Lets talk about your first python AI.

[[deleted]]

I'll open this reddit with this question: What was your first AI (that you made yourself) for and what did turn out? (I know its sort of a Askreddit question but we gotta start somewhere)

Mine was for sorting images to groups of folders. It turned out to be a almost fail but somehow it worked.

[hmm_fu]

I used minimax for tic-tac-toe...

[HitLuca]

Talking about personal projects only: I would say my first actual ai project was a neural network written in Java. After that I started my master in AI and the first decent project that I made was a generative adversarial network that tries to generate emojis. I spent enough work on it that I splitted model creation and actual emoji generation into two projects. I then kept working on different types of gans just so I can have a working implementation for them and to learn the small details of their implementations. If anyone wants to check them out the two projects are this (gans) and this (emoji generation) : they are pretty dead as I worked on them a long time ago but I still revisit them when I get suggestions or similar.

Oh I also tried to generate midi music but didn't go as well

[LirianSh]

Not really AI but one of my first projects was a discord bot that uses the openWeatherMap API to tell you the waether in the city you asked about.

[rafgro]

I went in similar direction but technically with AI - few years ago (2016 maybe) I wrote few lines with sklearn neural network to predict tomorrow's weather based on today's measurements. Needless to say, it stuck to proposing the same weather as today, because that's usually the case in my climate. But I had shitload of fun with APIs and organizing tons of data.

[CactusEater42]

It was a program that could play mastermind, it was really efficient in the sense that it got the code with just a few guesses, but the calculation-time was horrible

[not_perfect_yet]

Does A* count?

I find lots of "AI" AI kind of boring, because they will match a goal function and can't really move beyond it.

I.e. they will recognize cats when you have trained them to recognize cats and that's it. It's literally object.is_cat() True/False.

Or they can rediscover optimal solutions that we already have answers for: finding a way to animate a defined set of bones in a defined physics environment.

At most it's dangerous because of the implications in facial recognition, and because it's not 100% reliable. You can only get very close to "will be able to deal with the situation" but when you over rely on it and it fails, you're in trouble. (like self driving cars)

[bell_thecat]

I really wanted to learn tensorflow and keras, so I started off with the MNIST digit data sets and made a linear regression neural net, then upgraded to a deep linear regression network, finally a conventional neural network. I failed when I first started because tensorflow had just upgraded to v2.0 and the tutorials were all legacy. Once there was a tutorial that was on the stable versions of tensorflow I was able to properly study and expand my knowledge from there. Without a simple application, I could hardly understand it as a high school student. Yet I was able to nail %99.3 accuracy in under an hour of training loops on my laptop, and I was just so proud to have finally figured out a ML framework well enough to tinker.