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3. Types of AI Learning
AI models are trained using different methods depending on the data they receive (Kalota, 2024):
Supervised Learning: The model is trained on labeled data (like the "cat" and "not a cat" example). You supervise its learning by telling it the correct answer for each piece of data.
Example: A spam filter is trained on thousands of emails already labeled as "spam" or "not spam."
Unsupervised Learning: The model is trained on unlabeled data and has to find hidden patterns or groupings on its own.
Example: A system is given customer purchase data and figures out on its own that there are distinct groups of customers who buy similar things (e.g., "outdoor enthusiasts" and "tech lovers").
Reinforcement Learning: The model learns by trial and error through interacting with an environment. It receives "rewards" for good actions and "penalties" for bad ones.
Example: An AI trained to play a video game receives a reward when it wins and a penalty when it loses, which teaches it the optimal strategy over time.
References
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms (4th ed.). MIT Press.
Kalota, F. (2024). A Primer on Generative Artificial Intelligence. Education Sciences, 14(2), 172.
https://doi.org/10.3390/educsci14020172
Mueller, S. T., Veinott, E. S., Hoffman, R. R., Klein, G., Alam, L., Mamun, T., & Clancey, W. J. (2021). Principles of Explanation in Human-AI Systems. arXiv.
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