Does this have any bearing on AI or machine learning. According to Marcus Hutter, the best AI is one that can compress the most (the hutter prize for compression). If compression is the same problem as data transmission, than this might an optimal AI algorithm as well.
This isn't about compressing data, it's rather the opposite: expand the data so that even if a certain portion of it is affected by interference, the original data can still be reconstructed without errors. To take a really simple code, let's say you have this data: 10110 Then you append a checksum (actually a parity bit here) to the data 1+0+1+1+0 = 1 (in binary) 101101 Now let's say there's interference and a bit f…
The two problems (data compression, and noisy channel coding) are tied together quite neatly by information theory though: http://www.inference.phy.cam.ac.uk/mackay/itila/book.html