flâneur

Haze D

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on the atlas — 41

highlights — 220

  • Trust in business can no longer be based on visual signals of authenticity, only on proof of work.
    After Authenticity
  • eople co-create their identities with brands just as they do with religions, communities, and other other systems of meaning. This constructivist view is incompatible with popular forms of postmodern critique but it also opens up new critical opportunities.
    After Authenticity
  • As I struggled with these thoughts early on, I wanted to explore what it would mean for brands and subcultures to merge more fully, to offer a much deeper and richer meaning.
    Life After Lifestyle
  • the culture-grifting of brands, b
    Life After Lifestyle
  • his is why I always thought that a common popular line of critique, “people consume brands to form their identity”, was silly. Of course they do! People are always seeing and being seen, and in some ways, owning products does constitute one form of cultural participation.
    Life After Lifestyle
  • Now, “culture-washing” was the subject of Naomi Klein’s famous book, No Logo, which both captured and galvanized a zeitgeist of anti-corporate, anti-globalization activists in the 90s.
    Life After Lifestyle
  • Once the CPSE eats a category, it can be segmented a dozen, a hundred times, filling out every price point and segment
    Life After Lifestyle
  • everything has become vaguely aspirational, even sugar water.
    Life After Lifestyle
  • Seeing the cultural production service economy helps us understand why, for a decade, “now” has felt increasingly sophisticated but empty.
    Life After Lifestyle
  • The vibe economy, as Dena indicates, is an intermediary step between Lifestyle and what comes next.
    Life After Lifestyle
  • Dena Yago terms the “vibe economy,” i
    Life After Lifestyle
  • The cultural production service economy (CPSE) isn’t about a single company, nor is it a strategy. It is an entire arrangement of culture, production, and finance, it is the way things are right now.
    Life After Lifestyle
  • To be even more literal: cultural production has become a service industry for the supply chain.
    Life After Lifestyle
  • Stare long enough, and you begin to see the whole: an economy where culture is made in service of brands.
    Life After Lifestyle
  • Except for a couple concept stores, the vast majority of MrBeast Burgers are run out of a network of preexisting restaurants kitchens that use the Olo ordering and delivery software.
    Life After Lifestyle
  • A series of brands made entirely out of code, there is no there there.
    Life After Lifestyle
  • The “last mile” of this supply chain was the rapidly growing influencer economy.
    Life After Lifestyle
  • This brings us to our third important factor: supply chain.
    Life After Lifestyle
  • The cultural logic of the 2010s is best represented by the starter pack meme. In the starter pack meme, classes of people are identified through oblique subcultural references and products they are likely to consume.
    Life After Lifestyle
  • This also was an era of platforms.
    Life After Lifestyle
  • On reflection, he says, “I blamed capitalism and held the technology itself innocent.”
    Doug Rushkoff Is Ready to Renounce the Digital Revolution | WIRED
  • On reflection, he says, “I blamed capitalism and held the technology itself innocent.”
    Doug Rushkoff Is Ready to Renounce the Digital Revolution | WIRED
  • Chat is an essentially limited interaction
    Malleable software in the age of LLMs
  • The point is to imagine how a reasonable extrapolation from current AI might support new kinds of interactions with computers, and how we might apply this new technology to maximally empower end-users.
    Malleable software in the age of LLMs
  • Here’s why: I think it’s likely that soon all computer users will have the ability to develop small software tools from scratch, and to describe modifications they’d like made to software they’re already using.
    Malleable software in the age of LLMs
  • The "Act as..." Hack
    The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts
  • Clarity: Use clear and specific language that is easy for the ChatGPT to understand. Avoid using jargon or ambiguous language that could lead to confusion or misunderstandings. Conciseness: Be as concise as possible in your prompts, avoiding unnecessary words or tangents. This will help to ensure that the ChatGPT is able to generate a focused and relevant response. Relevance: Make sure that your prompts are relevant to the conversation and the needs of the user. Avoid introducing unrelated topics or tangents that can distract from the main focus of the conversation.
    The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts
  • Clarity: A clear and concise prompt will help to ensure that the ChatGPT understands the topic or task at hand and is able to generate an appropriate response. Avoid using overly complex or ambiguous language, and aim to be as specific as possible in your prompts. Focus: A well-defined prompt should have a clear purpose and focus, helping to guide the conversation and keep it on track. Avoid using overly broad or open-ended prompts, which can lead to disjointed or unfocused conversations. Relevance: Make sure that your prompts are relevant to the user and the conversation. Avoid introducing un…
    The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts
  • Why we use odds and not percentages Three out of four is of course the same as 75% (mathematicians prefer to use fractions like 0.75 instead of percentages). It has been found that people get confused and make mistakes more easily when dealing with fractions and percentages than with natural frequencies or odds. This is why we use natural frequencies and odds whenever convenient.
    Odds and probability - Elements of AI
  • , remember the key points from the above discussion: probability can be quantified (expressed as a number) and it can be right or wrong.
    Odds and probability - Elements of AI
  • Watch out for “suitcase words” Marvin Minsky, a cognitive scientist and one of the greatest pioneers in AI, coined the term suitcase word for terms that carry a whole bunch of different meanings that come along even if we intend only one of them. Using such terms increases the risk of misinterpretations such as the ones above.
    How should we define AI? - Elements of AI
  • When defining and talking about AI we have to be cautious as many of the words that we use can be quite misleading. Common examples are learning, understanding, and intelligence.
    How should we define AI? - Elements of AI
  • Autonomy The ability to perform tasks in complex environments without constant guidance by a user. Adaptivity The ability to improve performance by learning from experience.
    How should we define AI? - Elements of AI
  • minds (IBM’s famous Deep Blue prevailed in chess over Gary Kasparov, e.g.; and more recently, AI systems have prevailed in other games, e.g. Jeopardy! and Go, about which more will momentarily be said), minds have a (Cartesian) capacity for cultivating their expertise in virtually any sphere. (
    Artificial Intelligence (Stanford Encyclopedia of Philosophy)
  • For example, Descartes proposed TT (not the TT by name, of course) long before Turing was born.
    Artificial Intelligence (Stanford Encyclopedia of Philosophy)
  • one must confront the fact that Turing, and indeed many predecessors, did attempt to build intelligent artifacts.
    Artificial Intelligence (Stanford Encyclopedia of Philosophy)
  • Later, we shall discuss the role that TT has played, and indeed continues to play, in attempts to define AI.
    Artificial Intelligence (Stanford Encyclopedia of Philosophy)
  • both philosophical AI (AI pursued as and out of philosophy) and philosophy of AI are discussed, via examples of both. The entry ends with some de rigueur speculative commentary regarding the future of AI.
    Artificial Intelligence (Stanford Encyclopedia of Philosophy)
  • Such goals immediately ensure that AI is a discipline of considerable interest to many philosophers, and this has been confirmed (e.g.) by the energetic attempt, on the part of numerous philosophers, to show that these goals are in fact un/attainable.
    Artificial Intelligence (Stanford Encyclopedia of Philosophy)
  • There are also a smaller number of standalone Generative AI web apps, such as Jasper and Copy.ai for copywriting, Runway for video editing, and Mem for note taking.
    Generative AI: A Creative New World | Sequoia Capital US/Europe
  • As these applications get more user data, they can fine-tune their models to: 1) improve model quality/performance for their specific problem space and; 2) decrease model size/costs.
    Generative AI: A Creative New World | Sequoia Capital US/Europe
  • These are all exciting directions. We think generative AI stands to transform one industry after another, making all kinds of professionals more effective at work and delighting waves of consumers.
    Developer Tools 2.0 | Sequoia Capital US/Europe
  • GPT-3 has 175 billion parameters compared to GPT-2’s 1.5 billion, and was trained on 570 billion gigabytes of text, while GPT-2 was trained on 40.
    GPT-3: An AI Breakthrough, but not Coming for Your Job – Skynet Today
  • The paper’s core message however was less about its performance on benchmarks, and more about the discovery that due to its scale GPT-3 is capable of solving NLP tasks that it has never before encountered after seeing just one or a few examples of the task (‘few-shot’ learning).
    GPT-3: An AI Breakthrough, but not Coming for Your Job – Skynet Today
  • Or, will most of these companies switch to using others’ LLM infrastructure, similar to the cloud providers dynamic today?
    Overview & Applications of Large Language Models (LLMs)
  • LLM applications that don’t own the model themselves is the long-term outcome of LLM infrastructure
    Overview & Applications of Large Language Models (LLMs)
  • Product requirements documentation (PRD) generation Monterey is building “co-pilot for product development,” perhaps involving LLMs at some point. From my time as a PM, a bunch of documentation I wrote could have probably been auto-generated from code or other information.
    Overview & Applications of Large Language Models (LLMs)
  • (As a side note, companies like Anthropic AI are working to make LLMs more reliable & understandable.)
    Overview & Applications of Large Language Models (LLMs)
  • GPT-3 proves the feasibility (& gives an idea of cost) of other copywriting generation startups, but necessitates a more competitive market.
    Overview & Applications of Large Language Models (LLMs)
  • It’s important to note that the competitive advantage isn’t just the private data used to train the model initially, but the additional data you get when customers interact with the model, telling it what is right, wrong, and sometimes what the answer should be instead.
    Overview & Applications of Large Language Models (LLMs)