Readings and videos not yet evaluated

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.

--------------------Candidates pasted here on 3/7/2026--------------------------- 

• On the Origin of Algorithmic Progress in AI, https://arxiv.org/pdf/2511.21622
• The Misclassification of Autistic Writing as AI-Generated. Paywalled at https://link.springer.com/chapter/10.1007/978-3-031-98420-4_7. See https://drive.google.com/file/d/1EAq0ZjVpwtDPfVcoTZ0PILxyrFP5X6KS/view, p. 89. 
• Video 2: https://youtu.be/BFU1OCkhBwo?si=KnHNuedfNaTkZzwp
• Video 3: https://youtu.be/x4ZY25OU4Ys?si=K1xp4uA14vBdCgOI (VK)
• Video 4: https://www.youtube.com/watch?v=giT0ytynSqg (Hinton on the danger of AI)
• Read from Y. Qian, Prompt Engineering in Education: A Systematic Review of Approaches and Educational Applications, Journal of Educational Computing Research, 2025, Vol. 63(7-8) 1782–1818. ...\AIgoodBadUgly                               Abstract: The effectiveness of generative AI tools in education depends largely on prompt engineering—the practice of designing inputs and interactions that guide AI systems to produce relevant, high-quality outputs. This systematic literature review examines empirical studies published since the release of ChatGPT in late 2022, identifying two broad approaches of prompting strategies: technique-based, which targets specific learning goals, and process-based, which supports cognitive engagement and collaborative thinking with AI. The review identifies key educational applications of prompt engineering, notably in two overarching areas: critical skills development and the automation of educational functions. It also highlights emerging trends, such as the integration of multimodal AI and the growing influence of advanced AI reasoning capabilities. By mapping this evolving landscape, the findings provide a foundational understanding of prompt engineering as both a technical skill and a pedagogical strategy in AI-supported learning environments.
• DR website/book(s): Site: https://www.thecenterforethicalai.com. We read the first page of chapter 1 in his ebook Ethical AI Integration.
• New chatbot ‘outperforms PhDs on literature reviews’, finds Nature study https://share.google/de1pa28PKWKBOnq4R
• Check a couple minutes of https://www.youtube.com/watch?v=RNF0FvRjGZk to see if we should view it in detail.

 -------------------------------Candidates from latish 2025------------------------- 

https://arxiv.org/abs/2511.21622

Towards Monosemanticity: Decomposing Language Models With Dictionary Learning  (https://transformer-circuits.pub/2023/monosemantic-features/index.html); Bricken, T., et al., 2023. Transformer Circuits Thread.

Neural Networks, Deep Learning: The basics of neural networks, and the math behind how they learn, https://www.3blue1brown.com/topics/neural-networks. (We would need to pick a specific one later.)

LangChain free tutorial, https://www.youtube.com/@LangChain/videos. (The evaluation question is, do we investigate this any further?)

Chapter 6 recommends material by Andrej Karpathy, https://www.youtube.com/@AndrejKarpathy/videos for learning more. What is the evaluation question? "Someone should check into these and suggest something more specific"?

Chapter 6 recommends material by Chris Olah, https://www.youtube.com/results?search_query=chris+olah

Chapter 6 recommended https://www.youtube.com/c/VCubingX for relevant material, in particular https://www.youtube.com/watch?v=1il-s4mgNdI

Chapter 6 recommended Art of the Problem, in particular https://www.youtube.com/watch?v=OFS90-FX6pg

LLMs and the singularity: https://philpapers.org/go.pl?id=ISHLLM&u=https%3A%2F%2Fphilpapers.org%2Farchive%2FISHLLM.pdf (summarized at: https://poe.com/s/WuYyhuciNwlFuSR0SVEt). (Old eval from 6/7/24 was 4 3/7.) 

--------------------------older candidates---------------------

• 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