5/6/22: Reading and discussion (with co-host)

Agenda & Minutes

• Welcome to the 15th meeting.
• Any updates/news/inputs/comments?
• VK's paper 😲. 
• ??
• Readings, viewings, etc. (co-host: JS)
• Source to read/view in more depth. 
• https://e2eml.school/transformers.html: "Transformers From Scratch." We read up to the sentence "Because probabilities always sum to one, the values in each row will always add up to one." and were looking at the matrix after it, so next time we can finish the diagram and continue. The vote was 4 3/8 out of 5 for this document, though we can always revote as we progress through it.

• Potential future readings that we have assessed in earlier meetings.
• 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.
• 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.
• In a previous meeting we read through the fourth paragraph of  https://www.marktechpost.com/2022/03/07/an-introduction-to-saliency-maps-in-deep-learning/. Previously we read the fifth paragraph, 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.
• Brooks, R., 2017, Seven Deadly Sins of AI Prediction, in serveinfo\AIstudyGroup. Vote was 2.6 out of 5.
• Please send in more suggestions for readings. We can read/view the first paragraph/minute or so of each, assessing each whether to go over it in more depth. To assess each one, vote: Should we read/view more of this? 5=strongly agree, 4=agree, 3=neutral, 2=disagree, 1=strongly disagree.

• MM suggests 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. 
• 2021 Turing Award lecture paper: https://dl.acm.org/doi/pdf/10.1145/3448250 
• 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 
• We have read through section 3 so we could start with 3.1 next time we look at it.
• 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