4/1/22: Read paragraphs and decide whether to read more

     Agenda & Minutes

• Welcome to the 10th 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.
• Last time we read through the fourth paragraph of  https://www.marktechpost.com/2022/03/07/an-introduction-to-saliency-maps-in-deep-learning/. This time 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 read 2 paraagraphs. Read more? Vote was 3.6 out of 5.
• https://e2eml.school/transformers.html: "Transformers From Scratch." We read through the 2nd paragraph. Next time we will read the 3rd paragraph and vote on continuing with the document in the future. 

We ended here.

• https://www.youtube.com/watch?v=BolevVGJk18 
• Jonschkowski, Brock, Learning State Representations with Robotic Priors, on disk 
• Ni et al., Learning Good State and Action Representations via Tensor Decomposition, https://arxiv.org/abs/2105.01136.
• 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. 
• e 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

3/18/22: Browsing for concepts

    Agenda & minutes:

• Welcome to the 9th meeting.
• Any other updates/news/inputs/comments?
• Should we try assigning a reading one week, and then discussing it the next week, or stay with the current format where we read together and discuss, a little bit at a time?
• Future meetings:
• Next meeting(s): Any suggestions or requests? 
• Readings, viewings, etc.: 
• We read the first three paragraphs of https://www.marktechpost.com/2022/03/07/an-introduction-to-saliency-maps-in-deep-learning/ last and could potentially continue with it next time.
• Should we try reading the first paragraph or so of several sources, and then pick one to do in more depth? Or go with picking something and trying to get through it? We could vote on this.

• Future reading possibilities:
• CNN basics: https://towardsdatascience.com/the-most-intuitive-and-easiest-guide-for-convolutional-neural-network-3607be47480
• https://e2eml.school/transformers.html: "Transformers From Scratch"
• https://www.youtube.com/watch?v=BolevVGJk18
• Jonschkowski, Brock, Learning State Representations with Robotic Priors, on disk
• Ni et al., Learning Good State and Action Representations via Tensor Decomposition, on disk
• 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. However this paper seems dense for our current state of understanding, so we'll put it on hold.
• 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

3/11/22: Focus on a video about the transformers paper

   Agenda & minutes:

• Welcome to the 8th meeting.
• Any other updates/news/inputs/comments?
• Future meetings:
• Next meeting(s): Any suggestions or requests? 
• Readings, viewings, etc.: 
• We viewed and discussed https://www.youtube.com/watch?v=4Bdc55j80l8
• We read the first two paragraphs of https://www.marktechpost.com/2022/03/07/an-introduction-to-saliency-maps-in-deep-learning/ and could potentially continue with it next time.

Meeting ended here.

• Future reading possibilities:
• https://e2eml.school/transformers.html: "Transformers From Scratch"
• 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. However this paper seems dense for our current state of understanding, so we'll put it on hold.
• 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

3/4/22: Getting bogged down...

  Agenda & minutes:

• Welcome to the 7th meeting.
• Any other updates/news/inputs/comments?
• Any report on AI study group at Arkansas State U. this week? No
• List of many, many conferences: http://conferences.visionbib.com/Iris-Conferences.html.
• Future meetings:
• Next meeting(s): Any suggestions or requests? 
• Readings, viewings, etc.: 
• "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 finished through section 3 so we can start with 3.1 next time.
• This paper seems dense for our current state of understanding, so we'll put it on hold.
• 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.  
• Future reading possibilities:
• https://www.youtube.com/watch?v=4Bdc55j80l8
• 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=TheA.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?
• 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