3,037 Matching Annotations
  1. Jul 2023
    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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!

    1. You can tell people just like I have you to focus their attention, choose a target. Imagine there's a spotlight shining just on it. Don't pay much attention to what's in your periphery almost as if you have like blinders on, right? So don't pay attention to those distractors. People can do that. We have them talk to us about like, well, what is it that you're focused on? What's catching your attention right now? Those are easy instructions to understand and it's easy to make your eyes do it. What's important though is that that's not what their eyes do naturally. When they're walking or when they're running, people do take a sort of wider perspective. They broaden their scope of attention relative to what these instructions are having them do. And when we taught people that narrowed style of attention, what we found is that they moved 23% faster in this course that we had set up. From the start line to the finish line, it was always exactly the same distance. And we were using our stop watches to see how fast did they move. They moved 23% faster and they said it hurt 17% less. Right? So exactly the same actual experience, but subjectively it was easier and they performed better. They increase the efficiency of this particular exercise.

      (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.

    2. Those distances literally look farther to people that for whom it might be harder to make it to that finish line, to navigate that space. We also found that that's the case with motivation, that when people are more motivated to exercise or to make it to that finish line, that motivation can in a sense compensate for that effect of their body on their perception of distance. So that even highly motivated people, people who are highly motivated, even if they have a higher waist to hip ratio might see the distance in a way that suggests it's just as short as people who have a lower waist to hip ratio. So motivation can change our visual experience and align people to experience a world that looks more like a person who'd have an easier time navigating it. So those were two initial findings, sets of findings, that suggested our visual experiences are not just reflective of the world that's out there. But instead it has to do with what is our body capable of doing and what is our brain capable of supplementing, our own motivational states and physical states of our body are working together to shift what it is that we're seeing in the world out there.

      (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.

    3. We prioritize what we see versus what we hear, why is that? Now, what comes to mind when I say that is when, somebody is saying no, but shaking their head yes. And so we have this disconnect, but we tend to prioritize what the action and not what we're hearing. So something that we visually see instead of what we hear.Speaker 1There isn't a definitive answer on that, but one source of insight on why do we do that, it could be related to the neurological real estate that's taken up by our visual experience. There's far more of our cortex, the outer layer of our brain that responds to visual information than any other form of information

      (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)

    1. 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)

    1. In their article, Scientist Spotlight Homework Assignments Shift Students’ Stereotypes of Scientists and Enhance Science Identity in a Diverse Introductory Science Class,” Jeffrey Schinske, Heather Perkins, Amanda Snyder, and Mary Wyer created a “scientist spotlight” weekly homework assignment to introduce counter stereotypical examples of scientists and provide a diverse representation of contributions to science. Each week, students reviewed a resource regarding these scientists’ research and personal history in lieu of other textbook readings. Through their analysis, the scholars were able to study and detect shifts in both scientist stereotypes and the students’ ability to see their possible selves in science.

      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

  2. Jun 2023
    1. I think we have a responsibility not only to ourselves, but also to each other, to our community, not to use Ruby only in the ways that are either implicitly or explicitly promoted to us, but to explore the fringes, and wrestle with new and experimental features and techniques, so that as many different perspectives as possible inform on the question of “is this good or not”.
    1. 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.

    1. We use the same model and architecture as GPT-2

      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.

    1. (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.

    2. 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

    3. 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.

    1. Recent work in computer vision has shown that common im-age datasets contain a non-trivial amount of near-duplicateimages. For instance CIFAR-10 has 3.3% overlap betweentrain and test images (Barz & Denzler, 2019). This results inan over-reporting of the generalization performance of ma-chine learning systems.

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

    1. Deep focus is possible. Take care of the base (the body): • Nutrition • Sleep • Exercise Then train your focus by observing the mind. It gets easily distracted. You can be aware of this. And suddenly you are in flow, without the 'You' being there.

      Test Twitter Two

    1. That’s easy. You can’t learn without thinking. Thinking is cognition. It’s the ability to recognize, and reason something out. It is observation with some understanding. Learning occurs when memory is added to thinking. The toddler touches hot stove. It thinks, “ouch, there’s pain.” That is observation, and is thinking. But you can’t say it learned, until the toddler remembers that the sensation of heat gradient when approaching a stove will end in a burn, when the stove is touched

      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?

    1. Focus is a muscle. Start with 4 sets of 20 minutes. Rest between sets. Progressive overload still applies to mental lifting. When you get stronger, add more weight. Increase to 4 sets of 45 minutes. Train your focus to hit your ideal financial physique in record time.

      Test Twitter Annotation

    1. 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]
    1. 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"

    Tags

    Annotators

    1. 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!

    1. 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

    1. By the 1980s the adage had implausibly been reassigned to Benjamin Franklin. The 1986 book “Approaches and Methods in Language Teaching” by Jack C. Richards and Theodore S. Rodgers contained the following passage:[12]1986 (Seventh Printing 1991), Approaches and Methods in Language Teaching: A Description and Analysis by Jack C. Richards and Theodore S. Rodgers, Chapter 7: The Silent Way, Quote Page 100, Cambridge … Continue reading These premises are succinctly represented in the words of Benjamin Franklin: Tell me and I forget, teach me and I remember, involve me and I learn.

      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

    2. Several English renderings have been published over the years. The following excerpt is from “Xunzi: The Complete Text” within chapter 8 titled “The Achievements of the Ru”. The translator was Eric L. Hutton, and the publisher was Princeton University Press in 2014. Emphasis added to excerpts:[1]2014 Copyright, 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. (Verified with … Continue reading 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 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.

    1. the design and integration of new technologies in learning activities cannot be studied independently of the classroom environment, less attention has been paid in learning environments

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

    1. 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.

    1. Learning does not happen in a vacuum. It is influenced by social dynamics, most notably between students and their peers.

      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.

    2. college students engage with and consume more content than at any time in history. It just so happens that this content is delivered by a streaming service, video game or social media platform, not by a college instructor.
    3. Student engagement is one of the strongest leading indicators we have of positive learning outcomes. Consequently, when students are disengaged, they are less likely to achieve their learning goals.
  3. May 2023
    1. “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
      • 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
    2. the beauty of perceptual immaturity in altricial species is that it makes learning easier by reducing the complexity of the world
      • 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
    1. Deep Learning (DL) A Technique for Implementing Machine LearningSubfield of ML that uses specialized techniques involving multi-layer (2+) artificial neural networksLayering allows cascaded learning and abstraction levels (e.g. line -> shape -> object -> scene)Computationally intensive enabled by clouds, GPUs, and specialized HW such as FPGAs, TPUs, etc.

      [29] AI - Deep Learning

    1. 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.

    1. El e-learning proviene de los términos electronic-learning (aprendizaje electrónico o formación en línea), el cual consiste precisamente en llevar a cabo un proceso de aprendizaje utilizando algún tipo de dispositivo electrónico (ordenador, tablet, smartphone), lo cual y gracias al uso de internet, permite una mayor accesibilidad a la información dando lugar a que en un proceso educativo, éste pueda llevarlo el estudiante sin importar el lugar en que se encuentre, siempre y cuando se disponga de los recursos de comunicación, equipos y herramientas tecnológicas necesarias, situación que vino a darle el impulso a la creación de los estudios de carácter virtual a cualquier nivel, aunque en un principio se inició en el nivel superior, sin embargo, `por diferentes situaciones a venido abarcando otros niveles educativos.

    1. El sistema de aprendizaje blended learning o b-larning, debemos entenderlo como una combinación entre el aprendizaje presencial y el aprenizaje en línea. Lo cual aparece como una opción al surgir las diferentes herramientas tecnológicas y se considera el uso de ellas pero sin dejar de aprovechar los aspectos valiosos que contiene el aprendizaje presencial. Es por ello que a esta "mezcla" de modalidades se ha dado por nombrar también como: Sistema híbrido, mixto, intercambiable, etc. Por ello es de entenderse y aceptarse que es válido el que se quiera con ello aprovechar las cualidades y ventajas que nos concede el aprendizaje virtual y todo aquello que también sigue siendo válido y útil de las sesiones presenciales.

    1. Chatti notes that Connectivism misses some concepts, which are crucial for learning, such as reflection, learning from failures, error detection and correction, and inquiry. He introduces the Learning as a Network (LaaN) theory which builds upon connectivism, complexity theory, and double-loop learning. LaaN starts from the learner and views learning as the continuous creation of a personal knowledge network (PKN).[18]

      Learning as a Network LaaN and Personal Knowledge Network PKN , do these labels give me anything new?

      Mohamed Amine Chatti: The LaaN Theory. In: Personalization in Technology Enhanced Learning: A Social Software Perspective. Aachen, Germany: Shaker Verlag, 2010, pp. 19-42. http://mohamedaminechatti.blogspot.de/2013/01/the-laan-theory.html I've followed Chatti's blog in the past I think. Prof. Dr. Mohamed Amine Chatti is professor of computer science and head of the Social Computing Group in the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. (did his PhD at RWTH in 2010, which is presumably how I came across him, through Ralf Klamma)

    1. The few notes I did refer back to frequently where checklists, self-written instructions to complete regular tasks, lists (reading lists, watchlists, etc.) or recipes. Funnily enough the ROI on these notes was a lot higher than all the permanent/evergreen/zettel notes I had written.

      Notes can be used for different purposes.

      • productivity
      • Knowledge
        • basic sense-making
        • knowledge construction and dispersion

      The broad distinction is between productivity goals and knowledge. (Is there a broad range I'm missing here within the traditions?) You can take notes about projects that need to be taken care of, lists of things to do, reminders of what needs to be done. These all fall within productivity and doing and checking them off a list will help one get to a different place or location and this can be an excellent thing, particularly when the project was consciously decided upon and is a worthy goal.

      Notes for knowledge sake can be far more elusive for people. The value here generally comes with far more planning and foresight towards a particular goal. Are you writing a newsletter, article, book, or making a video or performance of some sort (play, movie, music, etc.)? Collecting small pieces of these things on a pathway is then important as you build your ideas and a structure toward some finished product.

      Often times, before getting to this construction phase, one needs to take notes to be able to scaffold their understanding of a particular topic. Once basically understood some of these notes may be useless and not need to be reviewed, or if they are reviewed, it is for the purpose of ensconcing ideas into long term memory. Once this is finished, then the notes may be broadly useless. (This is why it's simple to "hide them with one's references/literature notes.) Other notes are more seminal towards scaffolding ideas towards larger projects for summarization and dissemination to other audiences. If you're researching a topic, a fair number of your notes will be used to help you understand the basics while others will help you to compare/contrast and analyze. Notes you make built on these will help you shape new structures and new, original thoughts. (note taking for paradigm shifts). These then can be used (re-used) when you write your article, book, or other creative project.

    1. Devising a prompt (AKA a question) is the key to ChatGPT. I am still uncertain what a good question is in AI's "mind". It might be something "way strange" and "un-questionly".

    1. Stop to think about "normal app" as like desktop app. Android isn't a desktop platform, there is no such this. A "normal" mobile app let the system control the lifecycle, not the dev. The system expect that, the users expect that. All you need to do is change your mindset and learn how to build on it. Don't try to clone a desktop app on mobile. Everything is completely different including UI/UX.

      depends on how you look at it: "normal"

    1. A new import is the _LRScheduler which we will use to implement our learning rate finder.

    2. We will also show how to initialize the weights of our neural network and how to find a suitable learning rate using a modified version of the learning rate finder.

  4. Apr 2023
    1. at the targeted level

      This is quite important. Success is achieving the goal. If the goal is unattainable, then so is success.This is how we set students up for failure.

    2. The goal of this progression is to build up gradually, with each step building on the previous one,

      This may benefit from adoption of SOLO and simplification of the rubric model.

    3. progression starts with Beginning, where the student tries to write a conclusion

      Resembles SOLO, though the rubric is wordy and includes a lot of subjective descriptors. Each level is distinct and adds a level of complexity to the previous level. "Expert" clearly aligns with SOLO extended/abstract level. Levels are not clearly linked to levels of cognitive function (uni / multi structural / relational, etc.)

    1. 44:19 - [Claudia] The classification is anything but indifferent.44:24 The manner of shelving the books44:26 is meant to impart certain suggestions to the reader,44:30 who, looking on the shelves for one book,44:33 is attracted by the kindred ones next to it,44:36 glances at the sections above and below,44:39 and finds himself involved in a new trend of thought44:43 which may lend to additional interests44:46 to the one he was pursuing.

      The classification is anything but indifferent. The manner of shelving the books is meant to impart certain suggestions to the reader, who, looking on the shelves for one book, is attracted by the kindred ones next to it, glances at the sections above and below, and finds himself involved in a new trend of thought which may lend to additional interests to the one he was pursuing.<br /> —Claudia Wedepohl on the design of Warburg's library, [00:44:19] in Aby Warburg: Metamorphosis and Memory

      Provides a similar sort of description of the push towards serendipity and discovery found in one's zettelkasten as well as that in Melvil Dewey's library classification and arrangements.

    1. One way to weed those out is to begin with the most basic question we can formulate. Conceptual artist Jonathon Keats calls these “naive questions.” Geochemist Hope Jahren calls them “curiosity questions.” Whatever the label, they are, in essence, the kind of question a child could come up with.Progressing from such questions requires us to dig deeper and slow down our thinking — which, in turn, may reveal to us unknown unknowns or information we may have missed last time we explored the topic.

      For the intellectual worker, an Antinet can be used to keep track of such questions and the thought-lines corresponding to these questions.

    2. We can be bolder about asking questions in public and encouraging others to pursue their curiosity, too. In that encouragement, we help create an environment where those around us feel safe from the shame and humiliation they may feel in revealing a lack of knowledge about a subject, which can round back to us.

      As an educator, be courageous, lead by example. Start by asking questions out loud, not only those you wish students to answer, but also those you genuinely don't know, and wish to research together with your students.

    3. Many people, myself included, can find asking questions to be daunting. It fills us with worry and self-doubt, as though the act of being inquisitive is an all-too-public admission of our ignorance. Unfortunately, this can also lead us to find solace in answers — no matter how shaky our understanding of the facts may be — rather than risk looking stupid in front of others or even to ourselves.

      Asking questions is how we learn. Do not avoid it for the sake of not looking stupid. That is stupid. Inquiry-Based Learning.

      As Confucius said: "The one who asks a question is a fool for a minute, the one who doesn't ask is a fool for life."

    1. 8 Overtuigingen funest voor leren leren #1 Leren leren verloopt impliciet De eerste overtuiging die leraren kunnen hebben, is dat leren leren impliciet wordt aangeleerd en dus de informatieoverdracht niet expliciet hoeft te zijn. Leren leren wordt vanzelf aangeleerd als gevolg van ervaringen, dus expliciete instructie is niet nodig, is dan de gedachte. Deze gedachte gaat geheel voorbij aan wat we inmiddels uit onderwijsonderzoek weten over het cruciale belang van expliciete strategie-instructie. #2 Anders van aard De tweede overtuiging is dat kennis van leerstrategieën anders van aard is dan kennis van de leerstof. Voor leraren met deze overtuiging is kennis van leren leren eenduidig en niet problematisch, waardoor ze het onnodig vinden om er relatief veel aandacht aan te besteden. Terwijl we weten dat leren leren best een complexe aangelegenheid is. Zo identificeerden Pressley en Afflerbach maar liefst meer dan 150 verschillende leerstrategieën die mensen gebruiken tijdens het lezen. Als we verwachten dat leerlingen gedegen kennis hebben van vakken als geschiedenis of natuurkunde, waarom dan niet kennis van hoe ze moeten leren? #3 Komt niet vaak van pas Kennis over leren leren wordt toch niet zo vaak gebruikt dus hoef je er ook niet veel aandacht aan te besteden tijdens de les, is de derde overtuiging volgens Lawson cum suis. De gedachte is dat je als leraar je daarom maar beter kan richten op het onderwijzen van de leerstof. Maar als je leerlingen eens vraagt om hardop na te denken terwijl ze bijvoorbeeld de hoek van een driehoek proberen te berekenen, dan zal je horen dat ze voortdurend verschillende soorten strategieën inzetten; met de antwoorden krijg je bovendien meer inzicht in welke strategieën je leerlingen gebruiken. Zo leer je hoe je leerlingen leren. #4 Gewoon ervaren in de praktijk De vierde overtuiging is dat de kennis die leerlingen nodig hebben om leren leren aan te leren, vooral praktisch moet zijn en niet theoretisch. De gedachte is dat leerlingen leren leren aanleren door gewoon aan de slag te gaan, door te ervaren in de praktijk. Deze gedachte komt voort uit wat leraren in het verleden hebben ervaren, toen ze zelf op school zaten. #5 Onzeker over de materie De vijfde overtuiging is een belangrijke, namelijk dat leraren niet zeker weten of ze kunnen lesgeven over leren leren. Twee zaken zijn hier van belang: het oordeel van leraren over hun eigen kennis van leren leren en het vertrouwen dat ze nodig hebben om de instructie te kunnen geven. Dat laatste gaat over de self-efficacy van leraren. Self-efficacy van leraren gaat over de mate waarin zij zichzelf in staat achten om complexe taken uit te voeren, zoals het geven van strategie-instructie. Self-efficacy is een sterke motivator voor het instructiegedrag van leraren. #6 Is aan de leerlingen zelf Leren leren moet aan leerlingen worden overgelaten, is de zesde overtuiging. Leraren gaan ervan uit dat de verantwoordelijkheid en het initiatief voor leren leren bij de leerling liggen, terwijl kennis van de verschillende strategieën de leerling juist in staat stelt om verantwoordelijkheid en initiatief te nemen. Het middel wordt hier verward met het doel van leren leren. #7 Lage verwachtingen De zevende overtuiging is dat leren leren slechts is weggelegd voor enkele leerlingen, meestal de goed presterende leerlingen. De leraar kan de leerlingen die moeilijk meekomen in de klas, maar beter niet lastigvallen met instructie in leren leren, is de overtuiging. Er zijn bizar veel studies beschikbaar die laten zien dat expliciete instructie juist ook voor deze groep leerlingen de voorkeur geniet. Daarnaast laat onderzoek van Jeltsen Peeters cum suis onder 127 basisschoolleerkrachten in Vlaanderen zien dat de leraar met deze overtuiging laag presterende leerlingen zelfs opzadelt met een dubbel nadeel. Niet alleen hebben deze leerlingen al te maken met de nodige uitdagingen door de beperkte mate waarin ze in staat zijn om te leren, bovendien krijgen ze vanuit deze overtuiging minder mogelijkheden om leerstrategieën aan te leren. Extra inzet is dus geboden op het terrein van strategie-instructie. #8 Leren leren is niet aan te leren De achtste overtuiging is dat leren leren waarschijnlijk niet is aan te leren. Sommige leraren en onderzoekers denken dat leren leren niet te onderwijzen valt en dus geen onderdeel kan en hoeft te zijn van een expliciete instructie. John Sweller en Fred Paas verwoorden dit heel helder: “Self-regulated learning is likely to be a biologically primary skill and so unteachable.” Deze onderzoekers baseren zich op het werk van de Amerikaanse hoogleraar David Geary. In het artikel ‘An evolutionarily informed education science’ bekijkt Geary het leren van kinderen door een evolutionaire bril. Sommige dingen leren zij vanzelf. Ze leren bijvoorbeeld lopen met vallen en opstaan, ze leren luisteren en spreken in een moedertaal en ze leren hoe ze met anderen omgaan. Volgens Geary zijn dit vormen van leren die evolutionair zijn ingebakken, omdat deze noodzakelijk zijn gebleken om te overleven. Deze automatische processen noemt Geary ‘primair leren’. Daarnaast bestaat er ‘secundair leren’. Daarbij gaat het om het verwerven van kennis (en vaardigheden) die evolutionair gezien veel jonger is en die van generatie op generatie wordt overgedragen. Denk aan leren lezen en schrijven. Secundaire kennis is van belang om goed te functioneren in de huidige maatschappij. Jammer genoeg gaat secundair leren niet vanzelf, maar kost dat moeite, en volgens Geary is daar expliciete instructie door de leraar bij nodig. Of zoals Kirschner, Claessens en Raaijmakers (2018, p. 21) het stellen: “Het verwerven van kennis op school gaat dus niet vanzelf.” En dat geldt ook voor het aanleren van leren leren; dat hebben de vele meta-analyses en interventiestudies ons inmiddels wel geleerd.

      Myths about self-regulated learning to be aware of.

    1. one must also submit to the discipline provided by imitationand practice.

      Too many zettelkasten aspirants only want the presupposed "rules" for keeping one or are interested in imitating one or another examples. Few have interest in the actual day to day practice and these are often the most adept. Of course the downside of learning some of the pieces online leaves the learner with some (often broken) subset of rules and one or two examples (often only theoretical) and then wonder why their actual practice is left so wanting.

      link to https://hypothes.is/a/ZeZEgNm8Ee2woUds5QzgOw

    2. they weresensible enough to recognize that one does not acquire a skill simply bystudying rules; one must also submit to the discipline provided by imitationand practice. And they recognized too that in order to derive the maximumbenefit from precept, imitation, and practice, the student had to be firedwith a desire to learn as much as his natural endowments permitted.

      Going back at least as far as the rhetoric of Aristotle, Cicero, and Quintilian, we recognize that while sets of rules can be helpful to the student, these must also be paired with ample imitation and practice.

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

      New supervised pre-trained model for sequential decision making on Minecraft. Data are from human video playthroughs but are unlabelled.

      reinforcement-learning foundation-models pretrained-models proj-minerl minecraft

  5. Mar 2023
    1. What would a list of prompts for teaching creatively look like if it were created using Brian Eno's Oblique Strategies as a model?

      Inspired by Sophia Rahming (~09:48)

    2. As a teacher of English to secondary school students, and as an online doctoral student, I am excited to explore and possibly integrate Hypothesis into my work. I love research and everything involved with it. Thank you to the creators of this tool --

    1. ‘socially biased individual learning’

      Definition - socially based individual learning - an individual learns by interacting with the embedded environment - but the environment is itself biased - so that certain learning outcomes - are more easily learned - than they would otherwise be

    2. the problems inherent in assuming any simple individual/social learning distinction are already well understood by some researchers working on cultural evolution.

      moss sponging by chmpanzees - is a phenomena observed by researchers - in which the distinction between<br /> - individual and - collective learning - is fuzzy - Sponging is a technique of wild chimpanzees - in which they use chewed up plant material - as a sponge to soak up water - One individual wild chimpanzee - named by the researchers as KW - picked up a discarded sponge used by another wild chimpanzee - which happened to have moss in it - and so developed a sponge for water specifically from moss - KW did not learn it socially from another chimpanzee - yet if it weren't for - the behavior of other chimpanzees in the group - cultural artefacts they left behind - niche construction that resulted to changes in the environment - the individual learning of KW would never have produced moss sponging

    3. The problem with this way of defining things is that we ignore the fact that, even when acting in a manner that appears to involve no direct interaction with other creatures, organisms nonetheless develop and learn in environments that have been affected by the prior actions of their conspecifics (and not just their conspecifics). This is precisely the sort of phenomenon stressed by the proponents of the niche-construction approach to evolution, and it is also stressed by developmental systems theorists [40,41]. Organisms grow in environments that have been constructed by the actions of previous generations: in that way, what an organism learns can be profoundly affected and enhanced by the collective activities of individuals it may never meet. In other words, we should not assume that there is any good distinction between individual learning and what we might call ‘social transmission’. The latter can be achieved via the former.
      • This primate example demonstrates an ambiguity between individual and social learning.
      • The problem with this way of defining things exclusively as either
        • individual or
        • social
      • is that we ignore the fact that,
        • even when acting in a manner
        • that appears to involve no direct interaction with other creatures,
      • organisms nonetheless develop and learn in environments
        • that have been affected by the prior actions of their conspecifics (and not just their conspecifics).
      • This is precisely the sort of phenomenon
        • stressed by the proponents of the niche-construction approach to evolution,
        • and it is also stressed by developmental systems theorists [40,41].
      • Organisms grow in environments that have been constructed
        • by the actions of previous generations:
          • in that way, what an organism learns
          • can be profoundly affected and enhanced
          • by the collective activities of individuals it may never meet.
      • In other words, we should not assume
        • that there is any good distinction
        • between
          • individual learning and
          • what we might call ‘social transmission’.
      • The latter can be achieved via the former.
    1. Instead of wasting energy in the vain attempt to hold mentally slow and defective children up to a level of progress which is normal to the average child, it will be wiser to take account of the inequalities of children in original endowment and to differentiate the course of study in such a way that each child will be allowed to progress at the rate which is normal to him, whether that rate be rapid or slow.

      I think this would be the best approach to help children that are slower than "normal" children. I stated earlier that the best way to help children learn is to see how they learn such as being hands on, visual, or auditory learning. Everyone doesn't learn stuff at the exact same time, it may take some people longer to understand the concept than other people. Everyone learns differently. This is important to the history of psychology because it helps us understand that children who are slower than other children can develop intelligence at their own speed. I think another reason this is important to the history of psychology is because we now understand the grade of intelligence is different for everyone, but people can build intelligence at their own progressive rate and that we can't force someone who is slower at learning to be at normal speed.

    1. Learning has to be knowledge. 00:10:07 And learning has to be based on understanding. And what you understand you can absorb, internalize and it becomes knowledge. What you know, you don't forget. You can block something that you know, but not forget.
    1. Ritual practice embeds tacit knowledge. Its bodily actions enact meaning and operationalize values. The bodily motions of ritual actions, such as physically sharing drinks and food, and giving gifts, matters because of the reciprocal ideomotor effects of unconscious priming (Kahneman 2011:53). As Lakoff explains, there are connections between metaphoric meanings and bodily actions such that metaphoric associations are embedded in the structures of our brains. Compartmentalism, or “biconceptualism” in his terms, is physical in our brains, and frame shifts can be triggered through bodily movement with priming effects. “Going through the motions” of ritual will have some effects even for those who start off feeling silly for doing it.

      // - this is the mechanism by which ritual practice can bring about interpretive shift unconsciously -because bodily movements have a priming effect - Lakoff points to the concept of "biconceptualism, a compartmentalism in our physical brain

    2. interpretive drift is largely unconscious, not articulated, but brought on through practice (Luhrmann 1989:316). It involves more than a shift in the language people use (Luhrmann 1989:315, 321). It is not just cognitive, not just a new interpretive framework, but a shift in ontology and habitus, though Luhrmann uses the term “interpretive” drift. It is an acculturative process of change, but not an entirely passive internalization of culture. It is an interactive, though not necessarily conscious ongoing collaboration. We do this partly through imitation, but also growing skills in ourselves, as Michael Polanyi describes of tacit learning of personal knowledge.

      // in other words, - interpretive shift is unconscious and brought about through practice. It is a shift in ontology, habitus and many things happening at once and is also Polyani's tacit learning

    3. Haluza-Delay’s description of informal and incidental learning sounds much like Michael Polanyi’s (1974) “practical knowledge.” Haluza-Delay discusses it as tacit learning, a term Polanyi introduced. From Polanyi’s description, much of tacit learning is initially conscious, but subsides into subsidiary awareness. People learn values in this fashion, but core values are picked up through imitation without conscious awareness.

      //Summary of Haluza-Delay and Polylani's conception of Tacit Knowledge - Haluza-Delay’s description of informal and incidental learning sounds much like Michael Polanyi’s (1974) “practical knowledge.” - Haluza-Delay discusses it as tacit learning, a term Polanyi introduced. - From Polanyi’s description, much of tacit learning is: - initially conscious, - but subsides into subsidiary awareness - People learn values in this fashion, but core values are picked up through imitation without conscious awareness.

    1. we have turned to machine learning, an ingenious way of disclaiming responsibility for anything. Machine learning is like money laundering for bias. It's a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. The numbers don't lie.

      Machine learning like money laundering for bias

    1. The state of current technology greatly impacts our ability to manipulate information, which in turn exerts influence on our ability to develop new ideas and technologies. Tools designed to enable networked thinking are a step in the direction of Douglas Engelbart’s vision of augmenting the human intellect, resulting in “more-rapid comprehension, better comprehension, the possibility of gaining a useful degree of comprehension in a situation that previously was too complex, speedier solutions, better solutions, and the possibility of finding solutions to problems that before seemed insolvable.”

      There's a danger to using digital tools to help with Higher Order Thinking; namely, it offloads precious cognitive load, optimized intrinsic load, which is used to build schemas and structural knowledge which is essential for mastery. Another danger is that digital tools often make falling for the collector's fallacy easier, meaning that you horde and horde information, which makes you think you have knowledge, while in fact, you simply have (maybe related) information, not mastery. The analog way prevents this, as it forces you to carefully evaluate the value of an idea and decide whether or not it's worth it to spend time on writing it and integrating it into a line of thought. Evaluation/Analysis is forced in an analog networked thinking tool, which is a form of Higher Order Learning/Thinking, as they are in the higher orders of Bloom's Taxonomy/Hierarchy.

      This is also true for AI. Always carefully evaluate whether or not a tool is worth using, like a farmer. (Deep Work, Cal Newport).

      Instead, use a tool like mindmapping, the GRINDE way, which is digital, for learning... Or the Antinet Zettelkasten by Scott Scheper, which is analog, for research.

    2. Divergence and emergence allow networked thinkers to uncover non-obvious interconnections and explore second-order consequences of seemingly isolated phenomena. Because it relies on undirected exploration, networked thinking allows us to go beyond common sense solutions.

      The power of an Antinet Zettelkasten. Use this principle both in research and learning.

    3. For instance, we used to think that the main cause of obesity was a poor diet at an individual level, leading to treatments focused on the individual. However, taking a networked thinking approach in a 32-year-long study with over 12,000 people led researchers to discover that the participants’ personal network had a great impact on their likelihood to be obese. “Discernible clusters of obese persons were present in the network at all time points,” write the researchers.

      Another social factor influencing human behaviour. Beware of such factors when it comes to self-improvement and learning.

    4. Networked thinking is an explorative approach to problem-solving, whose aim is to consider the complex interactions between nodes and connections in a given problem space. Instead of considering a particular problem in isolation to discover a pre-existing solution, networked thinking encourages non-linear, second-order reflection in order to let a new idea emerge.

      Seems similar to Communicating with an Antinet Zettelkasten.

    1. asks for the Minecraft domain.

      They demonstrate the model on a "minecraft-like" domain (introduced earlier by someone else) where there are resources in the world and the agent has tasks.

    1. When you call 'foo' in Ruby, what you're actually doing is sending a message to its owner: "please call your method 'foo'". You just can't get a direct hold on functions in Ruby in the way you can in Python; they're slippery and elusive. You can only see them as though shadows on a cave wall; you can only reference them through strings/symbols that happen to be their name. Try and think of every method call 'object.foo(args)' you do in Ruby as the equivalent of this in Python: 'object.getattribute('foo')(args)'.
  6. Feb 2023
    1. No new physics and no new mathematics was discovered by the AI. The AI did however deduce something from the existing math and physics, that no one else had yet seen. Skynet is not coming for us yet.

    1. who believed that parents, caregivers, peers, and the culture at large are responsible for developing higher-order functions.

      We can watch adults model things, but we need people to teach us the nuance and context of those behaviors.

    1. Collecting does not transform us and always postpones learning and transformation to the future. Collecting creates debt that we promise to pay back in some future that never arrives.

      There's some truth and falsity here...

    1. Remember that life in a Zettelkasten is supposed to be fun. It is a joyful experience to work with it when it works back with you. Life in Zettelkasten is more like dance than a factory.

      I've always disliked the idea of "work" involved in "making" notes and "processing" them. Framing zettelkasten and knowledge creation in terms of capitalism is a painful mistake.

      the quote is from https://blay.se/2015-06-21-living-with-a-zettelkasten.html

    1. this is different than simply copying someone else's behavior.

      The inflection point of when something is learned comes in demonstration? Or in spontaneous performance of the behavior?

    1. it is time to actually perform the behavior you observed.

      Is this in conflict with the statement earlier of learning with no demonstration of new behaviors?

    2. Retention can be affected by a number of factors, but the ability to pull up information later and act on it

      Retrieval practice is a method which can be used to reinforce retention.

    1. Definition 3.2 (simple reward machine).

      The MDP does not change, it's dynamics are the same, with or without the RM, as they are with or without a standard reward model. Additionally, the rewards from the RM can be non-Markovian with respect to the MDP because they inherently have a kind of memory or where you've been, limited to the agents "movement" (almost "in it's mind") about where it is along the goals for this task.

    2. e thenshow that an RM can be interpreted as specifying a single reward function over a largerstate space, and consider types of reward functions that can be expressed using RMs

      So by specifying a reward machine you are augmenting the state space of the MDP with higher level goals/subgoals/concepts that provide structure about what is good and what isn't.

    3. However, an agent that hadaccess to the specification of the reward function might be able to use such information tolearn optimal policies faster.

      Fascinating idea, why not? Why are we hiding the reward from the agent really?

    4. Reward Machines: Exploiting Reward FunctionStructure in Reinforcement Learning

      [Icarte, JAIR, 2022] "Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning"

    1. Using Reward Machines for High-Level Task Specificationand Decomposition in Reinforcement Learning

      [Icarte, PMLR, 2018] "Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning"

    1. “Writing a thesis,”Eco wrote, “requires a student to organize ideas and data, towork methodically, and to build an ‘object’ that in principlewill serve others. In reality, the research experience mattersmore than the topic.”

      Where does the learning portion of education morph into research? Where is the dividing line?

    1. But the book’s enduring appeal—the reason it might interest someone whose life no longer demands the writing of anything longer than an e-mail—has little to do with the rigors of undergraduate honors requirements. Instead, it’s about what, in Eco’s rhapsodic and often funny book, the thesis represents: a magical process of self-realization, a kind of careful, curious engagement with the world that need not end in one’s early twenties.
    1. https://pair.withgoogle.com/

      People + AI Research (PAIR) is a multidisciplinary team at Google that explores the human side of AI by doing fundamental research, building tools, creating design frameworks, and working with diverse communities.

    1. around that same time i got a call from my daughter you know leave it to your kids and she said you know mom it's 00:03:48 just that all the problems we're dealing with in the world right now are insidious and um you know it came up last night siva was talking about the insidiousness 00:04:01 of the facebook problem and and this was an unlocker for me of what what does it mean for something to be insidious so i looked it up and i started to 00:04:14 explore and it turns out that insidious is defined and i think this is from the you know the oxford on the internet not the original but um that there's proceeding in a gradual 00:04:27 subtle way but with very harmful effects in other words there's something that's that's gathering combining in an unseen way that's leading to danger
      • comment
      • this is an example of how granular social learning, the evolution of consciousness and entangled and individual and collective learning takes place in a mundane way
        • another person relays an idea to us
        • it resonates with us by connecting to some point
        • in our salience landscape
        • in this case, caused Nora to look up the word "insidious" that appeared in the words of her daughter
        • and caused her to think of the meaning as something that starts out small and apparently harmless,
        • but gathering and combining in an unseen way to become dangerous
    1. This points to perhaps the most dangerous pitfall of note-taking. It’s very tempting to convince yourself you are learning just because you are writing down - in the sense of passively recording - what someone else says or writes.
    1. student outcomes, including learning, persistence, or attitudes.

      I would think that this would be one of the easiest things to measure and also would provide significant and useful data. We should check in with Brian (?) to see what data is currently being tracked.

    1. Zettelkasten can be described as a collection of conceptual maps in a written format.

      What are the connections between zettelkasten and conceptual maps?

      How are they different/similar to Tony Buzan's mind maps?

    1. https://cathieleblanc.com/2023/02/05/choosing-learning-materials/

      Cathie notices that students skip materials about the theoretical "why" of assignments to get to the simpler assignments.

      This seems to be an issue with some in the personal knowledge management space who want to jump into the technology, the terminology, and moving things about without always understanding what they're doing or why. Many end up giving up as a result. Few books provide reasoning behind the terminologies or building blocks they describe to provide the theoretical why. As a result some may figure it out from long, fraught practice, but it's likely that more are not seeing the results they expect and thus giving up.

      • = human being's = altricial nature - is an = evolutionary adaptation
      • resulting in exceptional = complex social learning
      • tradeoff of helplessness at birth
      • is complex social learning
      • that enables cumulative cultural evolution
    1. “The evolutionary goal of altricial species is not to become highly competent as quickly as possible but rather to excel at learning over time.”
      • = quotation
      • The evolutionary goal of altricial species
      • is not to become highly competent as quickly as possible - but rather to
      • excel at learning over time.
    2. extended altriciality creates opportunities for sophisticated social learning within the parent-offspring system.
      • = extended altriciality
      • creates opportunities for sophisticated = social learning
      • within the = parent-offspring system.
    1. Founder of StudyWand.com here, who received a 15k grant to develop an AI generating flashcard app in 2020 after an earlier prototype.We've found students more consistently study ready-made cards that are at desirable difficulty (they get about 80% correct) and which are segmented by topic (e.g. semantic grouping of flashcards to tackle "one lesson at a time" like Duolingo). Students would prefer to use pre-made flashcards by other students in their class, then AI flashcards, then create and use their own.There is limited evidence by Roediger and Karpicke who are the forefathers of retrieval practise that creating cards is also important. Frank Leeming (2002 study Exam-a-day) also showed that motivation when studying is peaked when you ask just a few questions a day, but every working day.Now one of the vital benefits of retrieval practise with AI over creating your own cards is foresight bias - not mentioned yet in this thread - the fact that particularly in some subjects like Physics, students don't know what they don't know (watch this amazing Veritasium video, it also explains why misconceptions are so handy for learning physics): https://www.youtube.com/watch?v=eVtCO84MDj8 - basically, if you use AI quizzes (or any prepared subject-specific right/wrong system), you learn quickly where your knowledge sits and what to focus on, and reduce your exam stress. If you just sit their making quizzes, firstly you make questions on things you already know, you overestimate how much you can learn, and you consolidate on your existing strengths, and avoid identifying your own knowledge gaps until later on, which is less effective.--To quote from my dissertation experiment on background reading for retrieval practise, the end is about foresight bias a little: Retrieval practice – typically, quizzing - is an exceedingly effective studying mechanism (Roediger & Karpicke, 2006; Roediger & Butler 2011; Bae, Therriault & Redifer, 2017, see Binks 2018 for a review), although underutilized relative to recorded merit, with students vastly preferring to read content (Karpicke & Butler, 2009; Toppino and Cohen, 2009). Notably mature students do engage in practice quizzes more than younger students (Tullis & Maddox, 2020). Undertaking a Quiz (Retrieval practice) can enhance test scores significantly, including web-based quizzes (Daniel & Broida, 2017). Roediger & Karpicke (2006) analysed whether students who solely read content would score differently to students who took a practice quiz, one week after a 5-minute learning session. Students retained information to a higher level in memory after a week with the quiz (56% retained), versus without (42%), despite having read the content less (average 3.4 times) than the control, read-only group (14.2 times). Participants subjectively report preference for regular Quizzing (Leeming, 2002) over final exams, when assessed with the quiz results, with 81% and 83% of participants in two intervention classes recommending Leemings “Exam-a-day” procedure for the next semester, which runs against intuition that students might biases against more exams/quizzes (due to Test Anxiety). Retrieval Practice may increase performance via increasing cognitive load which is generally correlated with score outcomes in (multimedia) learning (Muller et al, 2008). Without adequate alternative stimuli, volume of content could influence results, thus differentiated conditions to control for this possible confound are required when exploring retrieval practice effects (as seen in Renkl 2010 and implemented in Methods). Retrieval practice in middle and high school students can reduce Test Anxiety, when operationalised by “nervousness” (Agarwal et al 2014), though presently no research appears to have analysed the influence of retrieval practice on university students’ Test Anxiety. Quizzing can alleviate foresight bias – overestimation of required studying time – in terms of students appropriately assigning a greater, more realistic study time plan (Soderstrom & Bjork, 2014). Despite the underutilization noted by Karpicke and Butler (2009), quizzing is becoming more common in burgeoning eLearning courses, supported by the research (i.e. Johnson & Johnson, 2006; Leeming, 2002; Glass et al. 2008) demonstrating efficacy in real exam performance.

      .

    1. There’s a holy trinity in machine learning: models, data, and compute. Models are algorithms that take inputs and produce outputs. Data refers to the examples the algorithms are trained on. To learn something, there must be enough data with enough richness that the algorithms can produce useful output. Models must be flexible enough to capture the complexity in the data. And finally, there has to be enough computing power to run the algorithms.

      “Holy trinity” of machine learning: models, data, and compute

      Models in 1990s, starting with convolutional neural networks for computer vision.

      Data in 2009 in the form of labeled images from Stanford AI researchers.

      Compute in 2006 with Nvidia’s CUDA programming language for GPUs.

      AlexNet in 2012 combined all of these.

  7. Jan 2023
    1. 个人学习可能取决于他人行为的主张突出了将学习环境视为一个涉及多个互动参与者的系统的重要性
    1. Carers in this position need the support of a caring community to sustain them”

      and by sharing and modeling caring for each other, educators help learners build a community!

    1. json { "@context": { "@vocab": "https://schema.org/", "endDate": { "@type": "http://www.w3.org/2001/XMLSchema#dateTime" }, "startDate": { "@type": "http://www.w3.org/2001/XMLSchema#dateTime" } }, "@id": "https://example.org/id/course/2", "@type": "Course", "courseCode": "SD100", "name": "Structured Data", "description": "An introduction to authoring JSON-LD documents for OIH", "teaches": "JSON-LD", "provider": { "@type": "Organization", "name": "Example University", "@id": "https://grid.ac/institutes/grid.475727.4", "description": "UN Department of Economic and Social Affairs Sustainable Development" }, "hasCourseInstance": [{ "@type": "CourseInstance", "courseMode": ["MOOC1", "online"], "endDate": "2019-03-21", "startDate": "2019-02-15", "attendee": { "@type": "Person", "name": "Jane Doe", "jobTitle": "Professor", "telephone": "(425) 123-4567", "url": "http://www.janedoe.com", "identifier": { "@id": "ID_value_string", "@type": "PropertyValue", "propertyID": "This can be text or URL for an ID like ORCID", "url": "https://foo.org/linkToPropertyIDPage", "description": "Optional description of the ID" } } }, { "@type": "CourseInstance", "courseMode": ["MOOC2", "online"], "endDate": "2019-05-21", "startDate": "2019-04-15" }] }

    1. The best way to learn common table expressions is through practice. I recommend LearnSQL.com's interactive Recursive Queries course. It contains over 100 exercises that teach CTEs starting with the basics and progressing to advanced topics like recursive common table expressions.
    1. We can have a machine learning model which gives more than 90% accuracy for classification tasks but fails to recognize some classes properly due to imbalanced data or the model is actually detecting features that do not make sense to be used to predict a particular class.

      Les mesures de qualite d'un modele de machine learning

  8. Dec 2022
    1. Dans le zero-shot learning, le modèle doit être capable de généraliser ce qu'il a appris sur des exemples précédents pour effectuer une tâche sur laquelle il n'a jamais été entraîné. Cela signifie que le modèle doit être capable de transférer ses connaissances acquises sur une tâche donnée à une nouvelle tâche, sans avoir besoin d'exemples d'entraînement spécifiques pour cette nouvelle tâche.

      0-shot learning

    1. My goal was simply to scale this ladder over time. I worked the list 5 people at a time, starting at the bottom. I engaged relentlessly with those accounts until they noticed me and began engaging back.

      Interesting approach and these people are going to be great candidates for picking up new knowledge and self learning from too!

    2. Don’t try to convince everyone that what you say, feel, think, or have done is better than everyone else.

      This is pretty normal for those of us who are academically inclined so it shouldn't be too much of a stretch - after all a lot of the time what we're doing is thinking about other peoples' works critically

    1. Good teachers need to have the context of the student to know what level of explanation they need to give to satisfy the curiosity of the learner. (Also a potential reason that online programmatic learning is difficult as having the appropriate context to skip portions is incredibly hard to do with computers.)

      General rule of thumb: The levels of the depth of explanations provided are generally proportional to the levels of understanding achieved.

      Further understanding requires additional questions, research, and work.

    2. https://www.youtube.com/watch?v=Q8Xaw72ESdA

      According to researcher Danny Hatcher, the "Feynman Technique" was coined by Scott H. Young in the August 22, 2011 YouTube video Learn Faster with The Feynman Technique and the subsequent 2022-09-01 article Learn Faster with Feynman Technique, ostensibly in a summarization of Gleick, James (1992). Genius: The Life and Science of Richard Feynman. Pantheon Books. ISBN 0-679-40836-3. OCLC 243743850.

      The frequently quoted Einstein that accompanies many instances of the Feynman Technique is also wrong and not said by Einstein.

      The root Einstein quote, is apparently as follows:

      that all physical theories, their mathematical expressions apart ought to lend themselves to so simple a description 'that even a child could understand them.' —Ronald W. Clark, p418 of Einstein: His Life and Times (1972)

    1. The collocation results can be used to correct the sensor data to more closely match thedata from the reference instrument. This correction process helps account for known biasand unknown interferences from weather and other pollutants and is typically done bydeveloping an algorithm. An algorithm can be a simple equation or more sophisticatedprocess (e.g., set of rules, machine learning) that is applied to the sensor data. This sectionfurther discusses the process of correcting sensor data

      correction factors for collocated sensors using ML

    Tags

    Annotators

    1. Learning Partners

      Having a learning partner can help students learn in a number of ways. For one, it can provide a sense of accountability and motivation. When students are working with a partner, they are more likely to stay focused and engaged with the material, as they don't want to let their partner down by not participating or contributing. Additionally, learning with a partner can provide opportunities for collaboration and discussion, which can deepen students' understanding of the material. Working with a partner can also provide a chance for students to teach each other, which can help reinforce their own understanding of the material. Finally, having a learning partner can provide social support and a sense of community, which can make the learning experience more enjoyable and rewarding.

    1. Emergent abilities are not present in small models but can be observed in large models.

      Here’s a lovely blog by Jason Wei that pulls together 137 examples of ’emergent abilities of large language models’. Emergence is a phenomenon seen in contemporary AI research, where a model will be really bad at a task at smaller scales, then go through some discontinuous change which leads to significantly improved performance.

    1. Whether you want to call them mottos, memes, or manifestos, words can be the building blocks of how we think and transmit ideas. You can also gauge how well someone is grasping your concepts—or at least making an effort to—by the language they’re responding to you with as well.

      You can use the way that a person responds to your concepts as a metric for how well they understand you. If they don't understand chances are they will retreat back to jargon to try to hide the fact that they're struggling. If they're getting on well they might have an insightful way to extend your metaphor

    1. À la fin de ce cours, vous serez capable de :analyser un besoin de formation ;designer un dispositif pertinent ;développer ce dispositif en équipe ;implémenter la formation ;évaluer l’action de formation.

      Les objectifs pédagogiques du cours

  9. Nov 2022
    1. Discussion-Based Active Learning Strategies

      There are several unique ways to foster a discussion within a class setting. Some of these include-

      • Small Group Discussions - three to eight people
      • Think-Pair-Share - individually or in pairs, then share to a large group
      • Large Group Discussions- group of students

      • Brainstorming - group of students

      All of these contribute to ideas being shared and analyzed by the individuals involved. This also helps to build great communication skills as well as team building skills.

    1. partnerships, networking, and revenue generation such as donations, memberships, pay what you want, and crowdfunding

      I have thought long about the same issue and beyond. The triple (wiki, Hypothesis, donations) could be a working way to search for OER, form a social group processing them, and optionally support the creators.

      I imagine that as follows: a person wants to learn about X. They can head to the wiki site about X and look into its Hypothesis annotations, where relevant OER with their preferred donation method can be linked. Also, study groups interested in the respective resource or topic can list virtual or live meetups there. The date of the meetups could be listed in a format that Hypothesis could search and display on a calendar.

      Wiki is integral as it categorizes knowledge, is comprehensive, and strives to address biases. Hypothesis stitches websites together for the benefit of the site owners and the collective wisdom that emerges from the discussions. Donations support the creators so they can dedicate their time to creating high-quality resources.

      Main inspirations:

      Deschooling Society - Learning Webs

      Building the Global Knowledge Graph

      Schoolhouse calendar

    1. not really about the content of the sessions. Or anything you take from it. The most important thing are the relationships, the connections you gain from sharing the things you're passionate about with the people who are interested in it, the momentum you build from working on your project in preparation for a session

      I somewhat disagree - I think this community building is successful precisely because there is a shared interest or goal. It goes hand in hand. If there is no connecting theme or goal, the groups fall apart.

    1. A blog post is a very long and complex search query to find fascinating people and make them route interesting stuff to your inbox.

      This is a really cool take on blogging. By writing about interesting people and stuff you are increasing your chances of meeting someone cool and indeed increasing your luck

    1. https://brainsteam.co.uk/2022/11/26/one-week-with-hypothesis/

      I too read a lot of niche papers and feel the emptiness, but because I'm most often writing for myself anyway, its alright. There are times, however, when I see a growing community of people who've left their associative trails behind before I've found a particular page.

      I've used the phrase "digital exhaust" before, but I like the more positive framing of "learning exhaust".

      If you've not found it yet, my own experimentations with the platform can largely be found here: https://boffosocko.com/tag/hypothes.is/

    1. Annotations are the first step of getting useful insights into my notes. This makes it a prerequisite to be able to capture annotations in my note making tool Obsidian, otherwise Hypothes.is is just another silo you’re wasting time on. Luckily h. isn’t meant as a silo and has an API. Using the API and the Hypothes.is-to-Obsidian plugin all my annotations are available to me locally.

      This is key - exporting annotations via the API to either public commonplace books (Chris A Style) or to a private knowledge store seems to be pretty common.

    2. In the same category of integrating h. into my pkm workflows, falls the interaction between h. and Zotero, especially now that Zotero has its own storage of annotations of PDFs in my library. It might be of interest to be able to share those annotations, for a more complete overview of what I’m annotating. Either directly from Zotero, or by way of my notes in Obsidian (Zotero annotatins end up there in the end)

      I've been thinking about this exact same flow. Given that I'm mostly annotating scientific papers I got from open access journals I was wondering whether there might be some way to syndicate my zotero annotations back to h via a script.

    1. Whatever your thing is, make the thing you wish you had found when you were learning. Don’t judge your results by “claps” or retweets or stars or upvotes - just talk to yourself from 3 months ago

      Completely agree, this is a great intrinsic metric to measure the success of your work by.

    2. a habit of creating learning exhaust:

      not sure I love the metaphor but I can definitely see the advantages of leaving your learnings "out there" for others to see and benefit from

    1. Our kids have lost so much—family members, connections to friends and teachers, emotional well-being, and for many, financial stability at home. And, of course, they’ve lost some of their academic progress. The pressure to measure—and remediate—this “learning loss” is intense; many advocates for educational equity are rightly focused on getting students back on track. But I am concerned about how this growing narrative of loss will affect our students, emotionally and academically. Research shows a direct connection between a student’s mindset and academic success.
    1. “Our kids have lost so much—family members, connections to friends and teachers, emotional well-being, and for many, financial stability at home,” the article begins, sifting through a now-familiar inventory of devastation, before turning to a problem of a different order. “And of course, they’ve lost some of their academic progress.”
    1. As one expert reminded, “Bereavement is the No. 1 predictor of poor school outcomes.
    2. Analysts have labelled this as “learning loss,” and many have blamed school closures and remote instruction in the course of the past two years as the culprit. Essentially, schools serving largely Black and Latino populations were more likely to turn to remote teaching.
    1. Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

      Look into Eamonn Keogh's papers that won "best paper awards"

    1. Meta-analysis statistical procedures provide a measure of the difference between two groups thatis expressed in quantitative units that are comparable across studies

      The units are only "comparable across studies" if there weren't any mishaps (eg, clinical or methodological heterogeneity). If there's clinical heterogeneity, then we're probably comparing apples to oranges (ie, either participants, interventions, or outcomes are different among studies). If there's methodological heterogeneity, then that means there's a difference in study design

    2. Quadrants I and II: The average student’s scores on basic skills assessments increase by21 percentiles when engaged in non-interactive, multimodal learning (includes using textwith visuals, text with audio, watching and listening to animations or lectures that effectivelyuse visuals, etc.) in comparison to traditional, single-mode learning. When that situationshifts from non-interactive to interactive, multimedia learning (such as engagement insimulations, modeling, and real-world experiences – most often in collaborative teams orgroups), results are not quite as high, with average gains at 9 percentiles. While notstatistically significant, these results are still positive.

      I think this is was Thomas Frank was referring to in his YT video when he said "direct hands-on experience ... is often not the best way to learn something. And more recent cognitive research has confirmed this and shown that for basic concepts a more abstract learning model is actually better."

      By "more abstract", I guess he meant what this paper calls "non-interactive". However, even though Frank claims this (which is suggested by the percentile increases shown in Quadrants I & II), no variance is given and the authors even state that, in the case of Q II (looking at percentile increase of interactive multimodal learning compared to interactive unimodal learning), the authors state that "results are not quite as high [as the non-interactive comparison], with average gains at 9 percentiles. While not statistically significant, these results are still positive." (emphasis mine)

      Common level of signifcances are \(\alpha =.20,~.10,~.05,~.01\)

    3. Paper gives surprisingly good overview of models of learning within the cognitive sciences up to 2008. Attempts to dispel myths and summarize the literature on multimodal learning. Link to paper on Semantic Scholar

    4. Multimodal Learning Through Media:What the Research Says

      A white paper written by Metiri Group commissioned by Cisco in 2008. I came here to fact check some claims on this YT video about a "Feynman Technique 2.0".

      The claims were that

      1. direct hands-on experience in unimodal learning is (on average) inferior to multi-modal learning that wasn't hand-on. viz., for "basic concepts", a more abstract learning model is better

      2. "Once you get into higher-order concepts then hand-on experience is better"

      Page 13 was displayed while making these claims.

      These claims still need to be verified.

    5. Scaffolding is the act of providing learners with assistance or support to perform a taskbeyond their own reach if pursued independently when “unassisted.”

      Wood, Bruner, & Ross (1976) define scaffolding as what? (Metiri Group, Cisco Sytems, 2008) The act of providing learners with assistance or support to perform a task beyond their own reach if pursued independently when "unassisted."

      What term do Wood, Bruner, & Ross (1976) define as "The act of providing learners with assistance or support to perform a task beyond their own reach if pursued independently when 'unassisted.'"? (Metiri Group, Cisco Sytems, 2008) Scaffolding

    6. Schemas are chunks of multiple individual units of memory that are linked into a system ofunderstanding

      How do Bransford, Brown, & Cocking (2000) define schemas? (Metiri Group, Cisco Sytems, 2008) As chunks of multiple individual units of memory that are linked into a system of understanding

      What term is defined by Bransford, Brown, & Cocking (2000) to be "chunks of multiple individual units of memory that are linked into a system of understanding"? (Metiri Group, Cisco Sytems, 2008) Schemas.

    7. Learning is defined to be “storage of automated schema in long-term memory.

      How is learning defined by Sweller in 2002? (Metiri Group, Cisco Sytems, 2008) The storage of automated schema in long-term memory

      What term does Sweller define as the "storage of automated schema in long-term memory"?

    1. e argue that mutual learningwould benefit sentiment classification since it enriches theinformation required for the training of the sentiment clas-sifier (e.g., when the word “incredible” is used to describe“acting” or “movie”, the polarity should be positive)

      By training a topic model that has "similar" weights to the word vector model the sentiment task can also be improved (as per the example "incredible" should be positive when used to describe "acting" or "movie" in this context

    2. . However, such a framework is not applicablehere since the learned latent topic representations in topicmodels can not be shared directly with word or sentencerepresentations learned in classifiers, due to their differentinherent meanings

      Latent word vectors and topic models learn different and entirely unrelated representations

    1. “The metaphor is that the machine understands what I’m saying and so I’m going to interpret the machine’s responses in that context.”

      Interesting metaphor for why humans are happy to trust outputs from generative models

    1. Excel Spreadsheet Permissions on Android

      I've been notified of a problem for some Microsoft Excel users on Android. Which affects access to spreadsheets for Shrewd Learning followers, subscribers, and members. So I'm preparing documentation for this.

      Shrewd Learning Subscribers can access my progress notes.

    1. The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

      环境科学与工程(ESE)领域日益增长的数据量和复杂性,伴随着数据分析技术的进步而不断提高。先进的数据分析方法,如机器学习(ML) ,已经成为揭示隐藏模式或推断相关性的不可或缺的工具,而传统的分析方法面临着局限性或挑战。然而,机器学习的概念和实践并没有得到广泛的应用。该特性探索了机器学习在 ESE 领域革新数据分析和建模的潜力,并涵盖了此类应用所需的基本知识。首先,我们使用五个示例来说明 ML 如何处理复杂的 ESE 问题。然后,我们总结了机器学习在 ESE 中的四种主要应用类型: 预测、提取特征重要性、检测异常和发现新材料或化学品。接下来,我们介绍了 ESE 中机器学习应用所需的基本知识和目前存在的缺陷,重点介绍了应用机器学习时三个重要但经常被忽视的组成部分: 正确的模型开发、适当的模型解释和良好的适用性分析。最后,我们讨论了机器学习工具在 ESE 中的应用所面临的挑战和未来的机遇,以突出机器学习在这一领域的潜力。

    1. Teachers are actually managing something far more important than test scores. They're managing, massaging, inspiring, reinforcing and jollying along the only thing that helps a kid learn, which is the energy and trust in the classroom. Good teachers do it instinctively and constantly, though it's exhausting and painstaking to do. This is the one thing teachers don't get rewarded for or credit for. They care enough to manage the waves of excitement and wonder and fatigue and frustration in their classrooms. They manage the waves and let the particles take care of themselves.
    1. Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence.
    1. "On the Opportunities and Risks of Foundation Models" This is a large report by the Center for Research on Foundation Models at Stanford. They are creating and promoting the use of these models and trying to coin this name for them. They are also simply called large pre-trained models. So take it with a grain of salt, but also it has a lot of information about what they are, why they work so well in some domains and how they are changing the nature of ML research and application.

    1. https://www.cold-takes.com/learning-by-writing/

      Meh... generic process. Nothing broadly new here. The extended example is flawed because it's a broad thesis by a top level aggregator who doesn't have their own expert level experience (seemingly). Better to start from there, but delving more deeply into the primary literature of people who may have that experience.