Readings and videos already evaluated, and queued for future study

Potential future readings that we have assessed in earlier meetings, in order from highest to lowest.

1. https://jalammar.github.io/illustrated-transformer. We read the first section called "A Higher Level Look." 8/18/23 eval was 4.75.
2. https://www.youtube.com/watch?v=BolevVGJk18. This introduces Jonschkowski, Brock, Learning State Representations with Robotic Priors. 
1. Should we try the first paragraph(s) of the paper, and then decide on reading the entire paper? 9/1/23: Yes, 4 7/8.
2. Should we commit now to reading the entire paper? 9/1/23: Yes, 3 7/16
3. Foundational Models Defining a New Era in Vision: A Survey and Outlook, https://arxiv.org/abs/2307.13721. 8/18/23 evaluation was 4 1/3.
4. https://en.wikipedia.org/wiki/Markov_decision_process. Should we read/view more of this? Vote on 9/1/23 was 4 1/4.
5. Explainable AI as a reading/discussion topic: https://en.wikipedia.org/wiki/Explainable_artificial_intelligence. 8/18/23 evaluation was 4 1/6.
6. CNN basics: https://towardsdatascience.com/the-most-intuitive-and-easiest-guide-for-convolutional-neural-network-3607be47480. We read up to "What can you see?" and the vote was 4 1/9.
7. 9/8/23: https://towardsdatascience.com/the-bellman-equation-59258a0d3fa7 . Rating was 4.
8. Https://www.youtube.com/watch?v=dDUC-LqVrPU (suggested by BL). We got up to time 4:25. 5/24/24 vote to finish it was 3 5.234/6.
9. https://ai-challenges.nist.gov/genai. 6/7/24 vote was 3 2/7. We ended at "fake or misleading information."
10. We've been reading "Sparks of Artificial General Intelligence: Early Experiments ..." starting at "2.3 Music" (https://arxiv.org/abs/2303.12712). We got up to "We then asked the model to describe the tune in musical terms." 5/24/24 vote to read more of the paper was 3 1/5.
11. https://www.nist.gov/ai-test-evaluation-validation-and-verification-tevv. We read up to "NIST aims to expand these efforts, driving AI research and enabling progress by:". 6/7/24 vote was 3 1.75/8.
12. Chapter 1 part 4: https://huggingface.co/learn/nlp-course/chapter1/4. We read up to "Transformers are language models" and evaluated on 6/14/24 at: 3 1.75/9.
13. 8/25/23: vote was 3.0 (We read the title and 1st sentence. Vote to read more was 4 out of 5). Ni et al., Learning Good State and Action Representations via Tensor Decomposition, https://arxiv.org/abs/2105.01136.
14. https://arxiv.org/abs/2404.04125. We read up to "This trend persists even when controlling for sample-level similarity between pretraining and downstream datasets, and testing on purely synthetic data distributions." in the abstract. Evaluation on 6/14/24 was: 2 8.5/9.
15. 9/8/23: Brooks, R., 2017, Seven Deadly Sins of AI Prediction, in serveinfo\AIstudyGroup. Rating was 3 1/8.
16. 9/8/23: https://www.technologyreview.com/2020/12/04/1013294/google-ai-ethics-research-paper-forced-out-timnit-gebru/. Rating was 2.7375.
17. https://www.vox.com/the-highlight/23447596/artificial-intelligence-agi-openai-gpt3-existential-risk-human-extinction. 8/18/23: vote was 2.5.
18. 8/25/23: vote was 2.0. (6/10/22: vote was 4.0) on the following article coauthored by Timnit Gebru. https://dl.acm.org/doi/pdf/10.1145/3442188.3445922
19. https://en.wikipedia.org/wiki/The_Lawnmower_Man_(film). We read up to "The film was originally marketed" on 6/14/24. Vote to read more was: 1.4.