290 lines
14 KiB
Plaintext
290 lines
14 KiB
Plaintext
[1]Home [2]About [3]Moonbound From: Robin Sloan
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To: the lab
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Sent: March 2023
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Phase change
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An extremely close-up photograph of a snowflake, looking almost architectural.
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[4]Snowflake, Wilson Bentley, ca. 1910
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Earlier this week, in [5]my main newsletter, I praised a new project from Matt
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Webb. Here, I want to come at it from a different angle.
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Briefly: Matt has built the [6]Braggoscope, a fun and useful application for
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exploring the archives of the beloved BBC radio show In Our Time, hosted by the
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inimitable Melvyn Bragg.
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In Our Time only provides HTML pages for each episode — there’s no structured
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data, no sense of “episode X is connected to episode Y because of shared
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feature Z”.
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As Matt explains [7]in his write-up, he fed the plain-language content of each
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episode page into the GPT-3 API, cleverly prompting it to extract basic
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metadata, along with a few subtler properties — including a Dewey
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Decimal number!?
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(Explaining how and why a person might prompt a language model is beyond the
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scope of this newsletter; you can [8]read up about it here.)
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Here’s [9]a bit of Matt’s prompt:
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Extract the description and a list of guests from the supplied episode notes from a podcast.
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Also provide a Dewey Decimal Classification code and label for the description
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Return valid JSON conforming to the following Typescript type definition:
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{
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"description": string,
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"guests": {"name": string, "affiliation": string | null}[]
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"dewey_decimal": {"code": string, "label": string},
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}
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Episode synopsis (Markdown):
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{notes}
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Valid JSON:
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Important to say: it doesn’t work perfectly. Matt reports that GPT-3 doesn’t
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always return valid JSON, and if you browse the Braggoscope, you’ll find plenty
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of questionable filing choices.
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And yet! What a technique. (Matt credits Noah Brier for [10]the insight.)
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It fits into a pattern I’ve noticed: while the buzzy application of the
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GPT-alikes is chat, the real workhorse might be text transformation.
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As Matt writes:
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Sure Google is all-in on AI in products, announcing chatbots to compete
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with ChatGPT, and synthesised text in the search engine. BUT.
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Using GPT-3 as a function call.
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Using GPT-3 as a universal coupling.
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It brings a lot within reach.
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I think the magnitude of this shift … I would say it’s on the order of the
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web from the mid 90s? There was a radical simplification and democratisa
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tion of software (architecture, development, deployment, use) that took
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decades to really unfold.
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For me, 2022 and 2023 have presented two thick strands of inquiry: the web and
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AI, AI and the web. This is evidenced by the structure of these lab
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newsletters, which have tended towards birfucation.
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Matt’s thinking is interesting to me because it brings the strands together.
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One of the pleasures of HTTP (the original version) is that it’s almost plain
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language, though a very simple kind. You can execute an HTTP request “by hand”:
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telnet www.google.com 80 followed by GET /.
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Language models as universal couplers begin to suggest protocols that really
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are plain language. What if the protocol of the GPT-alikes is just a bare TCP
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socket carrying free-form requests and instructions? What if the RSS feed of
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the future is simply my language model replying to yours when it asks, “What’s
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up with Robin lately?”
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I like this because I hate it; because it’s weird, and makes me
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feel uncomfortable.
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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I think it’s really challenging to find the appropriate stance towards
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this stuff.
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On one hand, I find critical deflation, of the kind you’ll hear from Ted
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Chiang, Simon Willison, and Claire Leibowicz in [11]this recent episode of KQED
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Forum, appropriate and useful. The hype is so powerful that any corrective
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is welcome.
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However! On the critical side, the evaluation of what’s before us isn’t
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sufficient; not even close. If we demand humility from AI engineers, then we
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ought to match it with imagination.
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An important fact about these language models — one that sets them apart from,
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say, the personal computer, or the iPhone — is that their capabilities have
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been surprising, often confounding, even to their creators.
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AI at this moment feels like a mash-up of programming and biology. The program
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ming part is obvious; the biology part becomes apparent when you see [12]AI
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engineers probing their own creations the way you might probe a mouse in a lab.
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The simple fact is: even at the highest levels of theory and practice, no one
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knows how these language models are doing what they’re doing.
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Over the past few years, in the evolution from GPT-2-alikes to GPT-3-alikes and
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beyond, it’s become clear that the “returns to scale”—both in terms of (1) a
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model’s size and (2) the scope of its training data — are exponential and
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nonlinear. Simply adding more works better, and works weirder, than it should.
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The nonlinearity is, to me, the most interesting part. As these models have
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grown, they have undergone widely observed “phase changes” in capability, just
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as sudden and surprising as water frozen or cream whipped.
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At the moment, my deepest engagement with a language model is in a channel on a
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Discord server, where our gallant host has set up a ChatGPT-powered bot and
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laced a simple personality into its prompt. The sociability has been a
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revelation — multiplayer ChatGPT is much, MUCH more fun than single player —
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and, of course, the conversation tends towards goading the bot, testing its
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boundaries, luring it into absurdities.
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The bot writes poems, sure, and song lyrics, and movie scenes.
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The bot also produces ASCII art, and SVG code, and [13]PICO-8 programs, though
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they don’t always run.
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I find myself deeply ambivalent, in the original sense of: thinking many things
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at once. I’m very aware of the bot’s limitations, but/and I find myself stunned
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by its fluency, its range.
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Listen: you can be a skeptic. In some ways, I am! But these phase changes have
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happened, and that probably means they will keep happening, and no one knows
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(the AI engineers least of all) what might suddenly become possible.
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As ever, [14]Jack Clark is my guide. He’s a journalist turned AI practioner,
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involved in policy and planning at the highest levels, first at OpenAI, now at
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Anthropic. And if he’s no longer a disinterested observer, he remains deeply
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grounded and moral, which makes me trust him when he says, with confidence:
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this is the biggest thing going, and we had all better brace for weird
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times ahead.
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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What does that mean, to brace for it?
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I’ve found it helpful, these past few years, to frame my anxieties and dissatis
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factions as questions. For example, fed up with the state of social media, [15]
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I asked: what do I want from the internet, anyway?
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It turns out I had an answer to that question.
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Where the GPT-alikes are concerned, a question that’s emerging for me is:
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What could I do with a universal function — a tool for turning just about any X
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into just about any Y with plain language instructions?
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I don’t pose that question with any sense of wide-eyed expectation; a reason
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able answer might be, nothing much. Not everything in the world depends on the
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transformation of symbols. But I think that IS the question, and I think it
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takes some legitimate work, some strenuous imagination, to push yourself to
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believe it really will be “just about any X” into “just about any Y”.
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I help operate [16]a small olive oil company, and I have spent a bit of time
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lately considering this question in the context of our business. What might a
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GPT-alike do for us? What might an even more capable system do?
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My answer, so far, is indeed: nothing much! It’s a physical business, after
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all, mainly concerned with moving and transforming matter. The “obvious” appli
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cation is customer support, which I handle myself, and which I am unwilling to
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cede to a computer or, indeed, anyone who isn’t me. The specific quality and
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character of our support is important.
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(As an aside: every customer support request I receive is a miniature puzzle,
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usually requiring deduction across several different systems. Many of these
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puzzles are challenging even to the general intelligence that is me; if it
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comes to pass that a GPT-alike can handle them without breaking a sweat, I will
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be very, very impressed.)
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(Of course, it’s not going to happen like that, is it? Long before GPT-alikes
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can solve the same problems Robin can, using the tools Robin has, the problems
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themselves will change to meet the GPT-alikes halfway. The systems will all
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learn to “speak GPT”, in some sense.)
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The simple act of asking and answering the question was clarifying and calming.
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It plucked AI out of the realm of abstract dread and plunked it down on
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the workbench.
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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Jack Clark includes, in all of his AI newsletters, a piece of original
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micro-fiction. One of them, [17]sent in December, has stayed with me. I’ll
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reproduce it here in full:
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Reality Authentication
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[The internet, 2034]
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“To login, spit into the bio-API”
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I took a sip of water and swirled it around my mouth a bit, then hawked
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some spit into the little cup on my desk, put its lid on, then flipped over
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the receptacle and plugged it into the bio-API system.
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“Authenticating … authentication successful, human-user identified. Enjoy
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your time on the application!”
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I spent a couple of hours logged-on, doing a mixture of work and pleasure.
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I was part of an all-human gaming league called the No-Centaurs; we came
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second in a mini tournament. I also talked to my therapist sans his
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augment, and I sent a few emails over the BioNet protocol.
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When I logged out, I went back to the regular internet. Since the AI models
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had got minituarized and proliferated a decade ago, the internet had
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radically changed. For one thing, it was so much faster now. It was also
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dangerous in ways it hadn’t been before - Attention Harvesters were every
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where and the only reason I was confident in my browsing was I’d paid for a
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few protection programs.
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I think “brace for it” might mean imagining human-only spaces, online and off.
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We might be headed, paradoxically, for a golden age of “get that robot out of
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my face”.
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In the extreme case, if AI doesn’t wreck the world, language models could
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certainly wreck the internet, like Jack’s Attention Harvesters above. Maybe
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we’ll look back at the Web Parenthesis, 1990-2030. It was weird and fun, though
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no one in the future will quite understand the appeal.
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We are living and thinking together in an interesting time. My recommendation
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is to avoid chasing the ball of AI around the field, always a step behind.
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Instead, set your stance a little wider and form a question that actually
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matters to you.
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It might be as simple as: is this kind of capability, extrapolated forward,
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useful to me and my work? If so, how?
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It might be as wacky as: what kind of protocol could I build around plain
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language, the totally sci-fi vision of computers just TALKING to each other?
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It might even be my original question, or a version of it: what do I want from
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the internet, anyway?
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From Oakland,
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Robin
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March 2023, Oakland
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I'm [18]Robin Sloan, a fiction writer. You can sign up for my lab newsletter:
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[19][ ] [20][Subscribe]
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This website doesn’t collect any information about you or your reading.
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It aspires to the speed and privacy of the printed page.
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Don’t miss [21]the colophon. Hony soyt qui mal pence
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References:
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[1] https://www.robinsloan.com/
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[2] https://www.robinsloan.com/about/
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[3] https://www.robinsloan.com/moonbound/
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[4] https://publicdomainreview.org/essay/the-snowflake-man-of-vermont?utm_source=Robin_Sloan_sent_me
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[5] https://www.robinsloan.com/newsletters/ring-got-good/?utm_source=Robin_Sloan_sent_me
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[6] https://genmon.github.io/braggoscope/?utm_source=Robin_Sloan_sent_me
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[7] https://interconnected.org/home/2023/02/07/braggoscope?utm_source=Robin_Sloan_sent_me
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[8] https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api?utm_source=Robin_Sloan_sent_me
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[9] https://news.ycombinator.com/item?id=35073824&utm_source=Robin_Sloan_sent_me
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[10] https://brxnd.substack.com/p/the-prompt-to-rule-all-prompts-brxnd?utm_source=Robin_Sloan_sent_me
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[11] https://www.kqed.org/forum/2010101892368/how-to-wrap-our-heads-around-these-new-shockingly-fluent-chatbots?utm_source=Robin_Sloan_sent_me
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[12] https://www.anthropic.com/index/toy-models-of-superposition-2?utm_source=Robin_Sloan_sent_me
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[13] https://www.lexaloffle.com/pico-8.php?utm_source=Robin_Sloan_sent_me
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[14] https://importai.substack.com/?utm_source=Robin_Sloan_sent_me
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[15] https://www.robinsloan.com/lab/specifying-spring-83/
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[16] https://fat.gold/?utm_source=Robin_Sloan_sent_me
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[17] https://us13.campaign-archive.com/?u=67bd06787e84d73db24fb0aa5&&id=a03ebcd500&utm_source=Robin_Sloan_sent_me
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[18] https://www.robinsloan.com/about?utm_source=Robin_Sloan_sent_me
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[21] https://www.robinsloan.com/colophon/
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