Day 4: Computational Thinking

Part 1: First Day of Computational Thinking

My Prior Experience:

My previous coding experience was very limited before I started. I was once a sufficient programmer able to make designs on Code.org from AP Computer Science Principles with coding tools ranging from variables to functions; I was even able to make Wordle on my own time! However, this was all during my freshman year and in javascript, so as a rising senior, I have since then forgotten how to program. With this in mind, I decided to do the intro to Python on Codecademy to give myself a refresher.

What I learned:

Today I learned Python's syntax since I was previously familiar with the syntax of javascript, it was interesting to see the simplicity of Python. However despite the relatively simpler syntax, there were still some new functions that I had to learn, such as the use of %s to recall certain strings.

How Far I Advanced:

I reached to step 1 on Functions

Part 2: ChatGPT Bias

Based on the articles that I read this day, ChatGPT definitely has the potential to be biased, although I have not tried it myself. Other AI systems were mentioned in the articles such as facial recognition or resume scanning algorithms, but ChatGPT was not one of them. But since ChatGPT is based on an accumulation of data and written texts from the web, it very much so has the ability to regurgitate biased, discriminatory, or incorrect information.

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Day 7: Are Animals People?

If you do not meet all the criteria for personhood, does that mean you are not a person? A person could be capable of a certain criteria more than another criteria and still be a person. If a person lacks a certain criteria like morality, they could still be highly sentient or highly reasonable. I think 'matching the criteria' is a spectrum. For example, a being that we consider would normally consider capable of personhood (such as a human) can be born uncapable or weaker in a certain criteria, but that does not inherently make them not a person. The idea of these criteria is a blanket term, and the term 'personhood' itself is broad and nonrestrictive.

Day 11: Dr. Schuman

Guest Speaker: Dr. Schuman Today we got firsthand experience in creating data for a machine learning activity. We visited a site where we were able to feed information through series of pictures of either a facial expression, a hand signal, or certain objects. With this information, our machine was able to learn what traits were associated with certain items through an accumulation of mathematical formulas. Additionally we also got to play with an app that learned from other people's drawings and was able to complete our drawings and portray certain prompts (such as dog, cat, mermaid, lighthouse etc). Overall it was an engaging experience that allowed us to learn about machine learning data and experience collecting data ourselves. Pictured: Training Data of My Classmate

GSSE Blog Daily: Katie Lam

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