Potential future readings that we have assessed in earlier meetings, in order from highest to lowest.
https://jalammar.github.io/illustrated-transformer. We read the first section called "A Higher Level Look." 8/18/23 eval was 4.75.
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.
- Explainable
AI as a reading/discussion topic:
https://en.wikipedia.org/wiki/Explainable_artificial_intelligence. 8/18/23 evaluation was 4 1/6.
- 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
- 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.
- 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.
- https://www.youtube.com/watch?v=BolevVGJk18. This introduces Jonschkowski, Brock, Learning State Representations with Robotic Priors.
- 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.
- Should we commit now to reading the entire paper? 9/1/23: Yes, 3 7/16
- https://en.wikipedia.org/wiki/Markov_decision_process. Should we read/view more of this? Vote on 9/1/23 was 4 1/4.
- 9/8/23: https://www.technologyreview.com/2020/12/04/1013294/google-ai-ethics-research-paper-forced-out-timnit-gebru/. Rating was 2.7375.
- 9/8/23: https://towardsdatascience.com/the-bellman-equation-59258a0d3fa7 . Rating was 4.
- 9/8/23: Brooks, R., 2017, Seven Deadly Sins of AI Prediction, in serveinfo\AIstudyGroup. Rating was 3 1/8.
- https://www.vox.com/the-
highlight/23447596/artificial- intelligence-agi-openai-gpt3- existential-risk-human- extinction. 8/18/23: vote was 2.5.
No comments:
Post a Comment