4/8/22: Read more beginnings and vote on continuing with them

      Agenda & Minutes

• Welcome to the 11th meeting.
• Any updates/news/inputs/comments?
• Readings, viewings, etc.: 
• We can read/view the first paragraph/minute or so of several sources, and then vote to pick one to do in more depth. Here is a way to vote to pick the next reading:
• Vote whether to read more, using a scale 1-5: 
• Should we read/view more of this? 
• 5=strongly agree, 4=agree, 3=neutral, 2=disagree, 1=strongly disagree.
• Repeat the process for another article.
• After going through a few articles this way, we can pick the one with the best voting result to read more from.
1. Last time 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.
2. 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.
3. https://e2eml.school/transformers.html: "Transformers From Scratch." We previously read through the 2nd paragraph. This time we will read the 3rd paragraph and vote on continuing with the document in the future. Vote was 4 3/8 out of 5.
4. 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.
5. 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.
6. Brooks, R., 2017, Seven Deadly Sins of AI Prediction, in serveinfo\AIstudyGroup. Vote was 2.6 out of 5.

We got to here.

• https://en.wikipedia.org/wiki/Markov_decision_process.
• https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s42658/
• MM would like to step us through some of the resources available from NVIDIA. 
• MM suggests explainable AI as a reading/discussion topic. 
• MM suggests 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 (from VW):
• - 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