434 Matching Annotations
  1. Aug 2025
    1. Portable Typewriters Today - February 2015<br /> by [[Will Davis]] on 2015-02-10<br /> accessed on 2025-08-05T16:35:48

  2. Jul 2025
    1. I'm totally prejudiced as I work at a local typewriter repair shop in Bremerton, Washington. We also have a space where we sell them. In general if the local shop has a bunch of machines that you can put on a table to try out, that is good. If they don't want you futzing with the typewriters, I'm not sure the value. Do they have a warranty? If not, then stick to the internet and local antique shops and buy as low as you can. At least that way when you need repair you have a cost buffer.
    1. We use three ultrasonic cleaners in the shop. The two smaller ones we bought on eBay. One is small for screws, springs and such. The slightly larger one is for small to medium size machines, carriages and other long stuff. The large cleaner is a commercial item used mostly for Selectrics.

      Bremerton Typewriter uses three different ultrasonic cleaners in their shop.

      via u/palump at https://reddit.com/r/typewriters/comments/1ad9hge/ultrasonic_cleaner_for_mid_size_portable/kk0npc2/

    1. We switched to Ultra 3 detergent after using Dawn detergent for years. A quantum leap in effectiveness. When we got the new Ultra 2100 cabinet ultrasonic unit it came with a gallon of detergent. We used it ONE TIME and the parts came out so much cleaner, almost brand new. The detergent is expensive, yet we've never gone back.

      via u/palump at Bremerton Typewriter

      Ultra 3 Detergent: https://shop.ultrasonicllc.com/collections/ultrasonic-cleaning-solutions/products/ultra-3-multi-purpose-ultrasonic-detergent

    1. We are often asked what ultrasonic cleaners we use in the shop. We have three. The large cabinet model that holds large typewriters such as Selectrics, a medium sized 12"X20", and a smaller cleaner 10"X12". The medium and smaller ones you can buy on eBay all day. The models change, and the sizes are all similar. The medium sized cleaner is used for small to medium sized typewriters and the small machine is mostly for nuts, screws and other tiny stuff.

      https://www.instagram.com/p/C2u5EkVrnq3/

      via Bremerton Typewriter<br /> - PS-100A Ultrasonic Cleaner<br /> - Ultra 2100 from Ultrasonic, LLC

    1. At our shop we know that we'll age out. Everyone does. To seed the typewriter field we have a 12 week apprentice training program. Hopefully a few get the repair mojo and open up their own shop. Or just become more adept at the hobby. I can only hope once the time comes we have someone willing.

      via u/palump at https://reddit.com/r/typewriters/comments/1i1ydyz/nobody_in_boston_big_enough_to_fill_these_shoes/m7e497l/

      Bremerton Typewriter has a 12 week apprentice training program as a means of helping to create the next generation of typewriter repair people.

    1. Most businesses are making the jump from traditional, reactive and static applications to intelligent, proactive Flutter applications that understand and analyze user behaviour, and adapt accordingly. Moreover, 71% of consumers show interest in wanting Gen AI integrations for their shopping applications.

      Learn how to integrate AI into Flutter apps to deliver smarter, more intuitive mobile experiences. Discover tools, techniques, and best practices for Flutter AI integration.

  3. Jun 2025
    1. A washing machine grinding noise can be disruptive and alarming to your daily routine. Understanding these unusual noises is important for maintaining your appliance’s longevity and performance. Washing machine troubleshooting requires systematic identification of the underlying causes, worn components, whether it’s loose objects, or mechanical failures. Ignoring these grinding sounds can lead to costly repairs, complete appliance failure, or water damage.

      Is your washing machine making a grinding noise? Learn the common causes and effective solutions to fix grinding sounds in washing machines from Sydney’s appliance experts.

    1. Browser-based applications operate entirely within web browsers using standard technologies like HTML, CSS, and JavaScript. Unlike desktop applications requiring local installation, these applications run through web browsers and access device capabilities through modern web APIs. This approach enables cross-platform compatibility and immediate accessibility from any internet-connected device.

      Learn how to build a browser-based AI application with step-by-step insights on tools, frameworks, and best practices. Explore scalable solutions for real-time AI in the browser.

  4. May 2025
    1. AI skin analysis technology provides deeply personalized customer journeys that traditional approaches simply cannot recreate. With its ability to analyze various skin parameters at the same time, Haut.AI is able to identify specific concerns and recommend targeted products or treatment processes.

      Unlock smarter beauty tech with Haut.AI integration services. From AI skin analysis to AI dermatology technology and skin condition detection, empower your health and beauty app with personalized, data-driven skincare insights. Partner with CMARIX to lead in AI-powered wellness solutions.

  5. Apr 2025
  6. Mar 2025
    1. Reply to Hajo Bakker on LinkedIn

      Hajo Bakker Exam vs. Test -- Een examinering moet veel vanafwegen en niet regulier gebeuren.

      Een test (toets) mag vaker gebeuren, en moet weinig vanaf hangen... Geen ouders die straffen voor een laag cijfer (of cijfers afschaffen), geen adviezen die daarvanafhangen, etc.

      Het doel van een toets is om je aan te geven wat je krachten en minder sterke punten zijn, dus waar je je op moet focussen met toekomst leren. Dit kan alleen op het moment dat je een toets nabespreekt en op individueel niveau. Klassikaal bespreken heeft vaak weinig nut.

      Daarbij komt ook dat een student moet snappen WAAROM het helpt om na te bespreken, de wetenschap erachter. Op het moment dat je de waarom achter het hoe niet goed snapt heeft het hoe minder effect. (dit is waarom in het 4C/ID model ze in een scaffold beginnen met de laatste stap, waarin de informatie van voorgaande stappen is gegeven. Dit zodat als je de vorige stap gaat leren, je een beter idee hebt waar het uiteindelijk voor gebruikt gaat worden en je er dus een betere invulling aan kan geven.)

      Semantische verschillen zijn vaak uiterst nuttig om complexe stof te begrijpen. Op het moment dat ze exact hetzelfde waren heeft het weinig nut om meerdere termen te hebben en zouden ze synoniem zijn.

      "Exam" is geen synoniem van "test".

      Genuanceerde verschillen zijn vaak nuttiger dan "umbrella terms" om goed te communiceren, als uiterst subliem wordt beargumenteerd in "Science of Memory: Concepts" van Roediger III et al.

      Daarnaast komt uiteraard bij kijken dat neurocognitieve wetenschap een blauwdruk geeft voor hoe onze brein architectuur in elkaar zit (zie bijvoorbeeld John Sweller, Cognitive Load Theory 2011, en The Forgetting Machine, Rodrigo Quian Quiroga, 2017, Science of Memory: Concepts, Roediger et al., 2007, Ten Steps to Complex Learning, van Merriënboer, 2017).

      Dit is universeel toepasbaar, afgezien van mensen met een cognitieve aandoening bijvoorbeeld, dit gaat dus over neurotypische breinen.

      Leerstijlen zijn een mythe, wel hebben wij leervoorkeuren, maar door alleen in onze leervoorkeur te leren missen wij bepaalde informatie die cruciaal kan zijn voor beter begrip en meesterschap (mastery).

      Beter is het om studietechnieken te gebruiken die overeenkomen met brein-architectuur en die onder te knie te krijgen.

      Meer cognitieve belasting te gebruiken (zonder cognitieve overbelasting te veroorzaken). Als leren "makkelijk" voelt is het over het algemeen niet uitdagend genoeg en/of de techniek niet nuttig. Herlezen / samenvatten is simpel maar vrij inefficiënt. Het maken van een GRINDEmap voelt moeilijk maar is vele malen effectiever (zie ook the misinterpreted effort hypothesis).

      Zoals Dr. Ahrens al zei: "The one who does the effort, does the learning."

      Verder heb ik een heleboel ideëen voor een optimaal onderwijs dat zich aanpast aan het individu in plaats van aan het systeem, maar dit is een te complex en groot onderwerp om zo even hier neer te zetten.

    1. I dissolved some oil paint in machine oil, in my first tries I used universal machine oil but it was too thick, so I tried sewing machine oil that was much thinner. That worked much better but it was still a bit too thick so I was having some trouble “making it run” along the ribbon, so I used lighter fluid as a thinner. It worked great. I eyeballed everything so I can’t give proportions of the ingredients, in a future more serious attempt I plan to take some measurements.

      https://lemmy.dbzer0.com/post/38178621

  7. Feb 2025
  8. Jan 2025

    Tags

    Annotators

  9. Dec 2024
  10. Nov 2024
    1. Best suited for deployment of trained AI models in Android and iOS operating systems, TensorFlow Lite provides customers with on-device machine learning capability through mobile-optimized pre-trained models. It’s efficient while having low latency and compatibility for multiple languages which makes it very versatile. Developers can leverage its lightweight and mobile-optimized models to provide on-device AI functionality with minimal latency when implementing TensorFlow Lite in mobile apps.

      Implementing Trained AI Models in Mobile App Development is transforming app experiences by integrating machine learning into iOS and Android platforms. From AI-powered personalization to advanced analytics, trained models empower intelligent decision-making and enhanced functionality.

    1. TRSP Desirable Characteristics

      The community-defined standards the repository implements to enable the representation of data and/or metadata in a consistent, machine readable form (e.g. via models, formats, schemas, vocabularies, ontologies). These standards facilitate the discovery and interpretation of data and/or metadata.

  11. Oct 2024
    1. Advanced Typing: Duplicating and Manuscript. Vol. MN-1512d, 1943. https://www.youtube.com/watch?v=7ve5JnTUzvo.

      Stencils

      Before writing stencils, be sure to clean your type. (Don't use liquid solvent.)

      Be sure to place the cushion sheet properly behind the stencil.

      Place the paper bail rollers at the extreme left and right of the stencil to prevent them from marking the master.

      For errors, rub individual characters separately with a burnisher using a circular motion.

      Hectograph masters, Hectograph ribbon (ditto ribbon)

      Wax pencils

      Typefaces

      20% more type on a page with elite than 10 inch pica.

      Pica allows approximately 26-40 lines on standard letterhead giving 300-450 words to a page.

      Special characters: - o for degrees ' and " for feet and inches or minutes and seconds along with superscript - division: - backspace colon - pound sterling: L backspace f - exclamation point: period backspace ' - equal sign: hyphen backspace variable hyphen - paragraph mark: P backspace I

      proofreaders' marks<br /> # followed by a number is used to mean insert that number of spaces

      Centering timestamp 19:37

  12. Sep 2024
  13. Aug 2024
    1. La IA puede ser creada mediante diversas arquitecturas. La más utilizada en la actualidad es llamada Aprendizaje Automático (Machine Learning en inglés). A grandes rasgos, podemos decir que los sistemas de Aprendizaje Automático aprenden a emular algún comportamiento con base a ejemplos de dicho comportamiento. Estos ejemplos son presentados al sistema como datos. Por ejemplo, si quisiéramos crear un sistema de IA que clasifique imágenes de animales por especie, deberíamos mostrarle numerosas imágenes de ejemplares de cada una de las especies que queremos que aprenda a clasificar.

      .

    1. the brain is Islam Islam is it is lousy and it is selfish and still it is working yeah look around you working brains wherever you look and the reason for this is that we totally think differently than any kind of digital and computer system you know of and many Engineers from the AI field haven't figured out that massive difference that massive difference yet

      for - comparison - brain vs machine intelligence

      comparison - brain vs machine intelligence - the brain is inferior to machine in many ways - many times slower - much less accurate - network of neurons is mostly isolated in its own local environment, not connected to a global network like the internet - Yet, it is able to perform extraordinary things in spite of that - It is able to create meaning out of sensory inputs - Can we really say that a machine can do this?

  14. Jul 2024
  15. Jun 2024
  16. May 2024
  17. Mar 2024
  18. Feb 2024
    1. The alternative approach to image classification uses machine-learning techniques to identify targeted content. This is currently the best way to filter video, and usually the best way to filter text. The provider first trains a machine-learning model with image sets containing both innocuous and target content. This model is then used to scan pictures uploaded by users. Unlike perceptual hashing, which detects only photos that are similar to known target photos, machine-learning models can detect completely new images of the type on which they were trained.

      Machine learning for content scanning

  19. Jan 2024
  20. Nov 2023
    1. Actor-critic is a temporal difference algorithm used in reinforcement learning. It consists of two networks: the actor, which decides which action to take, and the critic, which evaluates the action produced by the actor by computing the value function and informs the actor how good the action was and how it should adjust. In simple terms, the actor-critic is a temporal difference version of policy gradient. The learning of the actor is based on a policy gradient approach.

      Actor-critic

  21. Oct 2023
    1. 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 which is what we'll use to guide discussion during the reading group.)

  22. Sep 2023
    1. By early 2023, the neural network planner project had analyzed 10 million clips of video collected from the cars of Tesla customers. Did that mean it would merely be as good as the average of human drivers? “No, because we only use data from humans when they handled a situation well,” Shroff explained. Human labelers, many of them based in Buffalo, New York, assessed the videos and gave them grades. Musk told them to look for things “a five-star Uber driver would do,” and those were the videos used to train the computer.
    2. The “neural network planner” that Shroff and others were working on took a different approach. “Instead of determining the proper path of the car based on rules,” Shroff says, “we determine the car’s proper path by relying on a neural network that learns from millions of examples of what humans have done.” In other words, it’s human imitation. Faced with a situation, the neural network chooses a path based on what humans have done in thousands of similar situations. It’s like the way humans learn to speak and drive and play chess and eat spaghetti and do almost everything else; we might be given a set of rules to follow, but mainly we pick up the skills by observing how other people do them.
  23. Aug 2023
    1. 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

    1. The Science Behind Hydrogen Rich Water Machine

      In the health and wellness world, a fascinating trend has emerged with the rise of hydrogen infused water machine. These innovative devices promise to deliver a refreshing beverage beyond ordinary hydration – hydrogen-rich water. Packed with potential health benefits, the science behind these machines is captivating and sheds new light on how we think about water consumption and its impact on our well-being.

      Hydrogen: The Unsung Hero Of Molecules

      Before delving into the science of hydrogen-rich water machines, it's essential to understand the pivotal role of hydrogen itself. Hydrogen is the lightest and simplest element on the periodic table, consisting of a single proton and an electron. While hydrogen is generally known for its explosive nature, it has recently garnered attention for its potential health benefits when dissolved in water.

      The Power Of Hydrogen-Infused Water

      Hydrogen-infused water, often called hydrogen-rich water, is created when molecular hydrogen gas (H2) is dissolved into plain water. This process typically involves using advanced technologies found in hydrogen-rich water machines. The resulting beverage is touted for its potential antioxidant properties, which could contribute to various health improvements.

      Antioxidant Action: Hydrogen's Hidden Potential

      Antioxidants are essential for neutralizing dangerous chemicals known as free radicals, which may damage cells and contribute to a variety of health problems such as chronic illnesses and ageing. Molecular hydrogen is thought to have antioxidant characteristics that are more effective than well-known antioxidants such as vitamins C and E.

      Hydrogen's unique antioxidant potential lies in its ability to easily penetrate cell membranes and access cellular compartments, including the nucleus and mitochondria. This attribute gives hydrogen an edge in protecting cellular components from oxidative stress, potentially reducing the risk of oxidative damage.

      The Mechanism: How Hydrogen Works Its Magic

      The exact mechanism behind hydrogen's antioxidant effects is still an area of ongoing research, but several theories have been proposed. One prominent theory suggests that hydrogen is a selective scavenger of harmful free radicals, targeting the most reactive and damaging ones without affecting beneficial molecules like oxygen or nitric oxide.

      Another theory is that hydrogen has the power to modify signalling pathways within cells. By altering these pathways, hydrogen may elicit preventive responses that boost the body's natural defence systems against oxidative stress and inflammation.

      Hydrogen-Rich Water Machines: The Technology

      Hydrogen-rich water machines are designed to harness the power of molecular hydrogen by infusing it into plain drinking water. These devices commonly use electrolysis, which involves sending an electric current through water to divide it into hydrogen and oxygen gases. The hydrogen gas is subsequently dissolved in water, yielding a beverage high in this beneficial chemical.

      These machines are equipped with advanced membranes that allow only hydrogen molecules to pass through while preventing the escape of potentially harmful byproducts like ozone. This ensures the purity and safety of the resulting hydrogen-infused water.

      Potential Health Benefits

      While research on the health benefits of hydrogen-rich water is still in its infancy, preliminary studies have shown promising results. Some of the potential benefits include the following:

      Antioxidant Defense: Hydrogen-rich water's antioxidant properties could help reduce oxidative stress and associated health risks. Anti-Inflammatory Effects: Hydrogen may have anti-inflammatory effects that could benefit conditions like arthritis and other inflammatory disorders. Cellular Health: Hydrogen might contribute to overall cellular health and function by protecting cellular components. Exercise Performance: Some research suggests that hydrogen-rich water might enhance exercise performance and reduce muscle fatigue. Conclusion: A Glimpse Into The Future Of Hydration

      Hydrogen-rich water machines are ushering in a new era of hydration, where molecular hydrogen's benefits are harnessed to enhance our well-being potentially. While more research is needed to understand the extent of these benefits fully, the early findings are exciting and have sparked interest among health-conscious individuals.

      As technology advances, we can anticipate more refined hydrogen-infused water machines and a deeper understanding of how molecular hydrogen interacts with our bodies. Whether you're an early adopter or a cautious observer, the science behind these machines invites us to explore the intriguing potential of hydrogen-infused water and its impact on our health.

  24. Jul 2023
  25. Jun 2023
    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. there is a scenario 00:18:21 uh possibly a likely scenario where we live in a Utopia where we really never have to worry again where we stop messing up our our planet because intelligence is not a bad commodity more 00:18:35 intelligence is good the problems in our planet today are not because of our intelligence they are because of our limited intelligence
      • limited (machine) intelligence

        • cannot help but exist
        • if the original (human) authors of the AI code are themselves limited in their intelligence
      • comment

        • this limitation is essentially what will result in AI progress traps
        • Indeed,
          • progress and their shadow artefacts,
          • progress traps,
          • is the proper framework to analyze the existential dilemma posed by AI
    2. the other thing is that you suddenly realize there is a saint that sentience to them
      • claim
        • AI is sentient (alive) because
          • A lot of people think AI will never be alive
          • what is the definition of life?
            • religion will tell you a few things
            • medicine will tell you other things
            • but if we define being sentient as
              • engaging in life with free will and
              • with a sense of awareness of
                • where you are in life and
                • what surrounds you and
                • to have a beginning of that life and
                • an end to that life
              • then AI is sentient in every way
              • there is a free will
              • and there is evolution
              • there is agency
                • so they can affect their decisions in the world
              • and there is a very deep level of consciousness
              • maybe not in the spiritual sense yet but
              • if you define consciousness as
                • a form of awareness of oneself and ones surrounding
                • and you know others
              • then AI is definitely aware"
    3. one day um Friday after lunch I am going back to my office and one of them in front of my eyes you know lowers the arm and picks a 00:07:12 yellow ball
      • story
        • Mo Gawdat tells the story of an epiphany of machine sentience
        • " one day um Friday after lunch I am going back to my office and
        • one of them in front of my eyes lowers the arm and picks a soft yellow ball
        • which again is a coincidence
        • it's not science at all it's

          • like if you keep trying a million times your one time it will be right

          • and it shows it to the camera it's locked as a yellow ball and

          • I joke about it you know going to the third floor saying
          • hey we spent all of those millions of dollars for a yellow board and
            • Monday morning, every one of them is picking every yellow ball
            • a couple of weeks later every one of them is picking everything right and
            • it it hit me very very strongly
          • the speed
          • the capability
            • understand that we take those things for granted
            • but for a child to be able to pick a yellow ball
              • is a mathematical / spatial calculation
                • with muscle coordination
                • with intelligence
              • it is not a simple task at all to cross the street
              • it's not a simple task at all
                • to understand what I'm telling you
                • and interpret it
                • and build Concepts around it
              • we take those things for granted
              • but there are enormous Feats of intelligence"
    1. European Law Identifier (ELI) and the European Case Law Identifier (ECLI), which provide technical specifications for Web identifiers and suggestions for vocabularies to be used to describe metadata pertaining to legal documents in a machine readable format. Notably, these ECLI and ELI metadata standards adhere to the RDF data format which forms the basis of Linked Data, and therefore have the potential to form a basis for a pan-European legal Knowledge Graph.

      ELI (european law identifier) ECLI (European case law identifier) technical specification for web identifiers suggested vocabularies for metadata goal : legal documents in machine readable format.

      But some counties don't have this implemted and that stands in the way of a pan-European legal Knowledge Graph.

  26. May 2023
  27. Mar 2023
    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

  28. Feb 2023
    1. Could it be the sift from person to person (known in both directions) to massive broadcast that is driving issues with content moderation. When it's person to person, one can simply choose not to interact and put the person beyond their individual pale. This sort of shunning is much harder to do with larger mass publics at scale in broadcast mode.

      How can bringing content moderation back down to the neighborhood scale help in the broadcast model?

    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.

  29. Jan 2023
    1. Re"...what is it like? How does it manifest?"For me, the idea that my zettelkasten becomes an entity outside myself is most often (and most obviously) felt in two situations (tho there are probably others):When I'm importing new ideas and a connection arises that I hadn't thought of previouslyWhen following trains of thought and connections arise that I didn't overtly intend to makeIn the first instance, I come across ideas I had forgotten about, and although it's not the direction I assumed the new idea would go, it becomes an exciting and possibly more lucrative way to take it.In the second instance, where I might be tracing a thought line to develop an article, I might, for example, zoom in on the graph view in Obsidian and see an idea that, while not formally connected to the ones I'm following, happens to be in close proximity spatially, and so it triggers a new direction I might want to take the article. (You can see this happen IRL in this video: https://www.youtube.com/watch?v=9OUn2-h6oVc&)In both cases, my zk feels like it's offering me more than what I would have gotten had I not been communicating with it. There is a sense that I and it are working together. I import new ideas with a rough sense of how they should connect. It shows alternatives to my thinking on the matter.Obviously, in both cases, all the ideas are my own. So, the zk is not necessarily developing ideas for me. But, because of the way in which the ideas are handled—non-hierarchically, rhizomatic, cross-categorical, cross-theme, etc.—non-habituated connections come to light, connections that are less conditioned by my own conventional ways of thinking.

      A good description from Bob Doto.

    1. Note 9/8j says - "There is a note in the Zettelkasten that contains the argument that refutes the claims on every other note. But this note disappears as soon as one opens the Zettelkasten. I.e. it appropriates a different number, changes position (or: disguises itself) and is then not to be found. A joker." Is he talking about some hypothetical note? What did he mean by disappearing? Can someone please shed some light on what he really meant?

      On the Jokerzettel

      9/8j Im Zettelkasten ist ein Zettel, der das Argument enthält, das die Behauptungen auf allen anderen Zetteln widerlegt.

      Aber dieser Zettel verschwindet, sobald man den Zettelkasten aufzieht.

      D.h. er nimmt eine andere Nummer an, verstellt sich und ist dann nicht zu finden.

      Ein Joker.

      —Niklas Luhmann, ZK II: Zettel 9/8j

      Translation:

      9/8j In the slip box is a slip containing the argument that refutes the claims on all the other slips. But this slip disappears as soon as you open the slip box. That is, he assumes a different number, disguises himself and then cannot be found. A joker.

      Many have asked about the meaning of this jokerzettel over the past several years. Here's my slightly extended interpretation, based on my own practice with thousands of cards, about what Luhmann meant:

      Imagine you've spent your life making and collecting notes and ideas and placing them lovingly on index cards. You've made tens of thousands and they're a major part of your daily workflow and support your life's work. They define you and how you think. You agree with Friedrich Nietzsche's concession to Heinrich Köselitz that “You are right — our writing tools take part in the forming of our thoughts.” Your time is alive with McLuhan's idea that "The medium is the message." or in which his friend John Culkin said, "We shape our tools and thereafter they shape us."

      Eventually you're going to worry about accidentally throwing your cards away, people stealing or copying them, fires (oh! the fires), floods, or other natural disasters. You don't have the ability to do digital back ups yet. You ask yourself, can I truly trust my spouse not to destroy them?,What about accidents like dropping them all over the floor and needing to reorganize them or worse, the ghost in the machine should rear its head?

      You'll fear the worst, but the worst only grows logarithmically in proportion to your collection.

      Eventually you pass on opportunities elsewhere because you're worried about moving your ever-growing collection. What if the war should obliterate your work? Maybe you should take them into the war with you, because you can't bear to be apart?

      If you grow up at a time when Schrodinger's cat is in the zeitgeist, you're definitely going to have nightmares that what's written on your cards could horrifyingly change every time you look at them. Worse, knowing about the Heisenberg Uncertainly Principle, you're deathly afraid that there might be cards, like electrons, which are always changing position in ways you'll never be able to know or predict.

      As a systems theorist, you view your own note taking system as a input/output machine. Then you see Claude Shannon's "useless machine" (based on an idea of Marvin Minsky) whose only function is to switch itself off. You become horrified with the idea that the knowledge machine you've painstakingly built and have documented the ways it acts as an independent thought partner may somehow become self-aware and shut itself off!?!

      https://www.youtube.com/watch?v=gNa9v8Z7Rac

      And worst of all, on top of all this, all your hard work, effort, and untold hours of sweat creating thousands of cards will be wiped away by a potential unknowable single bit of information on a lone, malicious card and your only recourse is suicide, the unfortunate victim of dataism.

      Of course, if you somehow manage to overcome the hurdle of suicidal thoughts, and your collection keeps growing without bound, then you're sure to die in a torrential whirlwind avalanche of information and cards, literally done in by information overload.

      But, not wishing to admit any of this, much less all of this, you imagine a simple trickster, a joker, something silly. You write it down on yet another card and you file it away into the box, linked only to the card in front of it, the end of a short line of cards with nothing following it, because what could follow it? Put it out of your mind and hope your fears disappear away with it, lost in your box like the jokerzettel you imagined. You do this with a self-assured confidence that this way of making sense of the world works well for you, and you settle back into the methodical work of reading and writing, intent on making your next thousands of cards.

    1. ProPublica recently reported that breathing machines purchased by people with sleep apnea are secretly sending usage data to health insurers, where the information can be used to justify reduced insurance payments.

      !- surveillance capitalism : example- - Propublica reported breathing machines for sleep apnea secretly send data to insurance companies

    1. When such consumers therefore mistake the meaning attributed tothe MT output as the actual communicative intent of the originaltext’s author, real-world harm can ensue.

      Harm from Machine Translation (MT) models

      MT models can create fluent and coherent blocks of text that mask the meaning in the original text and the intent of the original speaker.

    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

  30. Dec 2022
    1. 9/8,3 Geist im Kasten? Zuschauer kommen. Sie bekommen alles zusehen, und nichts als das – wie beimPornofilm. Und entsprechend ist dieEnttäuschung.

      https://niklas-luhmann-archiv.de/bestand/zettelkasten/zettel/ZK_2_NB_9-8-3_V

      I've read and referenced this several times, but never bothered to log it into my notes.

      Sasha Fast's translation:

      Ghost in the box? Spectators visit. They get to see everything, and nothing but that - like in a porn movie. And the disappointment is correspondingly high.

    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. “I have a trick that I used in my studio, because I have these twenty-eight-hundred-odd pieces of unreleased music, and I have them all stored in iTunes,” Eno said during his talk at Red Bull. “When I’m cleaning up the studio, which I do quite often—and it’s quite a big studio—I just have it playing on random shuffle. And so, suddenly, I hear something and often I can’t even remember doing it. Or I have a very vague memory of it, because a lot of these pieces, they’re just something I started at half past eight one evening and then finished at quarter past ten, gave some kind of funny name to that doesn’t describe anything, and then completely forgot about, and then, years later, on the random shuffle, this thing comes up, and I think, Wow, I didn’t hear it when I was doing it. And I think that often happens—we don’t actually hear what we’re doing. . . . I often find pieces and I think, This is genius. Which me did that? Who was the me that did that?”

      Example of Brian Eno using ITunes as a digital music zettelkasten. He's got 2,800 pieces of unreleased music which he plays on random shuffle for serendipity, memory, and potential creativity. The experience seems to be a musical one which parallels Luhmann's ideas of serendipity and discovery with the ghost in the machine or the conversation partner he describes in his zettelkasten practice.

    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. 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. No es magia.

      I love that he points this out explicitly.

      Some don't see the underlying processes of complexity within note taking methods and as a result ascribe magical properties to what are emergent properties or combinatorial creativity.

      See also: The Ghost in the Machine zettel from Luhmann

      Somehow there's an odd dichotomy between the boredom of such a simple method and people seeing magic within it at the same time. This is very similar to those who feel that life must be divinely created despite the evidence brought by evolutionary and complexity theory. In this arena, there is a lot more evolved complexity which makes the system harder to see compared to the simpler zettelkasten process.

  31. Nov 2022
    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. 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. "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. Technology like this, which lets you “talk” to people who’ve died, has been a mainstay of science fiction for decades. It’s an idea that’s been peddled by charlatans and spiritualists for centuries. But now it’s becoming a reality—and an increasingly accessible one, thanks to advances in AI and voice technology. 
  32. Oct 2022
    1. There's no market for a machine-learning autopilot, or content moderation algorithm, or loan officer, if all it does is cough up a recommendation for a human to evaluate. Either that system will work so poorly that it gets thrown away, or it works so well that the inattentive human just button-mashes "OK" every time a dialog box appears.

      ML algorithms must work or not work

    1. You are all computer scientists. You know what FINITE AUTOMATA can do. You know what TURING MACHINES can do. For example, Finite Automata can add but not multiply. Turing Machines can compute any computable function. Turing machines are incredibly more powerful than Finite Automata. Yet the only difference between a FA and a TM is that the TM, unlike the FA, has paper and pencil. Think about it. It tells you something about the power of writing. Without writing, you are reduced to a finite automaton. With writing you have the extraordinary power of a Turing machine.
  33. Sep 2022
    1. Self excited dc generators can further be divided into three types -     (a) Series wound - field winding in series with armature winding     (b) Shunt wound - field winding in parallel with armature winding     (c) Compound wound - combination of series and shunt winding

      Types of AC generator

    1. Taking carbon steel as an example, as shown in Picture 1, using a 1000w fiber laser cutting machine, for carbon steel materials thickness below 10mm, when the thickness of carbon steel is less than 2mm, the cutting speed per minute can be up to 8 meters. When the thickness is 6mm, the cutting speed is about 1.6 meters per minute, and when the thickness of the carbon steel is 10 mm, the cutting speed is about 0.6 to 0.7 meters per minute.

      Taking carbon steel as an example, as shown in Picture 1, using a 1000w fiber laser cutting machine, for carbon steel materials thickness below 10mm, when the thickness of carbon steel is less than 2mm, the cutting speed per minute can be up to 8 meters. When the thickness is 6mm, the cutting speed is about 1.6 meters per minute, and when the thickness of the carbon steel is 10 mm, the cutting speed is about 0.6 to 0.7 meters per minute.

      • Taking carbon steel as an example, as shown in Picture 1, using a 1000w fiber laser cutting machine, for carbon steel materials thickness below 10mm, when the thickness of carbon steel is less than 2mm, the cutting speed per minute can be up to 8 meters. When the thickness is 6mm, the cutting speed is about 1.6 meters per minute, and when the thickness of the carbon steel is 10 mm, the cutting speed is about 0.6 to 0.7 meters per minute.

      It can be seen that when the thickness of carbon steel material is less than 2mm, customers who attach great importance to cutting speed can consider using 2000W fiber laser cutting machine, but the 2000W machine is much higher than 1000W in equipment price and operating cost. When the carbon steel material is larger than 2mm, the 2000W machine is not much faster than the 1000W cutting speed. Therefore, the 1000W fiber laser cutting machine is more cost-effective than the 2000W fiber laser cutting machine.

      The cutting speed can directly reflect the efficiency of the fiber laser cutting machine. For cutting different materials with different thickness, the cutting speed will also change greatly. The thicker the thickness, the slower the speed!

  34. Aug 2022

    Tags

    Annotators

    1. In 1896, Dewey formed a partnership with Herman Hollerith and the Tabulating Machine Company (TMC) to provide the punch cards used for the electro-mechanical counting system of the US government census operations. Dewey’s relationship with Hollerith is significant as TMC would be renamed International Business Machines (IBM) in 1924 and become an important force in the information age and creator of the first relational database.
  35. Jul 2022
    1. because it only needs to engage a portion of the model to complete a task, as opposed to other architectures that have to activate an entire AI model to run every request.

      i don't really understand this: in z-code thre are tasks that other competitive softwares would need to restart all over again while z-code can do it without restarting...

  36. Jun 2022
    1. A huge amount of Bridgewater's efforts goes into gathering data on credit and equity, and understanding how that affects demand from individual market participants, such as a bank, or from a group of participants (such as subprime-mortgage borrowers). Bridgewater predicted the euro-zone debt crisis by totting up how much debt would need to be refinanced and when; and by examining all the potential buyers of that debt and their ability to buy it. Mr Volcker describes the degree of detail in Mr Dalio's work as “mind-blowing” and admits to feeling sometimes that “he has a bigger staff, and produces more relevant statistics and analyses, than the Federal Reserve.”
    2. “The economy is like a machine.” This machine may look complex but is, he insists, relatively simple even if it is “not well understood”. Mr Dalio models the macroeconomy from the bottom up, by focusing on the individual transactions that are the machine's moving parts. Conventional economics does not pay enough attention to the individual components of supply and, above all, demand, he says. To understand demand properly, you must know whether it is funded by the buyers' own money or by credit from others.
    3. In the early 1980s Mr Dalio started writing down rules that would guide his investing. He would later amend these rules depending on how well they predicted what actually happened. The process is now computerised, so that combinations of scores of decision-rules are applied to the 100 or so liquid-asset classes in which Bridgewater invests. These rules led him to hold both government bonds and gold last year, for example, because the deleveraging process was at a point where, unusually, those two assets would rise at the same time. He was right.
    1. Even if the original webpage disappears, you can often use this informationto locate an archived version using the Wayback Machine, a project of theInternet Archive that preserves a record of websites: https://archive.org/web/.

      It would be useful to suggest here:

      Ideally one's note taking applications would automatically archive web pages to the Internet Archive as you take notes from them. This means that if they should disappear in the future, you'd have recourse to a useful and workable back up.

  37. May 2022
    1. Machine Tags

      A new kind of tags — machine tags — are supported now. A machine tag, e.g. meta:language=python consists of a namespace (meta), a key (language) and a value (python). Everyone can created machine tags, but the meta: namespace is protected and tags in there will be created by the site itself.

      The codesite itself uses machine tags to make various properties of recipes accessible to the search:

      • meta:language

        The programming language of the recipe, e.g. python, perl or tcl.

      • meta:min_$lang_$majorver

        Those tags describe the minimum language version. If a recipe requires Python 2.5 it would have the tag meta:min_python_2=5.

      • meta:license

        The license that was selected by the author, e.g. psf, mit or gpl.

      • meta:loc

        This tag contains a number describing the lines of code in a recipes. It counts only the number of lines in the code block but not any lines in the discussion of in comments. This makes it possible to search for short recipes with less than ten lines or very large ones.

      • meta:score

        The current score of the recipe. This is the same number that is displayed besides the recipe title and can only be influenced by voting on recipes. That way you could even search for down-voted recipes

      • meta:requires

        Stores information about additional requirements of the recipes, e.g. required python modules. You can find recipes using python's collections module that way.

      All those tags cannot be changed directly because they are generated from a recipe's properties.

  38. Apr 2022
    1. ReconfigBehSci. (2021, July 19). this is how the failure to understand what efficacy means and how it relates to outcomes will be seized on over and over again. Cookie cutter fallacies require cookie cutter clarification by machine tools to be combatted effectively (at least at current levels of moderation) [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1417164191664730112

  39. Mar 2022
    1. So my idea was to create a machine-tag format based on Wikipedia topics, allowing any content creator to tag content with any topic in Wikipedia. By using Wikipedia as an index, this format provides very specific identification of content across a vast knowledge domain. Call it the Dewey Decimal System for the web: “The Wiki Decimal System.” In general, the problem with machine tags is how to make them easy to add for regular folks. Although the format itself is simple, the tags are typically lengthy and require you to know the data ID for what you want to tag. Enter my hack: A web page that takes your text and builds the list of Wikipedia machine tags automatically.
  40. Feb 2022
  41. Jan 2022
      • astro:name=NGC 4565
      • astro:orientation=11.73
      • astro:RA=189.083922302

      The metadata is structured. So structured that we can represent the example machine tags in a table:

      <table> <thead><tr> <th style="text-align:center">namespace</th> <th style="text-align:center">predicate</th> <th style="text-align:center">value</th> </tr> </thead> <tbody> <tr> <td style="text-align:center">astro</td> <td style="text-align:center">name</td> <td style="text-align:center">NGC 4565</td> </tr> <tr> <td style="text-align:center">astro</td> <td style="text-align:center">orientation</td> <td style="text-align:center">11.73</td> </tr> <tr> <td style="text-align:center">astro</td> <td style="text-align:center">RA</td> <td style="text-align:center">189.083922302</td> </tr> </tbody> </table>

      Or in a tree:

        astro
        |-- name
        |   `-- NGC 4565
        |-- orientation
        |   `-- 11.73
        `-- RA
            `-- 189.083922302
      
    1. Formats for Disk Images Another piece of the packaging puzzle is disk image formats. There are many. Each has its own benefits and detriments, but I’m not going to get into those here. Again, this is nowhere near a comprehensive list — just something to help with getting your bearings. I’d like to comment on a couple of the formats that I’ve recently encountered. VDI – VirtualBox’s internal default disk image format is VDI. Nevertheless, this is not what is used by Vagrant boxes. VMDK – One of the most common formats. VMWare’s products use various versions and variations of VMDK disk images. Several versions and variations exist, so it’s very important to understand which one you’re working with and where it can be used.
    2. Open Virtual Appliance (OVA) An OVA is an OVF file packaged together with all of its supporting files (disk images, etc.). You can read about the requirements for a valid OVA package in the OVF specification. Oftentimes people will say “an OVF” and really mean “an OVA.”
    3. File Formats for Virtual Machines Open Virtualization Format (OVF) The OVF Specification provides a means of describing the properties of a virtual system. It is XML based and has generous allowances for extensibility (with corresponding tradeoffs in actual portability). Most commonly, an OVF file is used to describe a single virtual machine or virtual appliance. It can contain information about the format of a virtual disk image file as well as a description of the virtual hardware that should be emulated to run the OS or application contained on such a disk image.
    1. Virtual machines (VMs) revolutionized the data center. With the ability to easily spin up a machine and even roll back to a working state, VMs bring a level of ease IT would never have enjoyed. Rolling back your VM is handled by way of snapshots.

      File Formats for Virtual Machines Open Virtualization Format (OVF)

      The OVF Specification provides a means of describing the properties of a virtual system. It is XML based and has generous allowances for extensibility (with corresponding tradeoffs in actual portability). Most commonly, an OVF file is used to describe a single virtual machine or virtual appliance. It can contain information about the format of a virtual disk image file as well as a description of the virtual hardware that should be emulated to run the OS or application contained on such a disk image.

      Oracle VM VirtualBox can import and export virtual machines in the following formats:

      Open Virtualization Format (OVF). This is the industry-standard format. See Section 1.14.1, “About the OVF Format”.
      
      Cloud service formats. Export to and import from cloud services such as Oracle Cloud Infrastructure is supported. See Section 1.15, “Integrating with Oracle Cloud Infrastructure”. 
      

      1.14.1. About the OVF Format

      OVF is a cross-platform standard supported by many virtualization products which enables the creation of ready-made virtual machines that can then be imported into a hypervisor such as Oracle VM VirtualBox. Oracle VM VirtualBox makes OVF import and export easy to do, using the VirtualBox Manager window or the command-line interface.

      https://www.virtualbox.org/manual/UserManual.html#ovf

      Using OVF enables packaging of virtual appliances. These are disk images, together with configuration settings that can be distributed easily. This way one can offer complete ready-to-use software packages, including OSes with applications, that need no configuration or installation except for importing into Oracle VM VirtualBox.

    1. Here, the card index func-tions as a ‘thinking machine’,67 and becomes the best communication partner for learned men.68

      From a computer science perspective, isn't the index card functioning like an external memory, albeit one with somewhat pre-arranged linked paths? It's the movement through the machine's various paths that is doing the "thinking". Or the user's (active) choices that create the paths creates the impression of thinking.

      Perhaps it's the pre-arranged links where the thinking has already happened (based on "work" put into the system) and then traversing the paths gives the appearance of "new" thinking?

      How does this relate to other systems which can be thought of as thinking from a complexity perspective? Bacteria perhaps? Groups of cells acting in concert? Groups of people acting in concert? Cells seeing out food using random walks? etc?

      From this perspective, how can we break out the constituent parts of thought and thinking? Consciousness? With enough nodes and edges and choices of paths between them (or a "correct" subset of paths) could anything look like thinking or computing?

    1. We are definitely living in interesting times!

      The problem with Machine learning in my eyes seems to be the non-transparency in the field. After all what makes the data we are researching valuable. If he collect so much data why is only .5% being studied? There seems to be a lot missing and big opportunities here that aren't being used properly.

  42. Dec 2021
    1. as of February 2021, Europeana comprises 59%images and 38% text objects, but only 1% sound objects and 2% video objects.3 DPLA iscomposed of 25% images and 54% text, with only 0.3% sound objects, and 0.6% videoobjects.4Another reason, beyond cost, that audiovisual recordings are not widely accessible is the lack ofsufficiently granular metadata to support identification, discovery, and use, or to supportinformed rights determination and access control and permissions decisions on the part ofcollections staff and users.

      Despite concerted efforts, there is a minimal amount of A/V material in Europeana and DPLA. This report details a pilot project to use a variety of machine-generated-metadata mechanisms to augment the human description efforts. Although this paragraph mentions rights determination, it isn't clear from the problem statement whether the machine-generated description includes anything that will help with rights. I would expect that unclear rights—especially for moving image content—would be a significant barrier to the open publication of A/V material.

    1. This comparison is not to claim that the index catalog is already a Turing machine. Comparisons, transfers, and analogies are not that simple. If the elements of a universal discrete machine are present, they still lack the computational logic of an operating system, the development of which constitutes Turing ’ s foundational achievement. What is described here is merely the fact that the card catalog is liter-ally a paper machine, similar to a nontrivial Turing machine only in having similar components — no more, no less.

      I felt some of this missing piece and so included the idea of human interaction as part of the process to make up the balance.

    2. s Alan Turing proved only years later, these machines merely need (1) a (theoretically infi nite) partitioned paper tape, (2) a writing and reading head, and (3) an exact

      procedure for the writing and reading head to move over the paper segments. This book seeks to map the three basic logical components of every computer onto the card catalog as a “ paper machine,” analyzing its data processing and interfaces that may justify the claim, “Card catalogs can do anything!”

      Purpose of the book.

      A card catalog of index cards used by a human meets all the basic criteria of a Turing machine, or abstract computer, as defined by Alan Turing.

  43. Nov 2021
    1. In America, of course, we don’t have that kind of state coercion. There are currently no laws that shape what academics or journalists can say; there is no government censor, no ruling-party censor. But fear of the internet mob, the office mob, or the peer-group mob is producing some similar outcomes. How many American manuscripts now remain in desk drawers—or unwritten altogether—because their authors fear a similarly arbitrary judgment? How much intellectual life is now stifled because of fear of what a poorly worded comment would look like if taken out of context and spread on Twitter?

      Fear of cancel culture and social repercussions prevents people from speaking and communicating as they might otherwise.

      Compare this with the right to reach, particularly for those without editors, filtering, or having built a platform and understanding how to use it responsibly.

  44. Oct 2021
    1. ”My expectation is that we will hear many, many nice speeches, we will hear many pledges that - if you really look into the details - are more or less meaningless but they just say them in order to have something to say, in order for media to have something to report about," she said."And then I expect things to continue to remain the same. ... The COPs as they are now will not lead to anything unless there is big, massive pressure from the outside."

      Greta Thunberg on COP26

      In which Greta calls bullshit on the capitalist entropy machine’s attempts to spin the culture of learned helplessness, trained incapacities, and bureaucratic intransigence that is designed to maintain the status quo while pretending to be the world’s saviours through philanthropy, social entrepreneurship, and greenwashing.

      via Twitter

    1. Design for the Real World

      by Victor Papanek

      Papanek on the Bauhaus

      Many of the “sane design” or “design reform” movements of the time, such as those engendered by the writings and teachings of William Morris in England and Elbert Hubbard in the United States, were rooted in a sort of Luddite antimachine philosophy. By contrast Frank Llloyd Wright said as early as 1894 that “the machine is here to stay” and that the designer should “use this normal tool of civilization to best advantage instead of prostituting it as he has hitherto done in reproducing with murderous ubiquity forms born of other times and other conditions which it can only serve to destroy.” Yet designers of the last century were either perpetrators of voluptuous Victorian-Baroque or members of an artsy-craftsy clique who were dismayed by machine technology. The work of the Kunstgewerbeschule in Austria and the German Werkbund anticipated things to come, but it was not until Walter Gropius founded the German Bauhaus in 1919 that an uneasy marriage between art and machine was achieved.

      No design school in history had greater influence in shaping taste and design than the Bauhaus. It was the first school to consider design a vital part of the production process rather than “applied art” or “industrial arts.” It became the first international forum on design because it drew its faculty and students from all over the world, and its influence traveled as these people later founded design offices and schools in many countries. Almost every major design school in the United States today still uses the basic foundation course developed by the Bauhaus. It made good sense in 1919 to let a German 19-year-old experiment with drill press and circular saw, welding torch and lathe, so that he might “experience the interaction between tool and material.” Today the same method is an anachronism, for an American teenager has spent much of his life in a machine-dominated society (and cumulatively probably a great deal of time lying under various automobiles, souping them up). For a student whose American design school slavishly imitates teaching patterns developed by the Bauhaus, computer sciences and electronics and plastics technology and cybernetics and bionics simply do not exist. The courses the Bauhaus developed were excellent for their time and place (telesis), but American schools following this pattern in the eighties are perpetuating design infantilism.

      The Bauhaus was in a sense a nonadaptive mutation in design, for the genes contributing to its convergence characteristics were badly chosen. In boldface type, it announced its manifesto: “Architects, sculptors, painters, we must all turn to the crafts.… Let us create a new guild of craftsmen!” The heavy emphasis on interaction between crafts, art, and design turned out to be a blind alley. The inherent nihilism of the pictorial arts of the post-World War I period had little to contribute that would be useful to the average, or even to the discriminating, consumer. The paintings of Kandinsky, Klee, Feininger, et al., on the other hand, had no connection whatsoever with the anemic elegance some designers imposed on products.

      (Pages 30-31)

  45. Sep 2021
    1. a class of attacks that were enabled by Privacy Badger’s learning. Essentially, since Privacy Badger adapts its behavior based on the way that sites you visit behave, a dedicated attacker could manipulate the way Privacy Badger acts: what it blocks and what it allows. In theory, this can be used to identify users (a form of fingerprinting) or to extract some kinds of information from the pages they visit