This week has been my favorite when it comes to learning about LLM( Large Langauge Models). It was especially interesting to learn about how AI breaks down the inputs we subimit into what the video called Token. Futhermore token in the context words the word itself or small pieces of the word. In the context of a image the patches of image would consider a token. These tokens are directly associated with a vector. A Vector is simply a sequence of numbers, and they are meant to encode whatever piece of token is submitted. Then the vectors are passed through an attention block, which allows the vectors to talk to each other to update values. Another key takeaway is understanding transformers and that LLMs can only produce a prediction for what comes next in the passage.

Another major takeaway from this week in understand how to use and prompt your AI. Closer to the end of the week, from the UT article, we learned very important and, I said helpful ways to prompt AI to get the best output. For example when prompting AI know what the capabilities are, be specific, provide an example, and refine the output

The most surprising thing from this week for me is understanding the capabilities of certain AI platforms. In class, we submit a very vague prompt, and each platform produces a different output. Seeing how certain AI can do so much more with such little information is very cool in my opinion. Furthermore, reading the article about generative AI producing biased images was extremely fascinating to me because I honestly have never known that AI was doing such a thing and pushing already existing stereotypes. I really like the part in that article where it talks about the philosophical question of how we ” define bias” and “fairness”.

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