https://matthew-van-der-hoorn.notion.site/matthew-van-der-hoorn/Book-Reading-bc745728387b4369b5b63739292c9ce7

van der Hoorn's suggestions for reading

31:00 Zettelkasten as system for ongoing learning (not bound to an outcome)

• see zk on process vs outcomes

People like me that have learned about this way for thinking and the challenge that is finding alike minds that want to explore and build this for the next generation. Sometimes the difference between a blue thought and a revolution is having who to talk to about it. How can we connect people working on the same problem how do you put in the same metaphorical room the people trying to push the envolope

Right, so the modern equivalent would be to design a game or an 3d animation in an intuitive way, yet the integration of pipeline in this systems makes it so that not even experienced professionals in the area cn develop a short film or an interactive experience through art that eases people into coding.

I think we need to do a better job at this. If the system that allowed us to design the processes also taught it to people then we wouldn't have to chose between improving the learning curve and the system there should all be one. why did we stop shipping manuals with our tech..? ahh it was because we stopped caring about what the people that designed the tool thought.

The simple idea that new systems are harder than old even if they're better because they are new and people have to put more effort into using them.

What I feel it's really important is the idea that the measure of a good system isn't only how easy it is to learn, if we only evaluate systems by their learning curve we'll be face with only being able to advance society at the speed of the slower adopter. Therefore we need to * Segment and dream about the future * Be mindful of the gap between where we are and where the vision is pushing towards since there has to be a common point that collectively moves us forwards

Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation

"SPRING: GPT-4 Out-performs RL Algorithms byStudying Papers and Reasoning"

Them's fighten' words!

I haven't read it yet, but we're putting it on the list for this fall's reading group. Seriously, a strong result with a very strong implied claim. they are careful to say it's from their empirical results, very worth a look. I suspect that amount of implicit knowledge in the papers, text and DAG are helping to do this.

The Big Question: is their comparison to RL baselines fair, are they being trained from scratch? What does a fair comparison of any from-scratch model (RL or supervised) mean when compared to an LLM approach (or any approach using a foundation model), when that model is not really from scratch.

I love this image:

Via Bryan Mathers at https://bryanmmathers.com/education-work/

Wang et. al. "Scientific discovery in the age of artificial intelligence", Nature, 2023.

A paper about the current state of using AI/ML for scientific discovery, connected with the AI4Science workshops at major conferences.

(NOTE: since Springer/Nature don't allow public pdfs to be linked without a paywall, we can't use hypothesis directly on the pdf of the paper, this link is to the website version of it.)

Free QR code sites

The data of the past is congruent with the current psychological trends that face the education system of today. Developmentalists have charted how children construct and revise intuitive theories. In turn, a variety of theories have developed because of the greater use of statistical information that supports probabilistic contingencies that help to better inform us of causal models and their distinctive cognitive functions. These studies investigate the physical, psychological, and social domains. In the case of intuitive psychology, or "theory of mind," developmentalism has traced a progression from an early understanding of emotion and action to an understanding of intentions and simple aspects of perception, to an understanding of knowledge vs. ignorance, and finally to a representational and then an interpretive theory of mind.

The mechanisms by which life evolved—from chemical beginnings to cognizing human beings—are central to understanding the psychological basis of learning. We are the product of an evolutionary process and it is the mechanisms inherent in this process that offer the most probable explanations to how we think and learn.

Bada, & Olusegun, S. (2015). Constructivism Learning Theory : A Paradigm for Teaching and Learning.

"Are Pre-trained Convolutions Better than Pre-trained Transformers?"

GPT-2 Introduction paper

Language Models are Unsupervised Multitask Learners A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, (2019).

GPT-3 introduction paper

• for: symmathesy, mutual learning, mutual transcontextual learning, individual collective entanglement, Indyweb, Indraweb, Indynet, Indranet

• definition: symmathesy

  • mutual transcontextual learning in living systems

• comment

  • symmathesy lay at the heart of the Indyweb and Indraweb

• for: symmathesy, mutual learning, mutual transcontextual learning, individual collective entanglement, Indyweb, Indraweb, Indynet, Indranet

• definition: symmathesy

  • mutual transcontextual learning

• comment

  • symmathesy lay at the heart of the Indyweb and Indraweb

proof of effort: How can teachers know that students have done the work?

Hypothes.is is an example of a tool which shows reading effort.

(~13:00) Koe argues for making information relevant (Dr. Sung always says you must make info relevant) through the learning for the solving of a particular problem, either for a client, your business, or your personal life. Your problem becomes the lense through which you learn.

For self-education this is ideal.

Dr. Sung's approach differs in that he advocates for the creation of relevancy through inquiry (the asking of relational questions) which is also incredibly powerful, however this is more suited to gaining more motivation for forced learning, i.e., in the formal education system.

In addition, Koe's lense is, I think, more of a high-level filter, whereas Sung's questioning is applicable on the content level. Therefore, both approaches could be, and should be, combined into the same overall (self-)educational system.

(~10:20) Koe makes a very, very, very valid point about education:

I quote: "There is one thing that the school system did get right which is consistent, daily education in hopes for a better future. But, schools don't prioritize curiosity, so most people hate learning by the time they graduate." (emphasis added by me)

The larger point that Koe is making is that if we own anything in life, it is our mind; for everything else can be taken away from us; as such, we must spend a significant amount of effort to cultivate it, grow it, care for it, and make it unique.

(~6:07) Koe argues that specializing, or focusing on one aspect only, limits your potential in every conceivable way.

I think I agree, yet I do also think there is a place for that... It depends on the person and what they enjoy. However, I might still be mistaken.

Dan Koe seems to argue against a specialistic education based on the argument that it is nigh-impossible for a teenager to decide what they want (to be) for the rest of their lives. He also gives the argument that it results in a lack of creativity and underlying knowledge (that which connects the dots, instead of compartmentalization) which would result in abnormal performance.

I can bypass the limitation of the first point by giving the counter-point that when one has an insane amount of metacognition, which can be trained, it does not matter if one changes path later; why? Because one can easily learn the new subject matter and skills.

However, the second point is interesting and I think I agree with it. That said, I think there is a continuum, instead of only two points, between super-specialists and super-generalists. I myself enjoy specializing. And I believe a team of specialists (that can also work together) can accomplish much more than one (or even multiple) generalist.

https://web.hypothes.is/help/annotating-youtube-videos-with-the-hypothesis-lms-app/

Walkthrough for how to add YouTube Videos into LMS assignments for annotation with H.

https://web.hypothes.is/blog/exciting-new-feature-annotate-youtube-videos-with-hypothesis/

Wishing this was easier within YouTube directly instead of hidden within the LMS. Of course, there's always still https://docdrop.org/ for this instead.

What is done right before and right after sleep sets the stage for literally everything.

How you do anything is how you do everything.

Title: Delays, Detours, and Forks in the Road: Latent State Models of Training Dynamics Authors: Michael Y. Hu1 Angelica Chen1 Naomi Saphra1 Kyunghyun Cho Note: This paper seems cool, using older interpretable machine learning models, graphical models to understand what is going on inside a deep neural network

Link: https://arxiv.org/pdf/2308.09543.pdf

Ideally in the evening, before sleep, do some activity or activities that turn off the mind. You want to relax and stop thinking so much.

Interestingly enough, forgiveness, or the act of forgiving makes relaxing easy. So, if you have someone, or even yourself, to forgive... Do this right before going to sleep :)

Apparently, cold shower for roughly 3-4 minutes (rather than a hot shower) before sleep are helpful for sleep, as it decreases the core body temperature.

When you wake up, get sunlight in. Andrew Huberman also advocates for that. It tells the brain and body to wake up. It creates cortisol.

Can be combined with movement/exercise as well which also increases sleep quality. (Movement should not to be too late, however.)

Consistency is key when it comes to sleep.

Always go to bed at the same time and wake up at the same time.

Apparently, some Magnesiums can help with deep sleep.

Author takes 400mg.

It is important to block blue light in the evening. Blue light sends signals to your body to be awake.

One of the things to optimize sleep is to take care of meal timing. Author eats: - Breakfast at 8 - Lunch at noon (12) - Dinner between 5 and 6.30

Discipline and consistency is important here.

Essential is to eat dinner 3+ hours before you go to sleep.

Food increases core body temperature which negatively impacts sleep.

Sleep is the absolute foundation for top-performance.

Measure it. Track it. Optimize it.

After that, results WILL go into the sky.

The sixth step, most essential as well, is to Accept the Wins

Owning the losses means also owning the wins.

The fifth step is to have Selective Memory only choose to remember the events that serve the future. Things that help to improve in the future.

It's like Marcus Aurelius wrote (in a slightly different way): "Ask yourself at any moment, is this essential?" In this way it would become: "Ask yourself at any moment, does this help me?"

The fourth step is to Apply the Reflection. Adjust behavior based on reflection. We improve not for validation, we improve for ourselves (stoic philosophy)

Document the journey in for example a journal. Make a comparison between what would be done in the past and what will be done in the future.

Data collection. Measurement.

Marginal Gains. It's sort of a daily continous Kolb's cycle but in a more lightweight form. I can already see the power in this. Absolute gem.

Could also be overwhelming if applied to a lot. therefore, use the power law and focus on what is essential to life change. (thanks Dr. Benjamin Hardy.)

The third step is to Reflect and think into the future. Extract meaning and lessons from the failure. Think about opportunities.

Reflection increases confidence. Kolb's can help with this a lot.

The second step is Sit with the loss in order to find the (root) cause of the loss or pain. Do not avoid the pain, don't distract oneself, instead embrace it and feel it.

Endurance can be trained. Comfort with uncomfortability can be trained in the same way.

Accept and sit in the fire. Embrace the turmoil.

The first step to deal with loss of any kind, be it a girlfriend, love, job, purpose, etc. Is to ACCEPT YOU LOST

Failure = Failure.

Failure is inevitable, and will be part of any learning process. Therefore it should not be avoided at all costs. It should be used to learn from. However; there is also no point in seeking failure, for if failure is not something negative, there is no point to improve (says the author at least)

• for: open source, indyweb, open learning commons, radical collaboration, individual / collective entanglement
• paraphrase
• quote
  • to live for the common good is a very good purpose but
  • purpose is a gift and the purpose of our life here on Earth is to change the environment which we met for something better because there is always an opportunity for something better
• author
  • Obiora Ike
• quote
  • I would urge us all to be in a learning mode and
  • we when we know things to be in a teaching mode also
  • that is propagating what we know
  • sharing it with others and
  • making this knowledge open source for the world and
  • especially to help train a young generation of new leaders who are going to be the ones that grapple with these problems
• author
  • Jeffrey Sachs

The essence for this video is correct; active learning, progressive summarization, deep processing, relational analytical thinking, even evaluative.

Yet, the implementation is severely lacking; marginalia, text writing, etc.

Better would be the use of mindmaps or GRINDEmaps. I personally would combine it with the Antinet of course.

I do like this guy's teaching style though 😂

reply to u/SnooPandas3432 at https://www.reddit.com/r/Zettelkasten/comments/158tzk7/zettelkasten_on_a_ring_binder_with_looseleaf_paper/

She didn't specify a particular dimension, but I recall that Beatrice Webb used larger sheets of paper than traditional index-card sized slips in her practice and likely filed them into something akin to hanging files in a filing cabinet.

For students, I might suggest using the larger sheets/3-ring binder to make Cornell notes for coursework and then later distilling down one or two of the best ideas from a lecture or related readings into index card form for filing away over time. You could then have a repository of bigger formatted literature notes from books/lectures with more space and still have all the benefits of a more traditional card-based zettelkasten for creativity and writing. You could then have the benefit of questions for spaced repetition for quizzes/tests, while still keeping bigger ideas for writing papers or future research needs.

Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, and Mark Crowley Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting In International Conference on Machine Learning (ICML). Honolulu, Hawaii, USA. Jul, 2023.

p. xxiv

Hutchins seems to indicate that the "vicious specialization of scholarship" is in part to blame for the emergence of the "two cultures" delineated by C. P. Snow later in the decade.

• Alphago
  • first version took months of Google UK software developers to program. It won the world Go championship.
  • Alphago Master played itself without ever watching a human player. It beat the first Alphago version after 3 days of playing itself.
  • In 21 days, it beat Alphago version one a thousand to zero.

(Espeholt, ICML, 2018) "IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures"

This paper introduced the DPG Algorithm

Link to page with information about the paper: https://openreview.net/forum?id=rJeXCo0cYX

Yann LeCun released his vision for the future of Artificial Intelligence research in 2022, and it sounds a lot like Reinforcement Learning.

The paper that introduced the DQN algorithm for using Deep Learning with Reinforcement Learning to play Atari game.

Paper that evaluated the existing Double Q-Learning algorithm on the new DQN approach and validated that it is very effective in the Deep RL realm.

This paper introduces the DDPG algorithm which builds on the existing DPG algorithm from classic RL theory. The main idea is to define a deterministic policy, or nearly deterministic, for situations where the environment is very sensitive to suboptimal actions, and one action setting usually dominates in each state. This showed good performance, but could not beat algorithms such as PPO until the additions of SAC were added. SAC adds an entropy penalty which essentially penalizes uncertainty in any states. Using this, the deterministic policy gradient approach performs well.

This famous paper gives a great review of the DQN algorithm a couple years after it changed everything in Deep RL. It compares six different extensions to DQN for Deep Reinforcement Learning, many of which have now become standard additions to DQN and other Deep RL algorithms. It also combines all of them together to produce the "rainbow" algorithm, which outperformed many other models for a while.

Arxiv paper from 2021 on reinforcement learning in a scenario where your aim is to learn a workable POMDP policy, but you start with a fully observable MDP and adjust it over time towards a POMDP.

Paper that introduced the PPO algorithm. PPO is, in a way, a response to the TRPO algorithm, trying to use the core idea but implement a more efficient and simpler algorithm.

TRPO defines the problem as a straight optimization problem, no learning is actually involved.

Bowen Baker et. al. (Open AI) "Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos" Arkiv, June 2022.

Introduction of VPT : New semi-supervied pre-trained model for sequential decision making on Minecraft. Data are from human video playthroughs but are unlabelled.

Liang, Machado, Talvite, Bowling - AAMAS 2016 "State of the Art Control of Atari Games Using Shallow Reinforcement Learning"

Response paper to DQN showing that well designed Value Function Approximations can also do well at these complex tasks without the use of Deep Learning

A great paper showing how to think differently about the latest advances in Deep RL. All is not always what it seems!

Tom Schaul, John Quan, Ioannis Antonoglou and David Silver. "PRIORITIZED EXPERIENCE REPLAY", ICLR, 2016.

Xu, ICCV, 2019 "Temporal Recurrent Networks for Online Action Detection"

arxiv: https://arxiv.org/abs/1811.07391 hypothesis: https://hyp.is/go?url=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_ICCV_2019%2Fpapers%2FXu_Temporal_Recurrent_Networks_for_Online_Action_Detection_ICCV_2019_paper.pdf&group=world

(24:58) In order to perform significantly better, you need to FOCUS your attention on a single thing only. Multitasking won't work, and thinking about different things at once also doesn't work. Set up your environment to foster this insane level of focus.

(21:47) There is a clear relation between the body and the brain and they influence each other, at least in terms of perception with regards to motivation.

(13:36) Perhaps this is also why visual information is so useful for learning and cognition (see GRINDE)... Maybe the visual medium should be used more in instruction instead of primarily auditory lectures (do take into account redundancy and other medium effects from CLT though)

GRINDE mapping: 1. Grouped: grouping knowledge together 2. Reflective: reflective of your (non-linear) thinking 3. Interconnected: making more & distant connections (stronger than the groups) 4. Non-verbal (visuals) 5. Directional: which relations are the strongest, in which order can you sequence them? 6. Emphasise (visually) the most important things (see directional as well)

This same sort of structure could be useful for introducing students to fellow college students and also professionals who eschew a hyper-connected, frenetic, algorithmic, hustle mindset.

A way to normalize digital minimalism and slow productivity

To be processed

The author, Rediscovering Analog, reads a book at least twice, usually. He first reads it mainly for pleasure, just to enjoy it and to see what's in it. During the second time, if applicable, he goes through the book using intellectual (or learning) systems and methodologies to extract value from the book.

The first pass, which the author terms Scouting, is thus namely for enjoyment, but keeping in mind what might be valuable or interesting that will be valuable in the future, basically an unguided open ear. He has a list of scouted books in each section of the Zettelkasten that might be relevant to the section. What he does is have a stack of physical cards there with just the name of the book and the author, without anything else. Then when author proceeds to extract value from the book, he takes the card out and puts it in the respective book. Afterwards throwing this particular card into the trash. It's a form of the Anti-Library.

( Personally, I would include an appropriate reading cost and a level on Adler's hierarchy of books. In addition, I would make sure that my process of orientation, in the Inquiry-Based Learning framework, has been completed before I put it as a book within the Anti-Library. )

This may not be the most efficient for the purpose of acquiring value, but efficiency is not all there is. Enjoyment is a big part of intellectual work as well, as Antonin Sertillanges argues in his book The Intellectual Life: Its spirit, methods, conditions, as well as Mihaly Csikszentmihaliy in his book Flow.

What do they mean by "model" here? If they have retrained on more data, with a slightly different architecture, then the model weights after training must be different.

(14:20-19:00) Dopamine Prediction Error is explained by Andrew Huberman in the following way: When we anticipate something exciting dopamine levels rise and rise, but when we fail it drops below baseline, decreasing motivation and drive immensely, sometimes even causing us to get sad. However, when we succeed, dopamine rises even higher, increasing our drive and motivation significantly... This is the idea that successes build upon each other, and why celebrating the "marginal gains" is a very powerful tool to build momentum and actually make progress. Surprise increases this effect even more: big dopamine hit, when you don't anticipate it.

Social Media algorithms make heavy use of this principle, therefore enslaving its user, in particular infinite scrolling platforms such as TikTok... Your dopamine levels rise as you're looking for that one thing you like, but it drops because you don't always have that one golden nugget. Then it rises once in a while when you find it. This contrast creates an illusion of enjoyment and traps the user in an infinite search of great content, especially when it's shortform. It makes you waste time so effectively. This is related to getting the success mindset of preferring delayed gratification over instant gratification.

It would be useful to reflect and introspect on your dopaminic baseline, and see what actually increases and decreases your dopamine, in addition to whether or not these things help to achieve your ambitions. As a high dopaminic baseline (which means your dopamine circuit is getting used to high hits from things as playing games, watching shortform content, watching porn) decreases your ability to focus for long amounts of time (attention span), and by extent your ability to learn and eventually reach success. Studying and learning can actually be fun, if your dopamine levels are managed properly, meaning you don't often engage in very high-dopamine emitting activities. You want your brain to be used to the low amounts of dopamine that studying gives. A framework to help with this reflection would be Kolb's.

A short-term dopamine reset is to not use the tool or device for about half an hour to an hour (or do NSDR). However, this is not a long-term solution.

Huberman states that doing these 4 things consistently and regularly, as a habit, might seem to take time, therefore decreasing performance. BUT, in reality they increase performance, as these things improve your health, focus, and awareness significantly.

Therefore they are so-called Performance Enablers

The 4 (behavioral) keypoints for great physical and mental as well as cognitive health:

One) (2:00-4:05) View sunlight early in the day. The light needs to reach the eyes--increasing alertness, mood, and focus, through certain receptors. Also increases sleep quality at night, according to Huberman. Ideally five to ten minutes on a clear day, and ten to twenty minutes on an overcast day. No sunglasses, and certainly not through windows and windshields. If no sun is out yet, use artificial bright light. Do this daily.

Two) (4:05-6:10) Do physical exercise each and every day. Doesn't have to be super intense. Huberman recommends zone two cardiovascular exercise. Walking very fast, running, cycling, rowing, swimming are examples. He says to get at least between 150 and 200 minutes of this exercise per week. Some resistance training as well for longevity and wellbeing, increases metabolism as well. Do this at least every other day, according to Huberman. Huberman alternates each day between cardiovascular exercise and resistance training.

Three) (6:20-9:10) People should have access to a rapid de-stress protocol or tools. This should be able to do quickly and instantly, without friction. You can just do one breath for destress. ( Deep long breath through nose, one quick breath in nose to completely fill the longs, and then breathe out through mouth long.)

Four) (9:12-14:00) To have a deliberate rewiring nervous system protocol to use. A thing that can be done is NSDR (Non-Sleep Deep Rest protocol), this is specifically to increase energy.

Ideally the NSDR should be done after each learning session as well to imitate deep sleep (REM) and therefore accelerate neuroplasticity and thus rewire the nervous system; increasing the strength of connections between neurons and therefore increase retention significantly.

NSDR is also a process of autonomity and control, it allows one to find that they are in control of their body and brain. It makes one realize that external factors don't necessarily have influence. According to Huberman, NSDR even replenishes dopamine when it is depleted, making it also suitable for increasing motivation.

CIFAR-10 performance results are overestimates since some of the training data is essentially in the test set.

Test Twitter Two

Learning happens when we add memory to thinking. So, thinking precedes learning, and is fundamental to learning.

note to self: is thinking required for memory?

Test Twitter Annotation

good article on learning

https://www.youtube.com/watch?v=TQXMl4GycD0

• (intro & title) Studying is not the same as learning
• Higher order learning is interweaving information (interconnecting, building knowledge in networks and graphs) [a zettelkasten and a commonplace book stimulate higher order learning]

How to learn the art of memory (quick)

Basically, you must be unique. You can't compete when you learn exactly the same as everyone else. The education system sets up to fail and makes you a modern slave.

Przeglądanie rozdziałów książki przed ich przeczytaniem pozwala ułożyć twe myśli. "You’re creating little neural hooks to hang your thinking on, making it easier to grasp the concepts"

Liang, Machado, Talvite, Bowling - AAMAS 2016 "State of the Art Control of Atari Games Using Shallow Reinforcement Learning"

A great paper showing how to think differently about the latest advances in Deep RL. All is not always what it seems!

LeBlanc, D. G., & Lee, G. (2021). General Deep Reinforcement Learning in NES Games. Canadian AI 2021. Canadian Artificial Intelligence Association (CAIAC). https://doi.org/10.21428/594757db.8472938b

The misattribution of this quote often seen in educational settings likely stems from Richards & Rodgers from 1986.

See also: - https://hypothes.is/a/cKMkaAZQEe6dq0fkeyNabA - https://hypothes.is/a/YWrJKgZPEe6dy2sJU5KcSw

The frequent educational quote "Tell me and I forget, teach me and I remember, involve me and I learn.", often misattributed to Benjamin Franklin, is most attributable to 3rd century Confucian philosopher Kunzi (Xun Kuang or 荀子) who wrote:

Not having heard of it is not as good as having heard of it. Having heard of it is not as good as having seen it. Having seen it is not as good as knowing it. Knowing it is not as good as putting it into practice. Learning arrives at putting it into practice and then stops . . .

The translation of which appears in Xunzi: The Complete Text, Translated by Eric L. Hutton, Chapter 8: The Achievements of the Ru, Quote Page 64, Princeton University Press, Princeton, New Jersey. 2014.

Variations of the sentiment and attributions have appeared frequently thereafter.

Designing new learning technology is not always the best solution without paying attention to its learning environment.

Blog post comparing ASG (Auto Segmentation Criterion - yes, the last letter doesn't match) to CTC (Connectionist Temporal Classification) for aligning speech recognition model outputs with a transcript.

this reminds me of the "whiteboard effect" and the concept of collaborative learning as described by cal newport in his book, deep work.

such dynamics cultivate a culture of fortuitous learning and the exchange of ideas. when another individual is present, it instills a sense of accountability and motivation to dive profoundly into a problem and the gaps of each other's knowledge than we might when woking in solitude.

• Quote worthy
  • "“Protracted immaturity and dependence on paternal care
    • is not an unfortunate byproduct of our evolution
    • but instead a highly adaptive trait of our species,
    • which has enabled human infants to
      • efficiently organize attention to social agents and
      • learn efficiently from social output,”
  • “The evolutionary goal of altricial species is
    • not to become highly competent as quickly as possible
    • but rather to excel at learning over time.”
• Authors
  • Michael Goldstein,
  • Katerina Faust,
  • Samantha Carouso-Peck
  • Mary R. Elson

• the beauty of perceptual immaturity in altricial species is that
• it makes learning easier by reducing the complexity of the world,” the researchers wrote.
• Parents are key to altricial learning, Goldstein said,
  • forming a two-way system of feedback.
• Far from being passive recipients, he said,
  • infants of many species can change the behavior of their parents
  • in ways that actively shape their own developments.

• Title
  • The Origins of Social Knowledge in Altricial Species,
• Journal
  • The Annual Review of Developmental Psychology, - -
• Publication Date
  • Dec, 2021
• Authors
• Michael Goldstein,
• Katerina Faust,
  • Samantha Carouso-Peck and
  • Mary R. Elson

[29] AI - Deep Learning

https://pressbooks.pub/illuminated/

A booklet prepared for teachers that introduces key concepts from the Science of Learning (i.e. cognitive neuroscience). The digital booklet is the result of a European project. Its content have been compiled from continuing professional development workshops for teachers and features evidence-based teaching practices that align with our knowledge of the Science of Learning.

More than just taking notes - Learning exhaust by Nicole van der Hoeven

https://www.youtube.com/watch?v=3L24rKggMX8

Nice framing to broadly define note taking as a form of learning exhaust, but broadly nothing new here for me.