flâneur

Lily Yang

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on the atlas — 86

highlights — 274

  • 25 ThemeSystem Theme 24 Sven-Harrys Konstmuseum Website 23 Polars Website 22 Independent Designers Collective Studio website 21 Nordic Behaviour Group Website 20 Re-Store Digital product 19
    Harry Atkins — Independent Developer
  • "method of loci" (often known as the "memory palace" method) that uses familiar routes and locations to help memorize sequential information. T
    Overview ‹ Customizing Mnemonics with AI — MIT Media Lab
  • "method of loci" (often known as the "memory palace" method) that uses familiar routes and locations to help memorize sequential information.
    Overview ‹ Customizing Mnemonics with AI — MIT Media Lab
  • To help people encode more into long-term memory, we can draw on another powerful idea from cognitive science: spaced repetition. By re-testing yourself on material you’ve learned over expanding intervals, you can cheaply and reliably commit huge volumes of information to long-term memoryFor a review of this effect’s practical implications, see Michael Nielsen’s “Augmenting Long-term Memory”. For more on the empirical evidence, this review by David Balota and colleagues is a good starting point.. Of course, memory is only a small slice of “understanding,” but to illustrate how one might begin …
    Why books don't work
  • So maybe part of “what’s necessary to understand” something is that most of its prerequisites must be not just familiar but fluent, encoded in long-term memory.
    Why books don't work
  • How might we design a medium so that its “grain” bends in line with how people think and learn? So that by simply engaging with an author’s work in the medium—engaging in the obvious fashion; engaging in this medium’s equivalent of books’ “read all the words on the first page, then repeat with the next, and so on”—one would automatically do what’s necessary to understand? So that, in some deep way, the default actions and patterns of thought when engaging with this medium are the same thing as “what’s necessary to understand”?
    Why books don't work
  • What’s more: it is possible to design new mediums which embody specific ideas.
    Why books don't work
  • For example, textbook exercises are often designed to yield both a solution to that specific problem and also broader insights about the subject. Will readers notice if they solved a problem but missed the insights it was supposed to reveal?
    Why books don't work
  • We saw earlier how non-fiction books’ accidental cognitive model left readers doing all the metacognitive work to plan, execute, and monitor their engagement with the book’s ideas. By contrast, textbooks do have explicit cognitive models: they support engagement with their concepts through things like exercises and discussion questions. Yet much of the metacognitive burden still remains with the reader.
    Why books don't work
  • textbooks usually are built around explicit cognitive models. For instance, they often alternate between explanations which introduce concepts, and exercises which push students to think about those concepts in specific ways.
    Why books don't work
  • These skills fall into a bucket which learning science calls “metacognition.
    Why books don't work
  • we might notice the implicit model they appear to share: “the lecturer says words describing an idea; the class hears the words and maybe scribbles in a notebook; then the class understands the idea.” In learning sciences, we call this model “transmissionism.”
    Why books don't work
  • If we collect enough of these underlying “truths,” some shared themes might emerge, suggesting a more coherent theory of how learning happens. We’ll call such theories cognitive models.
    Why books don't work
  • Books don’t work for the same reason that lectures don’t work: neither medium has any explicit theory of how people actually learn things, and as a result, both mediums accidentally (and mostly invisibly) evolved around a theory that’s plainly false.
    Why books don't work
  • All this suggests a peculiar conclusion: as a medium, books are surprisingly bad at conveying knowledge, and readers mostly don’t realize it.
    Why books don't work
  • It’s worth thinking hard about such objections. To develop the best possible memory system we need to understand and address the underlying concerns. In part, this means digging down far enough to identify the mistaken or superficial parts of these concerns. It also means distilling as sharply as possible the truth in the concerns. Doing both will help us improve and go beyond the current prototype mnemonic medium.
    How can we develop transformative tools for thought?
  • studying material in two different places, instead of twice in the same place, provided a 40% improvement in later recall. This is part of a broader pattern of experiments showing that varying the context of review promotes memory. We can use memory systems to support things like: changing the location of review; changing the time of day of review; changing the background sound, or lack thereof, while reviewing.
    How can we develop transformative tools for thought?
  • the psychologist Allan Paivio proposed the dual-coding theory, namely, the assertion that verbal and non-verbal information are stored separately in long-term memory. Paivio and others investigated the picture superiority effect, demonstrating that pictures and words together are often recalled substantially better than words alone. This suggests, for instance, that the question “Who was George Washington’s Vice President?” may have a higher recall rate if accompanied by a picture of Washington, or if the answer (John Adams) is accompanied by a picture of Adams.
    How can we develop transformative tools for thought?
  • One such idea is elaborative encoding. Roughly speaking, this is the idea that the richer the associations we have to a concept, the better we will remember it. As a consequence, we can improve our memory by enriching that network of associations.
    How can we develop transformative tools for thought?
  • But it may be more effective to follow up with questions designed to help the user understand some of the surrounding context. E.g.: “Who was George Washington’s Vice President?” (A: “John Adams”). Indeed, there could be a whole series of followup questions, all designed to help better encode the answer to the initial question in memory.
    How can we develop transformative tools for thought?
  • And so on, multiple different forms of the question, designed so the user must always engage deeply with the meaning of the question, not its superficial appearance. Ultimately, we’d like to develop a library of techniques for identifying when this learning-the-surface-feature pattern is occurring, and for remedying it.
    How can we develop transformative tools for thought?
  • In future, it’s worth digging deeper into this issue, both to understand it beyond informal models, and to explore ways of getting the benefits of active card making.
    How can we develop transformative tools for thought?
  • A possible explanation is that, as noted above, making good cards is a difficult skill to master, and so what users lose by not making their own cards is made up by using what are likely to be much higher-quality cards than they could have made on their own.
    How can we develop transformative tools for thought?
  • The reason seems to be that making the cards is itself an important act of understanding, and helps with committing material to memory. When users work with cards made by others, they lose those benefits.
    How can we develop transformative tools for thought?
  • But, with that said, we want to build as much support as possible into the medium. Ideally, even novices would benefit tremendously from the mnemonic medium. That means building in many ideas that go beyond the simplistic model of spaced repetition.
    How can we develop transformative tools for thought?
  • To our surprise, users performed poorly on these questions, worse than they did on the (much harder) later questions. Our current hypothesis to explain this is that when users failed to answer the first few questions correctly it served as a wakeup call. The questions were so transparently simple that they realized they hadn’t really been paying attention as they read, and so were subsequently more careful.
    How can we develop transformative tools for thought?
  • It seemed that the more atomic questions brought more sharply into focus what he was forgetting, and so provided a better tool for improving memory.
    How can we develop transformative tools for thought?
  • He entered the following question into his memory system: “How to create a soft link from linkname to filename”. Together with the corresponding answer “ln -s filename linkname”. This looks like a good question, but he routinely forgot the answer. To address this, he refactored the card into two more atomic cards. One card: “What’s the basic command and option to create a soft link?” (A: “ln -s”). Second card: “When creating a soft link, in what order do linkname and filename go?” (A: “filename linkname”). Breaking the card into more atomic pieces turned a question he routinely got wrong into …
    How can we develop transformative tools for thought?
  • question “how to write good cards?” requires thinking hard about your theory of knowledge and how to represent it, and your theory of learning.
    How can we develop transformative tools for thought?
  • What are new ways memory systems can be applied, beyond the simple, declarative knowledge of past systems? How deep can the understanding developed through a memory system be? What patterns will help users deepen their understanding as much as possible? How far can we raise the human capacity for memory? And with how much ease? What are the benefits and drawbacks? Might it be that one day most human beings will have a regular memory practice, as part of their everyday lives? Can we make it so memory becomes a choice; is it possible to in some sense solve the problem of memory?
    How can we develop transformative tools for thought?
  • But, more importantly, it’s possible because of the way the mnemonic medium embeds spaced repetition inside a narrative. That narrative embedding makes it possible for context and understanding to build in ways difficult in other memory systems.
    How can we develop transformative tools for thought?
  • One of the ideas motivating Quantum Country is that memory systems aren’t just useful for simple declarative knowledge, such as vocabulary words and lists of capitals. In fact, memory systems can be extraordinarily helpful for mastering abstract, conceptual knowledge, the kind of knowledge required to learn subjects such as quantum mechanics and quantum computing. This is achieved in part through many detailed strategies for constructing cards capable of encoding this kind of understanding.
    How can we develop transformative tools for thought?
  • Which brings us to our second problem: without a list of individual projects, you can’t connect your current efforts to your long-term goals.
    The PARA Method: The Simple System for Organizing Your Digital Life in Seconds
  • However, the actual pictures that were chosen, and especially their associated meanings, are what give us an amazing insight into Chinese history. The pictographic clues to that mysterious past have remained hidden in plain view for thousands of years.
    Genesis in Chinese Pictographs | The Institute for Creation Research
  • CS 160 — User Interface Design and Development CS 260B — Human-Computer Interaction Research INFO 217A — Human-Computer Interaction (HCI) Research NWMEDIA C203 — Critical Making NWMEDIA C265 — Interface Aesthetics INFO C262 — Theory and Practice of Tangible User Interfaces INFO C263 — Technologies for Creativity and Learning ART C166 — Critical Practices: People, Places, Participation
    Discovering Human-Computer Interaction at UC Berkeley | by Janaki Vivrekar | Medium
  • Among the excellent HCI faculty at UC Berkeley are Professors(/Lecturers) Eric Paulos, Björn Hartmann, Niloufar Salehi, Kimiko Ryokai, Marti Hearst, Ken Goldberg, Anca Dragan, Rediet Abebe, Michael Ball, Sarah Chasins and Aditya Parameswaran.
    Discovering Human-Computer Interaction at UC Berkeley | by Janaki Vivrekar | Medium
  • Synthesising goals from our research served as a lens through which we could consider not only what the app should do, but also how it should feel. We believed this would be the difference between delivering a good experience and a great one. Thinking about emotional design early on helped our client understand the importance of aesthetics and tone of voice to the experience.
    Bikes for London Case Study - User Experience Design Portfolio of Simon Pan
  • Keeping the scenarios at a high‐level allowed us to work fluidly and explore concepts that we could easily communicate with our team and client. They formed the backbone of our requirements, and allowed us to express these from both a functional and emotional perspective allowing for further empathy with our users.
    Bikes for London Case Study - User Experience Design Portfolio of Simon Pan
  • Knowing who exactly I was designing for allowed me to ask myself how the app fits into the lives of the users. I imagined ideal experiences and focused on how our personas think and behave rather than getting into specifics about interfaces, technologies or business goals.
    Bikes for London Case Study - User Experience Design Portfolio of Simon Pan
  • The class declaration public class HelloWorld: in Java, all code lives within classes.
    1.1 Basic Java Features - CS61B Textbook
  • Because all types are declared statically, the compiler checks that types are compatible before the program even runs. This means that expressions with an incompatible type will fail to compile instead of crashing the program at runtime.
    1.1 Basic Java Features - CS61B Textbook
  • branch_counts = [count_leaves(b) for b in branches(tree)]
    2.3 Sequences
  • assert is_tree(branch),
    2.3 Sequences
  • Dictionaries are Python's built-in data type for storing and manipulating correspondence relationships.
    2.4 Mutable Data
  • >>> pairs = [[1, 2], [2, 2], [2, 3], [4, 4]]
    2.3 Sequences
  • A linked list is a pair containing the first element of the sequence (in this case 1) and the rest of the sequence (in this case a representation of 2, 3, 4).
    2.3 Sequences
  • nested pairs is called a linked list
    2.3 Sequences
  • Sequences contain smaller sequences within them.
    2.3 Sequences
  • A list value is a sequence that can have arbitrary length.
    2.3 Sequences
  • String literals can express arbitrary text, surrounded by either single or double quotation marks.
    2.3 Sequences