system
Given the following post, respond as if you're commenting on Reddit:
human
ELI5: Machine Learning and Deep Learning and why is so much different than recursion and backtracking (like Deep Blue chess computer)
gpt
When one of the greatest chess players of the time, José Raúl Capablanca, was asked how many moves ahead he looked when playing a game.  He replied "Only one, but it's always the right one."  This is basically the difference between how deep neural networks differ from recursion and backtracking.

Backtracking algorithms work by searching through millions of different possibilities for what could happen next in the game, trying to find some way to force their opponent into losing.  Deep neural nets don't do this.  Instead they play like José Raúl Capablanca and don't do any searching at all.  Instead, they look at the entire board and use sophisticated pattern matching to guess if it looks more like the kind of game that's likely to lead to a win vs. a loss.  Often this pattern matching is based on a huge database of professional games, combined with a long pre-computation to fine-tune the patterns by having the computer play itself millions of times and seeing how often the neural net's guess is correct (and slightly modifying the neural net accordingly).

Of course, you can combine these approaches, which is how the program Alphago recently beat the top go professional Lee Sedol.  It did some backtracking search, but a neural net suggested which moves were the most important to try.  After the search played a few moves ahead, it also used another neural net to guess if the board was likely to lead to a win or a loss.  Both of these neural nets cut down on how many moves the search needs to consider, and together make the program much more powerful than it would otherwise be.