Here are some sources to scan to see if we want to read more. Please send in more suggestions for readings. We can read/view the first paragraph/minute or so of each, assessing each. Should we read it in more depth? 5=strongly agree, 4=agree, 3=neutral, 2=disagree, 1=strongly disagree.
https://arxiv.org/abs/2301.03044 (MM 2/9/24)
https://www.nvidia.com/en-us/training/online/ (MM 2/9/24)
- Illustrated Guide to Transformers- Step by Step Explanation | by Michael Phi | Towards Data Science (MM 2/9/24)
- Write With Transformer (huggingface.co) (MM 2/9/24)
- Illustrated Guide to Transformers- Step by Step Explanation | by Michael Phi | Towards Data Science (MM 2/9/24)
- [1706.03762] Attention Is All You Need (arxiv.org) (MM 2/9/24)
- GPT5 Next Gen : 7 Upcoming Abilities To Transform AI + The Future of Tech | OpenAI (youtube.com) (MM 2/9/24)
- GitHub
- WooooDyy/LLM-Agent-Paper-List: The paper list of the 86-page paper "The
Rise and Potential of Large Language Model Based Agents: A Survey" by
Zhiheng Xi et al. (MM 2/9/24)
- https://www.youtube.com/watch?v=22wlLy7hKP4&t=91s&ab_channel=rabbit (MM 2/9/24)
- https://www.youtube.com/watch?v=UOZqFMxRpWE&ab_channel=ExitsMedia (MM 2/9/24)
- https://www.youtube.com/watch?v=Rqh6fhcAqpw&ab_channel=ColdFusion (MM 2/9/24)
- https://www.youtube.com/watch?v=uJnhh7YSr5Q&ab_channel=TheAIGRID (MM 2/9/24)
- https://medium.com/version-1/the-rise-of-large-action-models-lams-how-ai-can-understand-and-execute-human-intentions-f59c8e78bc09 (MM 2/9/24)
- Soma Nonaka, Kei Majima, Shuntaro C. Aoki, Yukiyasu Kamitani, Brain hierarchy score: Which deep neural networks are hierarchically brain-like?, iScience, Volume 24, Issue 9, 2021, 103013, ISSN 2589-0042, https://doi.org/10.1016/j.isci.2021.103013.
- Do we really want explainable AI? Edward A. Lee, EECS (Berkeley), https://www.youtube.com/watch?v=Yv13-UPZNGE
How Generative AI Tools Help Transform Academic Research. https://www.forbes.com/sites/
beatajones/2023/09/28/how- generative-ai-tools-help- transform-academic-research/ https://towardsdatascience.com/watching-machine-learning-models-fitting-a-curve-c594fec4bbdb
- Illustrated guide to transformer: https://youtu.be/4Bdc55j80l8
- Attention in neural network: https://youtu.be/W2rWgXJBZhU
- https://youtu.be/rBCqOTEfxvg
- https://youtu.be/S27pHKBEp30
- https://youtu.be/FWFA4DGuzSc
- https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/
- https://www.youtube.com/watch?v=4Bdc55j80l8&ab_channel=The A.I.Hacker-MichaelPhi as a transformer video.
- The Dutch Tax Authority Was Felled by AI—What Comes Next? https://spectrum.ieee.org/artificial-intelligence-in-government
- Anticipative Video Transformer, https://facebookresearch.github.io/AVT/?fbclid=IwAR1RurSM33v8baN10H9JCX_dvVNtscydsLupaB8NMgKOmNIPjIwD3XO2vOA.
- "Deep learning—a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact," https://peerj.com/articles/cs-773. We read the abstract. It is not clear whether we should continue reading material from it. Any opinions/thoughts/comments?
- "Attention is all you need," https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf. Attention is all you need, A Vaswani, N Shazeer, N Parmar… - Advances in neural …, 2017 - proceedings.neurips.cc … Cited by 35,980
- Featured resource: Short and long videos -
- A longer video (44 min. but can skip last 10 minutes about negative result): https://www.youtube.com/watch?v=HfnjQIhQzME&authuser=1. We watched up to time 16:00. However this is a bit ahead of what we want so we'll put it on hold.
- VW has developed an annotated bibliography of LLM articles. We could read these like we would abstracts, then decide whether to read the actual articles based on that.
- Youtube is full of videos about neural nets, transformers, etc. We could check some of those.
- Some quantum computing references we could read as needed:
- - Quantum crossing threshold (free): https://www.nature.com/articles/s41586-021-04273-w
- - Crossing threshold in silicon: https://www.nature.com/articles/s41586-021-04182-y
- - Three-qubit donor processor in Si: https://www.nature.com/articles/s41586-021-04292-7
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