1) I personally have a lot of imposter syndrome when it comes to hacking. I never felt I could claim it at all until I had to make a logical jump about "what's on the other side" such that it proved correct. I would actually say my favorite hacking project has been employing what I've learned from hacking in my regular software work. Understanding networking in order to reverse engineer an old app's HTTPS networking calls was super helpful (from iOS 5 to iOS 12).
I've also done some innocent fun with people--like moving a canvas on a local network when my co-worker said it wasn't possible for people to jump on it and try things... but as I said, they were all innocent and I tell people afterwards. :-)
2) Reinforcement learning is a really cool area of machine learning that is often overlooked. If you can construct an environment where an agent (e.g. robot) can make any of a fixed list of decisions (e.g. left, right, up, or down) and it can sense whether it's doing better or worse in achieving its goals, you can create an algorithm that learns. If this sounds a bit strange, you may remember the AlphaGo algorithm that beat the best human player in the world in 2016. That kind of put reinforcement learning on the map as far as modern publicity.
Below is an example I wrote a little while back to "learn" how to play checkers with an adequate strategy. Granted--it's not all that great, but it should be noted that I'm not using any outside libraries for the actually algorithm update process. There's technically not any higher math required to implement this type of algorithm.
Hey, Christian!
What is your favorite hacking project so far? Something you enjoyed the most, or got the most good out of?
What is an application of data science / AI / Ml that is often overlooked?
1) I personally have a lot of imposter syndrome when it comes to hacking. I never felt I could claim it at all until I had to make a logical jump about "what's on the other side" such that it proved correct. I would actually say my favorite hacking project has been employing what I've learned from hacking in my regular software work. Understanding networking in order to reverse engineer an old app's HTTPS networking calls was super helpful (from iOS 5 to iOS 12).
I've also done some innocent fun with people--like moving a canvas on a local network when my co-worker said it wasn't possible for people to jump on it and try things... but as I said, they were all innocent and I tell people afterwards. :-)
2) Reinforcement learning is a really cool area of machine learning that is often overlooked. If you can construct an environment where an agent (e.g. robot) can make any of a fixed list of decisions (e.g. left, right, up, or down) and it can sense whether it's doing better or worse in achieving its goals, you can create an algorithm that learns. If this sounds a bit strange, you may remember the AlphaGo algorithm that beat the best human player in the world in 2016. That kind of put reinforcement learning on the map as far as modern publicity.
Below is an example I wrote a little while back to "learn" how to play checkers with an adequate strategy. Granted--it's not all that great, but it should be noted that I'm not using any outside libraries for the actually algorithm update process. There's technically not any higher math required to implement this type of algorithm.
github.com/rcdilorenzo/TicTacToe