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   From: Robin Sloan
   To: the lab
   Sent: March 2023

                                  Phase change

   An extremely close-up photograph of a snowflake, looking almost
   architectural. [6]Snowflake, Wilson Bentley, ca. 1910

   Please excuse the off-schedule transmission. The idea of a steady,
   predictable publishing cadence is always so appealing … and then events
   overtake me!

   I think this lab newsletter will shift into a “whenever appropriate”
   schedule for the rest of this year. In this news flash, you’ll find two
   items: a time-sensitive link, and some thoughts on all that’s happening
   with AI.

   This is an archived edition of Robin’s lab newsletter. You can sign up
   to receive future editions using the form at the bottom of the page.

   A group of internet thinkers has proposed a [7]Summer of Protocols:

     an 18-week program that will run from May 1 to Aug 31, 2023, and
     aims to catalyze broad-based and wide-ranging exploration of the
     rapidly evolving world of protocols.

   [8]Applications are due in just a couple weeks, which is why I wanted
   to circulate the link immediately. I don’t know a ton about the program
   or its organizers, but I like the spirit captured on the website, and
   I feel like it might be a generative opportunity for someone(s)
   reading this.

   Even for those of us who aren’t going to participate, the introductory
   paper makes for a bracing, inspiring read: [9]The Unreasonable
   Sufficiency of Protocols.

   Now, on to the AI thoughts, which, as you’ll see, loop around to
   connect to protocols again:

[10]Finding a question

   Earlier this week, in [11]my main newsletter, I praised a new project
   from Matt Webb. Here, I want to come at it from a different angle.

   Briefly: Matt has built the [12]Braggoscope, a fun and useful
   application for exploring the archives of the beloved BBC radio show In
   Our Time, hosted by the inimitable Melvyn Bragg.

   In Our Time only provides HTML pages for each episode — there’s no
   structured data, no sense of “episode X is connected to episode Y
   because of shared feature Z”.

   As Matt explains [13]in his write-up, he fed the plain-language content
   of each episode page into the GPT-3 API, cleverly prompting it to
   extract basic metadata, along with a few subtler properties — including
   a Dewey Decimal number!?

   (Explaining how and why a person might prompt a language model is
   beyond the scope of this newsletter; you can [14]read up about it
   here.)

   Here’s [15]a bit of Matt’s prompt:
Extract the description and a list of guests from the supplied episode notes fro
m a podcast.

Also provide a Dewey Decimal Classification code and label for the description

Return valid JSON conforming to the following Typescript type definition:

{
  "description": string,
  "guests": {"name": string, "affiliation": string | null}[]
  "dewey_decimal": {"code": string, "label": string},
}

Episode synopsis (Markdown):

{notes}

Valid JSON:

   Important to say: it doesn’t work perfectly. Matt reports that GPT-3
   doesn’t always return valid JSON, and if you browse the Braggoscope,
   you’ll find plenty of questionable filing choices.

   And yet! What a technique. (Matt credits Noah Brier for [16]the
   insight.)

   It fits into a pattern I’ve noticed: while the buzzy application of the
   GPT-alikes is chat, the real workhorse might be text transformation.

   As Matt writes:

     Sure Google is all-in on AI in products, announcing chatbots to
     compete with ChatGPT, and synthesised text in the search engine.
     BUT.

     Using GPT-3 as a function call.

     Using GPT-3 as a universal coupling.

     It brings a lot within reach.

     I think the magnitude of this shift … I would say it’s on the order
     of the web from the mid 90s? There was a radical simplification and
     democratisation of software (architecture, development, deployment,
     use) that took decades to really unfold.

   For me, 2022 and 2023 have presented two thick strands of inquiry: the
   web and AI, AI and the web. This is evidenced by the structure of these
   lab newsletters, which have tended towards birfucation.

   Matt’s thinking is interesting to me because it brings the
   strands together.

   One of the pleasures of HTTP (the original version) is that it’s almost
   plain language, though a very simple kind. You can execute an HTTP
   request “by hand”: telnet www.google.com 80 followed by GET /.

   Language models as universal couplers begin to suggest protocols that
   really are plain language. What if the protocol of the GPT-alikes is
   just a bare TCP socket carrying free-form requests and instructions?
   What if the RSS feed of the future is simply my language model replying
   to yours when it asks, “What’s up with Robin lately?”

   I like this because I hate it; because it’s weird, and makes me
   feel uncomfortable.
     __________________________________________________________________

   I think it’s really challenging to find the appropriate stance towards
   this stuff.

   On one hand, I find critical deflation, of the kind you’ll hear from
   Ted Chiang, Simon Willison, and Claire Leibowicz in [17]this recent
   episode of KQED Forum, appropriate and useful. The hype is so powerful
   that any corrective is welcome.

   However! On the critical side, the evaluation of what’s before us isn’t
   sufficient; not even close. If we demand humility from AI engineers,
   then we ought to match it with imagination.

   An important fact about these language models — one that sets them
   apart from, say, the personal computer, or the iPhone — is that their
   capabilities have been surprising, often confounding, even to
   their creators.

   AI at this moment feels like a mash-up of programming and biology. The
   programming part is obvious; the biology part becomes apparent when you
   see [18]AI engineers probing their own creations the way you might
   probe a mouse in a lab.

   The simple fact is: even at the highest levels of theory and practice,
   no one knows how these language models are doing what they’re doing.

   Over the past few years, in the evolution from GPT-2-alikes to
   GPT-3-alikes and beyond, it’s become clear that the “returns to
   scale”—both in terms of (1) a model’s size and (2) the scope of its
   training data — are exponential and nonlinear. Simply adding more works
   better, and works weirder, than it should.

   The nonlinearity is, to me, the most interesting part. As these models
   have grown, they have undergone widely observed “phase changes” in
   capability, just as sudden and surprising as water frozen or
   cream whipped.

   At the moment, my deepest engagement with a language model is in a
   channel on a Discord server, where our gallant host has set up a
   ChatGPT-powered bot and laced a simple personality into its prompt. The
   sociability has been a revelation — multiplayer ChatGPT is much, MUCH
   more fun than single player — and, of course, the conversation tends
   towards goading the bot, testing its boundaries, luring it
   into absurdities.

   The bot writes poems, sure, and song lyrics, and movie scenes.

   The bot also produces ASCII art, and SVG code, and [19]PICO-8 programs,
   though they don’t always run.

   I find myself deeply ambivalent, in the original sense of: thinking
   many things at once. I’m very aware of the bot’s limitations, but/and
   I find myself stunned by its fluency, its range.

   Listen: you can be a skeptic. In some ways, I am! But these phase
   changes have happened, and that probably means they will keep
   happening, and no one knows (the AI engineers least of all) what might
   suddenly become possible.

   As ever, [20]Jack Clark is my guide. He’s a journalist turned AI
   practioner, involved in policy and planning at the highest levels,
   first at OpenAI, now at Anthropic. And if he’s no longer a
   disinterested observer, he remains deeply grounded and moral, which
   makes me trust him when he says, with confidence: this is the biggest
   thing going, and we had all better brace for weird times ahead.
     __________________________________________________________________

   What does that mean, to brace for it?

   I’ve found it helpful, these past few years, to frame my anxieties and
   dissatisfactions as questions. For example, fed up with the state of
   social media, [21]I asked: what do I want from the internet, anyway?

   It turns out I had an answer to that question.

   Where the GPT-alikes are concerned, a question that’s emerging for
   me is:

   What could I do with a universal function — a tool for turning just
   about any X into just about any Y with plain language instructions?

   I don’t pose that question with any sense of wide-eyed expectation; a
   reasonable answer might be, eh, nothing much. Not everything in the
   world depends on the transformation of symbols. But I think that IS the
   question, and I think it takes some legitimate work, some strenuous
   imagination, to push yourself to believe it really will be “just about
   any X” into “just about any Y”.

   I help operate [22]a small olive oil company, and I have actually spent
   a fair amount of time lately considering this question in the context
   of our business. What might a GPT-alike do for us? What might an even
   more capable system do?

   My answer, so far, is indeed: eh, nothing much! It’s a physical
   business, after all, mainly concerned with moving and transforming
   matter. The “obvious” application is customer support, which I handle
   myself, and which I am unwilling to cede to a computer or, indeed,
   anyone who isn’t me. The specific quality and character of our support
   is important.

   (As an aside: every customer support request I receive is a miniature
   puzzle, usually requiring deduction across several different systems.
   Many of these puzzles are challenging even to the general intelligence
   that is me; if it comes to pass that a GPT-alike can handle them
   without breaking a sweat, I will be very, very impressed.)

   (Of course, it’s not going to happen like that, is it? Long before
   GPT-alikes can solve the same problems Robin can, using the tools Robin
   has, the problems themselves will change to meet the GPT-alikes
   halfway. The systems will all learn to “speak GPT”, in some sense.)

   The simple act of asking and answering the question was clarifying and
   calming. It plucked AI out of the realm of abstract dread and plunked
   it down on the workbench.
     __________________________________________________________________

   Jack Clark includes, in all of his AI newsletters, a piece of original
   micro-fiction. One of them, [23]sent in December, has stayed with me.
   I’ll reproduce it here in full:

     Reality Authentication

     [The internet, 2034]

     “To login, spit into the bio-API”

     I took a sip of water and swirled it around my mouth a bit, then
     hawked some spit into the little cup on my desk, put its lid on,
     then flipped over the receptacle and plugged it into the
     bio-API system.

     “Authenticating … authentication successful, human-user identified.
     Enjoy your time on the application!”

     I spent a couple of hours logged-on, doing a mixture of work and
     pleasure. I was part of an all-human gaming league called the
     No-Centaurs; we came second in a mini tournament. I also talked to
     my therapist sans his augment, and I sent a few emails over the
     BioNet protocol.

     When I logged out, I went back to the regular internet. Since the AI
     models had got minituarized and proliferated a decade ago, the
     internet had radically changed. For one thing, it was so much faster
     now. It was also dangerous in ways it hadn’t been before - Attention
     Harvesters were everywhere and the only reason I was confident in my
     browsing was I’d paid for a few protection programs.

   I think “brace for it” might mean imagining human-only spaces, online
   and off. We might be headed, paradoxically, for a golden age of “get
   that robot out of my face”.

   In the extreme case, if AI doesn’t wreck the world, language models
   could certainly wreck the internet, like Jack’s Attention Harvesters
   above. Maybe we’ll look back at the Web Parenthesis, 1990-2030. It was
   weird and fun, though no one in the future will quite understand
   the appeal.

   We are living and thinking together in an interesting time. My
   recommendation is to avoid chasing the ball of AI around the field,
   always a step behind. Instead, set your stance a little wider and form
   a question that actually matters to you.

   It might be as simple as: is this kind of capability, extrapolated
   forward, useful to me and my work? If so, how?

   It might be as wacky as: what kind of protocol could I build around
   plain language, the totally sci-fi vision of computers just TALKING to
   each other?

   It might even be my original question, or a version of it: what do
   I want from the internet, anyway?

   From Oakland,

   Robin

   March 2023, Oakland

   I'm [24]Robin Sloan, a fiction writer. You can sign up for my
   lab newsletter:
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   This website doesn’t collect any information about you or your reading.
   It aspires to the speed and privacy of the printed page.

   Don’t miss [25]the colophon. Hony soyt qui mal pence

References

   1. file:///confirm/main/subscribe/
   2. https://www.robinsloan.com/feed.xml
   3. file:///
   4. file:///about
   5. https://ooo.ghostbows.ooo/?utm_source=Robin_Sloan_sent_me
   6. https://publicdomainreview.org/essay/the-snowflake-man-of-vermont?utm_source=Robin_Sloan_sent_me
   7. https://efdn.notion.site/Summer-of-Protocols-3d7983d922184c4eb72749e9cb60d076?utm_source=Robin_Sloan_sent_me
   8. https://efdn.notion.site/Application-5b71c238d6bd44cf9137946ef7767e53?utm_source=Robin_Sloan_sent_me
   9. https://efdn.notion.site/Pilot-Study-1bf3e3be6bf34a2eb8156ddf98d3fa67?utm_source=Robin_Sloan_sent_me
  10. file:///var/folders/q9/qlz2w5251kzdfgn0np7z2s4c0000gn/T/L12614-3163TMP.html#gpt
  11. https://www.robinsloan.com/newsletters/ring-got-good/?utm_source=Robin_Sloan_sent_me
  12. https://genmon.github.io/braggoscope/?utm_source=Robin_Sloan_sent_me
  13. https://interconnected.org/home/2023/02/07/braggoscope?utm_source=Robin_Sloan_sent_me
  14. https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api?utm_source=Robin_Sloan_sent_me
  15. https://news.ycombinator.com/item?id=35073824&utm_source=Robin_Sloan_sent_me
  16. https://brxnd.substack.com/p/the-prompt-to-rule-all-prompts-brxnd?utm_source=Robin_Sloan_sent_me
  17. https://www.kqed.org/forum/2010101892368/how-to-wrap-our-heads-around-these-new-shockingly-fluent-chatbots?utm_source=Robin_Sloan_sent_me
  18. https://www.anthropic.com/index/toy-models-of-superposition-2?utm_source=Robin_Sloan_sent_me
  19. https://www.lexaloffle.com/pico-8.php?utm_source=Robin_Sloan_sent_me
  20. https://importai.substack.com/?utm_source=Robin_Sloan_sent_me
  21. file:///lab/specifying-spring-83/
  22. https://fat.gold/?utm_source=Robin_Sloan_sent_me
  23. https://us13.campaign-archive.com/?u=67bd06787e84d73db24fb0aa5&&id=a03ebcd500&utm_source=Robin_Sloan_sent_me
  24. https://www.robinsloan.com/about?utm_source=Robin_Sloan_sent_me
  25. file:///colophon/
