Readings

Topics Covered in Programming Humanity:

1. Data: Are We Data?
2. Processing: Brains and Computers, Algorithms and Consciousness
3. Output: Storytelling Visualized, Persuasion and Deception
4. Networks 
5. Languages: Artificial and Natural
6. Ro/bots: Automating Humanity 
7. Quantifying Uncertainty: Bayes and Probability
8. Databases: The Surveillance State
9. Natural Language: Finding Humanity in Text
10. Models: Predicting Humanity
11. Simulations: Parallel Worlds
12. Evolving Life: Human Data and Computation 
13.  Social Networks: Domesticating the Social Animal
14. Artificial Intelligence: Humanity’s Last Creative Act
15. Final Project Rubrics/Guidelines below
16. Office hours during exam week: 
17. Due dates/times, TR 1:10 due Thursday Dec 18 at 6:30, TR 2:40 Wed Dec 16 at 6:30. 

Grading Rubric for all Final Projects:

• Use the IPHS200 Poster Template, customize color, layout and images as you like.
• For grading, poster should be submitted in ppt form (if working in a different format, please save as a powerpoint and check formatting) and pdf files via Moodle.
• Important details: Please see bottom of AI week for most up-to-date notebooks for sentiment analysis and social network analysis. If you would like to try shorter texts please consult my new-ish methodology detailed in “The Shapes of Cinderella” and my github repo for the code/method. 
• For whichever project you pick, please make sure you have read and incorporated feedback from your miniproject
• Project should be organized into the following sections:
  • Abstract: outline project
  • Background: cite several prior work/related research
  • Methodology: explain tool(s) and methodology
  • Results: explain your findings/visualizations
  • Future Work/Conclusion: Summarize
• Your poster should include visuals, analysis, a conclusion, and suggest avenues for future research. There should also be a list of at least 3-5 references. 
• Project should not plagiarize by directly copying others’ work or analysis. You must search for similar projects (e.g. on kaggle, github, google, etc) and directly reference and cite these.
• Project should be spellchecked and checked for grammatical errors. If you’re unsure, copy and paste text boxes into a word or google document to review. TEXT CLEARLY WRITTEN USING AI WILL BE DOWNGRADED & POSTERS WILL NOT BE ELIGIBLE FOR DIGITAL KENYON. Please make sure to note if you use AI for data analysis, etc. 
• DUE at the end of the exam period for your class. We are not allowed (per college rules) to accept final projects after the end of the semester (i.e. Friday at 4:30). No wiggle room. 

An “A” Data Analytics Project Should

• Clearly explain datasets, both source(s), as well as a detailed description of the metadata (fields, types, distribution, missing values, etc).
• Clearly frame a question and provide relevant research/background information.
• Provide useful and clear visualizations.
• Include exploratory data analysis.
• Find a compelling story in the data.
• Draw conclusions that are non-obvious and non-trivial.

An “A” Technology and Ethics Project Should

• Provide an in-depth explanation of relevant technology
• Cite at least 3 other relevant sources/research projects relating to the project. At least three of your sources should be current (i.e. less than 2 years old). Note your sources should not be mainstream press but instead cutting-edge frontier research. If your topic has already been covered by journalists you are too late. The goal is to be covering technical research and anticipate what has not already been translated for a general audience.
• Clearly frame a question or issue
• Provide analysis of the issue with detailed reference to the technology
• Provide useful visuals
• Discuss nuanced ethical considerations
• Draw conclusions that are non-obvious and non-trivial

An “A”  Shape of Story (Sentiment Analysis) Project Should

• ***Note on using your python notebook. Diachronic (i.e. over time) sentiment analysis: please evaluate at least two, preferably three models and select the *best* model according to crux analysis. Social media analysis: topic modeling requires a large number of tweets to be reliable–great caution is warranted if you have a small sample size (in which case the much cruder word frequency is a safer approach). 
• Clearly frame a question or field of inquiry
• Detail how the dataset was collected and analyzed
• Provide useful visuals
• Include exploratory data analysis
• Find a compelling story in the data
• Draw conclusions that are non-obvious and non-trivial