Week 7 Reflection
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University of Colorado, Boulder *
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1101
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Philosophy
Date
Apr 3, 2024
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docx
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Uploaded by maddie6638
Even though it was not really the main topic this week, I think the most interesting thing we went
over this week was learning more about the similarities and differences between artificial intelligence and humans. I have always found this topic to be fascinating, specifically the contrast between how humans interact with various things versus how machines interact with them.
Carruth (2023) uses the example of AI learning to play the game Breakout. At first the AI did not
know how to play the game and obviously struggled with figuring out how the mechanics worked, which was very similar to what it would be like for a human to play this game (or do anything) for the first time. The AI was quick to figure it out, though, and had essentially mastered the game a few hours after it started. The difference between AI and humans here is the
time it takes for each to learn how to do something. The AI likely learned the patterns and structure of how the game works and was able to basically beat the game as it learned how it worked, but most humans do not have that kind of luxury. Instead of learning the code and mechanics of the game in just a few hours, most humans must take time to practice and have a much longer “trial and error” phase of playing Breakout. I found this interesting because both AI
and humans take very different routes in learning how to complete things – for example, humans may use previous video game experience to learn how to Breakout, while AI learns the code and patterns of Breakout itself.
While Brown (2021) did not explicitly discuss the differences between humans and AI, she did explain some of how AI and machine learning works. She highlighted three different subcategories of machine learning – supervised, unsupervised, and reinforcement – and explained how each one works. Out of the three, I think that supervised machine learning is most
similar to how we generally view AI (machines being trained to do different things based on data
sets), and reinforcement (using trial and error to best figure out how to complete something) to be the most “human” subcategory discussed. AI uses a multitude of different practices to learn and grow, much like humans.
Overall, I think the comparison of humans and AI is very interesting, and we honestly might be more similar than I originally thought. Even though I don’t really like AI, as I have stated multiple times throughout this semester, I think relating it more to humans makes it easier for me
to understand and accept.
Carruth, Christopher M. (2023) ML + DL, 19:10 - 21:23
Brown, Sara (2021) Machine learning, explained.
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