🗂️ To do:

• [x] Create a blog (or a category on a blog) for the course. (You may use any means for publishing your assignments including, but not limited to, a GitHub markdown file, Notion page, medium post, etc.) This wiki page has resources and information on creating your own blog. Additionally, here is some helpful information on privacy options for your blog.
• [x] Read A People’s Guide to AI by Mimi Onuoha and Mother Cyborg (Diana Nucera).
• [x] Explore ImageNet, ImageNet sample images, Kaggle ImageNet Mini 1000. What surprises you about this data set? What questions do you have? Thinking back to last week’s assignment, can you think of any ethical considerations around how this data was collected Are there privacy considerations with the data?
• [x] Using the code examples above, try running image classification on a variety of images. What does the model recognize properly? What does it not recognize? What other aspects of the image affect the classification, including but not limited to position, scale, lighting, etc. You are welcome to use the provided examples as they are or modify the code to create interactive experience that reflects your creativity and curiosity!
• [x] Document your thoughts on MobileNet and image classification in a blog post and add a link to the Assignment 1 Wiki page.

📚 Readings:  A People’s Guide to AI by Mimi Onuoha and Mother Cyborg (Diana Nucera).

AI is often added to existing systems to make them faster, more efficient, or less dependent on human labor. While this can lead to concerns about job displacement, it's important to recognize the value AI brings in other areas. For instance, it plays a significant role in advancing medical research, improving diagnoses, and assisting in complex surgeries. In everyday life, AI enhances convenience, from personalized recommendations to smart home devices. So, while there are valid concerns, AI is also contributing to progress, innovation, and improvements that touch many aspects of our lives.

🔍 Exploring ImageNet, ImageNet sample images, Kaggle ImageNet Mini 1000.

Image Net

• ImageNet is an image dataset organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images.
• There are more than 100,000 synsets in WordNet; the majority of them are nouns (80,000+).

👾 Emoji Scavenger Hunt

I enjoyed how clean the user experience and design of the game was. And the sound effect incorporated into the count down. I played 6 rounds and every time I got stuck on a mouse. I kept loosing cause I don’t own a mouse :(

👩🏻‍💻 Image Classification with Mobile Net:

I remember doing something similar in Creative Computing (project referenced at the bottom), and I was excited to dive into this project using ml5. But after a while, I hit a wall. After about half an hour of trying to troubleshoot, I decided to go back to watching the tutorials. I followed the steps closely, but when I reached the part with ml5, the .predict command wasn’t recognized. Unfortunately, the newer version of ml5 wasn’t compatible with the tutorial, which made it difficult to create a perfectly functioning project. However, I experimented with the example code and ml5 image classification examples, and I worked to combine them into one cohesive project.