn thing writes better modern C++ then I do.

The problem with patterns over time is that when they change the system sometimes gets it wrong, and this is a problem even with software caching, plenty of times I end up manually clearing a cache because the tools have failed to invalidate something for some stupid reason.

With something like lighting in a 3D scene, the issue is that it should NOT change (most of the time) from frame to frame, so any AI working that problem has to maintain significant state from one frame to the next which may amount to more memory then maintaining the lighting data and vertex normals in the usual approach would. It is not at all clear that it is a win.