I am currently taking a university boot camp about AI. I am looking for a Steam game to reinforce what I’m learning in the class. Ideally, but not a requirement, it will also help me learn the coding and techniques faster and be a fun game in general. I have not really programmed before taking the boot camp, and it just started about a week ago. Will playing TIS-100 be good for this purpose, or will another game make more sense to play?

I have tried out TIS-100, but only for about an hour. Even after reading the manual, I got super stuck and made absolutely no progress. However, this game does involve typing in commands, so I'm wondering if it will be useful for me to play it.

We are primarily using Python in the boot camp. Near the end of this post, I’ll share more info about what is actually being taught in the AI boot camp.

Programming game wise, I have played over 130 hours of Opus Magnum and I love it. While that game has been great at getting my brain to think in terms of programming logic, placing 13 commands on timelines to manipulate 15 elements is a bit different from a game with an actual text-based code editor. Opus Magnum is an awesome game and I’d classify it as a game that’s very easy to learn, challenging to beat, and very hard to master.

I did a Steam tag search for games that have both the programming and education tags. I have tried out some of the following games for about an hour. I liked SpaceChem and Human Resource Machine enough to want to play those from start to finish in the future, for fun. I didn’t like TIS-100 and Hacknet, but I’d be willing to go back to those games if there’s educational value in them that will help me learn Python and AI programming. Are any of these games worth playing to help me in the boot camp?

Programming games that I have tried out for about an hour:
• TIS-100
• SpaceChem: I liked this one a lot. However, I think this one is more like Opus Magnum with the timeline instructions instead of typing in code.
• Human Resource Machine: This one was great too. However, I think this was more about mathematical/logical formulas and less about having to deal with complicated syntax.
• Hacknet: I love the graphics and music of this one, but I got quite stuck after the tutorial. Just like TIS-100 though, I think there might be some value here when it comes to programming.

Programming games that I have not yet played at all:
• SHENZHEN I/O: This game looks cool. Educational value for Python and AI programming?
• Bitburner: It looks like there’s a lot of code and typing in this one. Educational value?
• while True: learn(): I thought about this game during one of the boot camp class sessions where we were covering while True statements! This game looks really cool, fun, and seems like it has a hilarious story. Educational value though? It has learn in the title!
• Buddi Bot: Your Machine Learning AI Helper With Advanced Neural Networking!: This game has AI and neural networking in the title! That’s a good indication of its value to me. However, it seems more of an adventure game with puzzles than a pure programming game. (It reminds me of Rumu). Educational value?

More info about what we’re learning about in the AI Boot Camp:

*** AI BOOT CAMP TOPICS ***

Introduction to AI
Programming for AI
Preparing Data with Pandas
Sourcing Data for AI Projects
Exploring Data through Visualizations
Making Predictions with Data
Unsupervised Learning
Supervised Learning - Linear Regression
Supervised Learning - Classification
Machine Learning Optimization
Data Ethics
Neural Networks
Deep Learning
Natural Language Processing
Transformers
Emerging Topics in AI

*** AI BOOT CAMP GOALS ***

1. Create Python-based scripts to automate the cleanup, restructuring, and rendering of large, heterogeneous datasets.
2. Interact with APIs using Python Requests and JSON parsing techniques.
3. Create in-depth graphs, charts, and tables utilizing a wide-variety of data-driven programming languages and libraries.
4. Apply machine learning techniques to gain knowledge and solve problems.
5. Use unsupervised machine learning models to categorize unlabeled data.
6. Use supervised machine learning models trained on labeled data to make predictions about data.
7. Evaluate and improve the performance of machine learning models by using test data, metrics, and optimization techniques.
8. Use neural networks and deep learning models to make predictions about data.
9. Determine the sentiment of vector-encoded text using NLP and transformers.
10. Apply the fundamentals of NLP and transformer models to describe how Generative AI creates content.
11. Describe recent innovations in AI and their impact on the field of AI.

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If you’ve read this far, thank you. Will playing TIS-100 be a useful and fun accompaniment to the boot camp, or should I go with another game?