Forlorn tree on a submerged pier

Collective intelligence: A unifying concept for integrating biology across scales and substrates

Patrick McMillen & Michael Levin, Collective intelligence: A unifying concept for integrating biology across scales and substrates, Communications Biology, (2024) 7:378, https://doi.org/10.1038/s42003-024-06037-4

Abstract: A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

How is an LLM like a mechanical clock?

Let's think about LLMs a little more here, but first let's think about mechanical clocks.

A clock movement is an arrangement of gears and springs and levers that rotates 2 or more shafts are very specific speeds. That's it. An LLM is a lot like a mechanical clock movement.

1/

— amolitor.dolt (@amolitor99) April 25, 2024

The rest of the tweet stream:

A clock movement has no concept of "time" if you take the gears and levers apart, nowhere in there will you find any notion of what "time" is, nor, especially, the *correct* time of day. It just turns shafts, and even that function is not obvious to the non-expert.

If you attach some hands to the shafts, and position a "face" with numerals on it behind, and so forth, you can cause the combined machine to "tell the time" in the sense of making an assertion about what the time of day is.

The position of the hands over the face has a *meaning* for a suitably trained reader, in the same way that a sentence has *meaning*. If you speak the language, a sentence, as a 'signifier,' points to a 'signified,' the meaning of the sentence.

A book does not "know" anything, it does not "mean" anything by itself. If you, a language reader, read it, the meaning arises in that interaction.

In the same way, "it is 2:17" is a meaning which arises when you look at a clock face.

If the clock has not been wound, or if the clock has been set to the wrong time, the statement "it is 2:17" will still have meaning, but that meaning will likely be false. It is 10:17, not 2:17.

The clock is not *lying* in any reasonable sense. It itself does not even carry the meaning of the signs it displays. The meaning arises in the interaction of you with the hands and face attached to the clock.

The gears and levers don't even have "2:17" encoded in them.

In the same way, an LLM has no meaning encoded in it, although it produces signs which in their interaction with you often have a great deal of meaning.

LLMs do not "lie" or "hallucinate" any more than a clock does. It's simply a very very complicated set of gears and lever which, if you wind it up, and if you set it to the correct time, can produce true statements like "it is 10:20" some of the time.

a set of signs on the screen are incapable of being true or false, hallucinations or reality. They're just arrangements of letters.

Only meanings are true or false, and there are no meanings in the LLM. 

It is true that you will find echoes of meaning inside the LLM. Objects arise in the data which resemble an Othello board in interesting ways, when you train an LLM to play Othello.

This is like the position of the shaft of a clock movement.

You can see artifacts in the clock's machinery which, if you know what's going on, you can map to the output statement about what time it is. At the same time, it's foolish to argue that the clock "knows" what time it is, and then to argue that "it's lying."

The clock just turns shafts. This is not changed by the fact that you can imagine the hands and face without actually putting them on, and thereby imaginatively replicate the ability of the clock to make temporal assertions.

Now, an electronic clock can extract the actual correct time from a number of over-the-air oracles (GPS etc) and in fact if you take one of these apart you *will* find a place in there which contains the actual time. Allegedly the "correct" time.

A sequence of ones and zeros in a memory location encodes the "correct" time in some sense, or at least is supposed to.

There is a meaning inside the thing, kinda! It can be wrong, now, and it can arguably lie! If the clock is damaged, say.

When you look at the display you see that it reads "2:17" but the correct time is now 10:27. The clock is making some mistake between the oracle it consults and the display is shows you. It is "lying" or "hallucinating" in some meaningful way.

There is a model of the world attached to the clock, which the clock consults. That model can be interpreted to "mean" that "the time is 10:27" and the display on the clock can be interpreted to "mean" that "the time is 2:17" and that mismatch is a falsehood.

LLMs have no world model. They are clock movements, just clock movements. Attach them to a screen and a keyboard, and you've given the clock hands and a face. You can now read meaning if you like, but to propose that the "meaning" is inside the LLM is simply false.

fin/

Then there's this:

Friday Fotos: Let's play in the park!

Take your kids to work day – to Mt. Kenya [Don't punish women scientists because they have kids]

Toby Kiers, A Simple Act of Defiance Can Improve Science for Women, NYTimes, April 26, 2024.

sci

They don’t tell you beforehand that it will be a choice between having a career in science or starting a family. But that’s the message I heard loud and clear 17 years ago, in my first job after completing my Ph.D. in evolutionary biology. During a routine departmental meeting, a senior academic announced that pregnant women were a financial drain on the department. I was sitting visibly pregnant in the front row. No one said anything.

She has another child. There are incidents: "It felt like an impossible choice: to be a bad scientist or a bad mother."

What to do:

Through an act of academic defiance: I bring my kids with me on my scientific expeditions. It’s a form of rebellion that is available to mothers not just in the sciences but also in other disciplines that require site visits and field work, such as architecture and journalism. [...]

It started for me as a simple necessity. When my son was just under 2 and my daughter not yet 4, I took them on an expedition to the base of Mount Kenya in Africa, to study how fungi help trees defend themselves against the elephants and giraffes who feed on them. My son was still nursing, and I didn’t want to stop working. My husband, a poet, came along to stay with them at base camp.

As time went on, I began to embrace the decision to bring my kids with me on my expeditions, not as an exigency of parenting but as a kind of feminist act.

It has worked out well:

I started tasting soils in the field — a technique I now use to notice subtle differences across ecosystems — only after seeing my kids eat dirt. Children have an uncanny ability to make local friends quickly; many of those new friends have led me to obscure terrain and hidden fungal oases that I otherwise would never have come across. And my kids’ naïve minds routinely force me to rethink old assumptions by asking questions that are simultaneously absurd and profound. Can you taste clouds? Do fungi dream? How loud are our footsteps underground?

And so it goes. More examples of how things worked out.

At its core, feminism is about having the power to choose. For female scientists, this means having the ability to bring children into the field — or the full support to leave them at home. The pressure is acute because, as research shows, women on scientific teams are significantly less likely than men to be credited with authorship. So for me, it is crucial to keep collecting data with my own hands.

There's much more at the link.

Lilacs

The Industrial Revolution started earlier than conventional wisdom would have us believe

Fred Lewsey, ‘Nation of makers’: Britain industrialised over a century earlier than history books claim, University of Cambridge, 5 April, 2024.

Britain was well on its way to an industrialised economy under the reign of the Stuarts in the 17th century – over 100 years before textbooks mark the start of the Industrial Revolution – according to the most detailed occupational history of a nation ever created.

Built from more than 160 million records and spanning over three centuries, the University of Cambridge’s Economies Past website uses census data, parish registers, probate records and more to track changes to the British labour force from the Elizabethan era to the eve of World War One.

The research shows that 17th century Britain saw a steep decline in agricultural peasantry, and a surge in people who manufactured goods: from local artisans like blacksmiths, shoemakers and wheelwrights, to an explosion in networks of home-based weavers producing cloth for wholesale.

Historians say the data suggests that Britain was emerging as the world’s first industrial powerhouse several generations before the mills and steam engines of the late 18th century – long credited as the birth of global industry and economic growth.

Distributed manufacture:

Yet in the England of 1700, half of all manufacturing employment was in the countryside. “In addition to village artisans, there were networks of weavers in rural areas who would work for merchants that supplied wool and sold the finished articles,” said Shaw-Taylor.

Industries of textiles, or metalworkers making nails and scythes, were shaped like “factories without machines spread out over hundreds of households” according to Shaw-Taylor – and increasingly produced goods for international markets.

In Gloucestershire, for example, expansions in textiles, footwear and metals saw the share of the male workforce in industry grew from a third (33%) to almost half (48%) over the 17th century.comm

While in Lancashire, the share of men in manufacturing work grew from 42% in 1660 to 61% in 1750, driven by a doubling of textile workers (from 15% to 30%). This all occurred prior to the Industrial Revolution.

Some networks evolved into workshops, and eventually the mills of Blake’s visions as industries migrated to the North of England, where coal was abundant and crops were harder to grow.

This meant that by the mid-18th century – considered the start of the Industrial Revolution – much of England’s South and East had actually lost its long-established industries, and even returned to farm labour, according to the research.

There's more at the link.

H/t Tyler Cowen.

POST BOP JAZZ STYLES (Was 1959 the most important year?)

 

From the YouTube page:

This video introduces a series of episodes focusing on the various styles of jazz that emerged in the 1950s following the bebop era. By almost any account, this was one of the most impactful decades in jazz history, one which foretold a future in which it would become increasingly difficult to define jazz music. You'll also learn about Blue Note and Prestige, two independent record labels that played an outsize role in documenting the music of the post-bop era.

Construction in the cove

Katja Grace: The first future and the best future [opportunity costs]

The post:

It seems to me worth trying to slow down AI development to steer successfully around the shoals of extinction and out to utopia.

But I was thinking lately: even if I didn’t think there was any chance of extinction risk, it might still be worth prioritizing a lot of care over moving at maximal speed. Because there are many different possible AI futures, and I think there’s a good chance that the initial direction affects the long term path, and different long term paths go to different places. The systems we build now will shape the next systems, and so forth. If the first human-level-ish AI is brain emulations, I expect a quite different sequence of events to if it is GPT-ish.

People genuinely pushing for AI speed over care (rather than just feeling impotent) apparently think there is negligible risk of bad outcomes, but also they are asking to take the first future to which there is a path. Yet possible futures are a large space, and arguably we are in a rare plateau where we could climb very different hills, and get to much better futures.

My comment: YES. 

At the moment the A.I. world is dominated by an almost magical believe in large language models. Yes, they are marvelous, a very powerful technology. By all means, let's understand and develop them. But they aren't the way, the truth and the light. They're just a very powerful and important technology. Heavy investment in them has an opportunity cost, less money to invest in other architectures and ideas. 

And I'm not just talking about software, chips, and infrastructure. I'm talking about education and training. It's not good to have a whole cohort of researchers and practitioners who know little or nothing beyond the current orthodoxy about machine learning and LLMs. That kind of mistake is very difficult to correct in the future. Why? Because correcting it means education and training. Who's going to do it if no one knows anything else? 

Moreover, in order to exploit LLMs effectively we need to understand how they work. Mechanistic interpretability is one approach. But: We're not doing enough of it. And by itself it won't do the job. People need to know more about language, linguistics, and cognition in order to understand what those models are doing.

Kids these days

Elon's Xanadu rumpus room

At last! ChatGPT does, shall we say, interesting imitations of “Kubla Khan”

As you may know, I have a long-term interest in Coleridge’s “Kubla Khan” and have written a lot of posts about it. If you scan through those posts you’ll find that some of the more recent one’s involve ChatGPT. Here’s my first attempt to have ChatGPT parody the poem:

How ChatGPT parodied “Kubla Khan” and pwned DJT45 at the same time

While the Chatster’s performance is not without interest, the parodies were not very good. A month and a half later I decided to give it another try:

Let’s give ChatGPT another crack at “Kubla Khan.”

Again, not without interest, but no cigar.

Meanwhile, I have managed to prompt ChatGPT into imitations of Wallace Stevens’ “Thirteen Ways of Looking at a Blackbird.” I did it by using the technique I’d used to investigate story-telling. I’d give it a story and then ask ChatGPT to use that as the basis for a new story where I specified that the new story have a different protagonist. ChatGPT could do whatever it wanted to do with the rest. That worked well for stories, and it worked will with Stevens as well. A month ago I asked ChatGPT to substitute “A.I.” for “blackbirds.” Just the other day I had it produce a poem about irises.

Perhaps it will work with “Kubla Khan.” I decided to restrict the exercise to the first 36 lines. First, I had it substitute “Elon Musk” for “Kubla Khan,” and then “FDR.” The results? Good poetry, no. Some of it is silly. But it’s more interesting than the earlier tries. Three cigars and a pop tart.

As always, I set my prompts in boldface and ChatGPT’s response in plain-face.

From Kubla Khan to Elon Musk

I’m going to give you the first 36 lines of “Kubla Khan.” I want you to base a new poem on that, using “Elon Musk” instead of “Kubla Khan.”

In Xanadu did Kubla Khan
A stately pleasure-dome decree:
Where Alph, the sacred river, ran
Through caverns meaureless to man
Down to a sunless sea.
    So twice five miles of fertile ground
With walls and towers were girdled round:
And there were gardens bright with sinuous rills,
Where blossomed many an incense-bearing tree;
And here were forests ancient as the hills,
Enfolding sunny spots of greenery.

But oh! that deep romantic chasm which slanted
Down the green hill athwart a cedarn cover!
A savage place! as holy and enchanted
As e'er beneath a waning moon was haunted
By woman wailing for her demon lover!
And from this chasm, with ceaseless turmoil seething
As if this earth in fast thick pants were breathing,
A mighty fountain momently was forced:
Amid whose swift half-intermitted burst
Huge fragements vaulted like rebounding hail,
Of chaffy grain beneath the thresher's flail:
And 'mid these dancing rocks at once and ever
It flung up momently the sacred river.
Five miles meandering with a mazy motion
Through wood and dale the sacred river ran,
Then reached the caverns endless to man,
And sank in tumult to a lifeless ocean:
And 'mid this tumult Kubla heard from far
Ancestral voices prophesying war!

    The shadow of the dome of pleasure
    Floated midway on the waves;
    Where was heard the mingled measure
    From the fountain and the caves.
It was a miracle of rare device,
A sunny pleasure-dome with caves of ice!

In Tesla's realm, where dreams take flight,
Elon Musk decreed with might:
A wondrous hub of innovation, grand,
Where streams of thought would gently land.

Through circuits vast and wires deep,
Electric currents would swiftly creep,
From silicon valleys to skies above,
A symphony of progress and love.

So twice ten miles of tech terrain,
With factories and launchpads, a futuristic reign.
And there were labs with glowing screens,
Where ideas blossomed, futuristic dreams.

And here were forests of silicon trees,
Enfolding secrets, buzzing with ease.

But oh! That deep, electric abyss,
Where AI whispers in binary bliss.
A digital place, both dark and bright,
Where minds explore, day and night.

And from this abyss, with ceaseless spark,
As if the universe ignites in the dark,
A mighty surge, endlessly unfurled,
Of innovation, changing the world.

Amidst these circuits, where electrons race,
Humanity's future finds its place.
And through this maze, Elon heard from far,
The call of progress, like a guiding star.

The shadow of the rocket's trail,
Hovered above, without fail.
Where was heard the rumble and roar,
Of engines burning, yearning for more.

It was a marvel of modern lore,
A technological empire to explore!

KK to FDR

Do you know who FDR is?

Yes, FDR refers to Franklin D. Roosevelt, the 32nd President of the United States. He served from 1933 to 1945, making him the only president to serve four terms. FDR is known for his leadership during the Great Depression and World War II. He implemented the New Deal programs to combat economic depression and instituted numerous reforms, including Social Security. His presidency had a profound impact on American politics and society.

Good. I’m going to give you the first 36 lines of “Kubla Khan.” I want you to base a new poem on that, using “Elon Musk” instead of “Kubla Khan.”

[The first 36 line of "Kubla Khan" go here, as above.]

In Xanadu did FDR stand
A leader mighty, bold and grand,
Where laws, like rivers, freely ran
Through chambers vast, a sacred plan
Down to a nation's sea.

So twice four terms of governance he found,
With policies and programs girdled round:
And there were acts bright with promise still,
Where blossomed dreams of a nation's will;
And here were reforms, ancient as the hills,
Enfolding hope in valleys and on hills.

But oh! that deep, tumultuous time which raged
Down the slopes of despair, by history engaged!
A turbulent era, fraught and enchanted,
As e'er beneath a waning moon was planted
By people yearning for a brighter day!
And from this time, with ceaseless change unfurled,
As if the nation in fast-paced swirls were hurled,
A mighty movement, momentous, forced:
Amid whose swift and ever-changing course
Great challenges arose like thunderous hail,
Of trials beneath the leader's tireless tale:
And 'mid these tumults, through trials and endeavor,
He navigated the nation ever.

Four terms, meandering with a mazy motion,
Through trials and triumphs, the leader's devotion,
Then reached the caverns, challenges to span,
And sank in tumult, faced by a world at war's plan:
And 'mid this tumult, FDR heard from far
Ancestral voices, echoing a call to mar!

The shadow of leadership, dome of governance,
Floated midway, amidst the expanse,
Where was heard the mingled measure
Of struggle and triumph, of loss and treasure.
It was a miracle of leadership, concise,
A steady hand guiding through storms and ice.

Pink! Pink! Pink! [Sakura]

Basketball GOAT: Jordan or James?

I've thinking about GOATs – Greatest Of All Time – in response to Tyler Cowen's book, GOAT: Who is the Greatest Economist of all Time and Why Does it Matter? I was looking over the NYTimes yesterday, as I a wont to do, and came across this article in The Athletic, their sports affiliate: Who is the NBA GOAT: LeBron or Jordan? For current players, longevity is narrowing the gap (by Sam Amick and Josh Robbins, Apr. 23, 2024). So I thought I'd quote some passages just to see how NBA players think about the issue:

While Michael Jordan won the “Greatest of All Time” category for the third consecutive time, his once-massive lead over LeBron James has shrunk significantly with every passing poll. This time around, James almost took the mantle. The data speaks loud and clear...

• 2019 (the first time The Athletic conducted the poll): Jordan earned 73 percent of the votes, with James second at 11.9 percent (a gap of 61.1 percentage points)
  
• 2023: Jordan earned 58.3 percent of the votes, with James second at 33 percent (a gap of 25.3 percent)
  
• 2024: Jordan earned 45.9 percent of the votes, with James second at 42.1 percent (a gap of just 3.8 percent)

But why has Jordan’s lead shrunk so much? We wanted to let the players themselves explain.

The consistent rationale among LeBron voters, both old and new, is that his longevity is the ultimate difference-maker between the two. He’ll be 40 years old on Dec. 30, yet is still great enough to be widely considered one of the best players in today’s game. While Jordan was epic in his 14- year career, from his 6-0 record in the NBA Finals to his five Most Valuable Player awards and his incredible two-way play, many players shared the view that James’ ability to remain elite for more than two decades puts him over the top.

Jordan, to review, retired twice (in 1993 and 1998) during his storied career and played 14 seasons in a 19-year span. When he was James’ age, in the last of his two forgettable seasons in Washington, he was putting up good numbers on a bad Wizards team that went 37-45 in both of his postseason-less campaigns. James, meanwhile, has saved some of his best work for last:

• He broke Kareem Abdul-Jabbar’s all-time scoring record on Feb. 7, 2023
• He became the first player to be named to a 20th All-Star team in February
• He was one of three players to average at least 25 points, eight assists and seven rebounds this season (the others were Nikola Jokić and Luka Dončić)

Out of respect for the GOAT incumbent, we’ll begin by highlighting this nuanced opinion from a Jordan voter who believes MJ’s influence on the entire sports world — not just basketball — is a deciding X-factor.

“The greatest ever is LeBron James, (but) the greatest of all time is Michael Jordan,” the player said. “The difference is stats. When you talk about impact, Michael Jordan. When you talk about stats and numbers, LeBron. Mike has the most impact, so that makes him the greatest ever in all aspects because he doesn’t just impact basketball. He impacts people who look up to him in tennis and football. But you won’t hear that about LeBron. ... LeBron changed the game, but more so how it’s played. Jordan changed how it’s viewed. And that’s a big difference.”

I find that last remark particularly interesting. One of Cowen's criteria for economic greatness was influencing the world of thinking beyond economics. Preferring Jordan because of his influence on sports in general is the same kind of consideration, no? 

There's more at the link.

Irises & ChatGPT@3QD

I’ve posted a new article at 3 Quarks Daily:

The Irises Are Blooming Early This Year

Yes, it IS about irises, and contains photos of irises, but it manages to work its way to ChatGPT, where I include another parody of Wallace Stevens’s famous “Thirteen Ways of Looking at a Blackbird.” Last month it became a poem about an A.I. Now it’s about irises.

Another Stevens imitation

Thirteen Ways
	By Wallace Stevens	By ChatGPT
I	Among twenty snowy mountains,    The only moving thing    Was the eye of the blackbird.	Among twenty blooming irises, The only stirring thing Was the flutter of a butterfly.
II	I was of three minds,    Like a tree    In which there are three blackbirds.	I was of three minds, Like a garden Where there are three irises.
III	The blackbird whirled in the autumn winds.    It was a small part of the pantomime.	The iris swayed in the spring breeze. It was a small part of the dance.
IV	A man and a woman    Are one.    A man and a woman and a blackbird    Are one.	A gardener and a sunbeam Are one. A gardener and a sunbeam and an iris Are one.
V	I do not know which to prefer,    The beauty of inflections    Or the beauty of innuendoes,    The blackbird whistling    Or just after.	I do not know which to admire more, The elegance of petals Or the subtlety of colors, The iris blooming Or just before.
VI	Icicles filled the long window    With barbaric glass.    The shadow of the blackbird    Crossed it, to and fro.    The mood    Traced in the shadow    An indecipherable cause.	Raindrops adorned the petals With delicate jewels. The silhouette of the iris Danced upon them, back and forth. The feeling Traced in the silhouette A mystery beyond words.
VII	O thin men of Haddam,    Why do you imagine golden birds?    Do you not see how the blackbird    Walks around the feet    Of the women about you?	O wise gardeners of old, Why do you dream of other flowers? Do you not see how the iris Swirls around the stems Of the flowers beside it?
VIII	I know noble accents    And lucid, inescapable rhythms;    But I know, too,    That the blackbird is involved    In what I know.	I know the language of flowers And the undeniable pulse of nature; But I know, too, That the iris is entwined In all that I comprehend.
IX	When the blackbird flew out of sight,    It marked the edge    Of one of many circles.	When the iris swayed out of view, It marked the boundary Of one of many moments.
X	At the sight of blackbirds    Flying in a green light,    Even the bawds of euphony    Would cry out sharply.	At the sight of irises Dancing in the sunlight, Even the cynics of beauty Would pause in wonder.
XI	He rode over Connecticut    In a glass coach.    Once, a fear pierced him,    In that he mistook    The shadow of his equipage    For blackbirds.	She strolled through the garden, Lost in thought. Once, a joy overcame her, As she mistook The scent of the blooms For irises.
XII	The river is moving.    The blackbird must be flying.	The breeze is stirring. The iris must be swaying.
XIII	It was evening all afternoon.    It was snowing    And it was going to snow.    The blackbird sat    In the cedar-limbs.	It was morning all evening. It was blossoming And it was going to blossom. The iris stood In the garden's embrace.

Meta: A word about process

Some of the articles I write for 3 Quarks are like pulling teeth. I’ll make notes, sketch outlines, draft sections, perhaps as a post here at New Savanna, and then assemble the pieces into the final article on the Saturday and Sunday before the article shows up on Monday. This is one of those pieces: Western Metaphysics is Imploding. Will We Raise a Phoenix from The Ashes? [Catalytic AI]. I liked it a lot. But friends tell me it left them a bit mystified.

Other pieces that come easy. This one for example: Old School: Torpor and Stupor at Johns Hopkins. That was a while ago, so the writing process is not clear in my mind. But I pretty sure it’s one of those pieces where I thought about it a bit, did a little web surfing (in that case, I had to get the photo and a link or two) and then just sat down and drafted it. No doubt I stepped away from the computer every now and then, but it was basically one work session. I wrote a draft Sunday morning and early afternoon, checked it over, and then upload it.

Those two pieces are quite different in kind. The Western Metaphysics piece developed a complex argument whereas Torpor and Stupor was narrative in kind. Complex arguments require a complex web of connections between the various pieces. That’s hard to do and requires you to flit back and forth making things fit and relate. Narratives have a simpler structure. Torpor and Stupor didn’t tell a single continuous story. Rather, it was organized as a set of vignettes, each of them a little narrative. There was no argument to speak of. Just a an overall flow.

This irises piece was closer to the come-easy kind than the pulling-teeth kind. I had some points to make, but I made them more though analogy and metaphor than explicit argument. It had three sections. ChatGPT’s Stevens imitation went in the middle. I prepared that on Friday evening and made a few notes. More notes on Saturday. But I didn’t start writing until Sunday morning, and then I drafted it from beginning to end over course of, say, two to three hours.

The most interesting thing about the process was the decisions I made on Sunday morning to query ChatGPT about the nature of blossoms. I didn’t need to do that as I already more or less knew the story. But in so doing I was able to introduce ChatGPT into the exposition and thus prepare the way for the poem. That opened the way for the concluding discussion about DNA, strings, and complexity.

I wonder how LLMs manage different kinds of discourse? That’s what’s in the back of my mind.