6/10/22: Back from break - continue readings

 

Agenda & Minutes

• Welcome to the 18th meeting, June 10, 2022.
• Any updates/news/inputs/comments?
• VK's paper - recap
• The last two meetings were cancelled and that may be why so few people are here for this meeting. Or maybe people are just in Summer mode. In any case DB will send a specific email for next week letting people know we are back to regular meetings.
  
• Readings, viewings, etc. 
• Source to read/view in more depth. 
• https://e2eml.school/transformers.html: "Transformers From Scratch." We read part of "Second order sequence model with skips" but plan to reread starting from that section next week when there will hopefully be more attendees. The vote was 4 3/8 out of 5 for this document, though we can always revote as we progress through it.
• 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. 
• Explainable AI as a reading/discussion topic. 
• 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.  
• 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

• Potential future readings that we have assessed in earlier meetings.
• 2021 Turing Award lecture paper: https://dl.acm.org/doi/pdf/10.1145/3448250. We read the first two paragraphs. Vote was 4.5 to read more.
• Vote on: Dalle-E 2 - how it works: https://www.youtube.com/watch?v=F1X4fHzF4mQ. Should we read/view more of this? Vote was 4.
• 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
• Ni et al., Learning Good State and Action Representations via Tensor Decomposition, https://arxiv.org/abs/2105.01136. We read the title and 1st sentence. Vote to read more was 4 out of 5.
• We have read through the fifth paragraph of  https://www.marktechpost.com/2022/03/07/an-introduction-to-saliency-maps-in-deep-learning, then voted on the priority for reading more of it. Vote: 3.67 out of 5.
• CNN basics: https://towardsdatascience.com/the-most-intuitive-and-easiest-guide-for-convolutional-neural-network-3607be47480. We previously read 2 paragraphs. Read more? Vote was 3.6 out of 5.
• 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? Vote was 3.6 out of 5.
• https://en.wikipedia.org/wiki/Markov_decision_process. Should we read/view more of this? Vote was 3 1/5.  
• 6/10/22: vote was 3.0 on the following article. https://www.technologyreview.com/2020/12/04/1013294/google-ai-ethics-research-paper-forced-out-timnit-gebru/.
• Brooks, R., 2017, Seven Deadly Sins of AI Prediction, in serveinfo\AIstudyGroup. Vote was 2.6 out of 5.