Noah Smith talking about what more intelligence will do for us. It starts off with this.

Not a lot of people expected that AI would come for the mathematicians before it came for the truck drivers, but it did.

Ha!

I see a lot of parallels here with Yuval Noah Harari’s quote from his book 21 Lessons for the 21st Century.

Many doctors focus almost exclusively on processing information: they absorb medical data, analyze it, and produce a diagnosis. Nurses, in contrast, need good motor and emotional skills in order to give a painful injection, replace a bandage, or restrain a violent patient. Therefore we will probably have an AI family doctor on our smartphone decades before we have a reliable nurse robot.

Noah quotes multiple people in his post to support his thought. One of them is from François Chollet.

One of the biggest misconceptions people have about intelligence is seeing it as some kind of unbounded scalar stat, like height. “Future AI will have 10,000 IQ”, that sort of thing. Intelligence is a conversion ratio, with an optimality bound. Increasing intelligence is not so much like “making the tower taller”, it’s more like “making the ball rounder”. At some point it’s already pretty damn spherical and any improvement is marginal.

Now of course smart humans aren’t quite at the optimal bound yet on an individual level, and machines will have many advantages besides intelligence — mostly the removal of biological bottlenecks: greater processing speed, unlimited working memory, unlimited memory with perfect recall… but these are mostly things humans can also access through externalized cognitive tools.

It’s a long and interesting read.

Dex Horthy:

The cost function of bad architecture is measured in months and years. If you have a coding episode and you only find out months later “somebody vibed this a little too hard” it’s really hard to propagate that reward signal back across the gap.

Sasha Putilin writes in the defense of em dash:

I fucking love em dashes. There is no greater joy in life than typing a sentence, realising it needs a clarification, a caveat, an addendum — but in the same sentence, not a new one — and, guess what, the em dash is right there for you. You want to package things neatly together — and the em dash lets you do exactly this. Em dashes make writing feel like you are assembling lego bricks.

Em dashes can be used instead of brackets in a sentence — like this one for example — and you can just type your shit and keep going. Brackets are for people who raise their hand in meetings and say: “this might be a stupid question”. A parenthetical is whispering: “Sorry, don’t mind me, I’ll be quick”. Em dash kicks the door open and announces itself. Use brackets when you are embarrassed by your own thought.

And also occasionally — occasionally — one just needs a random dramatic pause. Punctuation marks originally evolved as pause or breathing markers, after all. It’s not like grammar and punctuation rules were sent to us by god in their final form. People were just writing shit, and at some point the most popular patterns got codified. Sure, you need commas to signify a small break. Then em dash is a natural way to express a larger, longer, break. A period is great to express a complete thought — and it shifts the register of the next letter.

Matthew Morgan explaining why Bill Watterson despised the idea of merchandising Calvin and Hobbes.

It was still early days in the ten-year run of Calvin and Hobbes when the syndicate approached Watterson with its big ideas of Calvin sweatshirts, Spaceman Spiff bumper stickers, an animated Calvin and Hobbes Saturday show, maybe a movie, and — worst of all — a Hobbes doll. Watterson really loathed the Hobbes doll. To make sense of how much it bothered him, we need to talk about the tiger in the room.

When Watterson created Hobbes, his focus was on the character more than the conceit of a teddy that comes to life. Watterson told Rich West for The Comics Journalthat “there’s something a little peculiar about [Hobbes] that’s, hopefully, not readily categorised”. But Watterson’s readers often wanted Hobbes categorised into either “real” or “imaginary”. So Watterson came up with a compelling non-answer to the question:

“Calvin sees Hobbes one way, and everyone else sees Hobbes another way. I show two versions of reality, and each makes complete sense to the participant who sees it. I think that’s how life works.”

You could say Hobbes is both imaginatively real and really imaginary, depending on your perspective. Hobbes can be either, which also means he’s both. Is Hobbes a tiger or a toy? Yes.

Watterson insisted that if he wasn’t going to settle the question of Hobbes, then he definitely wouldn’t let some toy manufacturer settle it by turning Hobbes “into a stuffed toy for real, and deprive the strip of an element of its magic”. He’d sound off wherever he could on how “licensing usually cheapens the original creation” by saturating a market with characters until readers are bored of seeing them; how a multi-paneled story with dynamic action cannot be respected by the vagaries of a coffee mug illustration; how subtlety is sacrificed for immediacy; how selling off “everything fun and magical” means “the strip’s world is diminished”.

He has a hundred lines like these, articulations of higher reasoning against merchandising, but just once, in the Tenth Anniversary Book, he drops the high-and-mighty in favour of I-the-mighty: “Calvin and Hobbes was designed to be a comic strip and that’s all I want it to be. It’s the one place where everything works the way I intend it to.”

Tom Bedor sharing a few examples of how, in the past, suppression of encryption by the U.S government didn’t work. In the same vein, it won’t work with suppression of AI models from China.

Open source software is very difficult to suppress

In reality, the argument about suppressing open source models is mostly beside the point. History tells us that suppression of open source software is extremely difficult, and attempting to do so only serves to weaken companies against international competitors. A brief history of encryption is illustrative:

Today, PGP is a commonplace tool anyone can use, and most devs are at least familiar with. But when Phil Zimmermann invented it in 1991, the U.S. government considered encryption to be military technology. A criminal investigation was opened against Zimmermann.

When Netscape created SSL, the U.S. government allowed it to only release a weakened version of it internationally. These controls backfired: it was much easier to acquire the weakened, “international” version, so even many Americans used it.

Export controls did not succeed in limiting encryption as the government wished. SSL, PGP, and similar tools were readily available throughout the world, and the controls disadvantaged Americans. Eventually, courts ruled that releasing encryption source code is protected speech, and the U.S. government relaxed encryption export controls.

But are the models from China really open source? This comment on Hacker News by petcat:

This is not “open source” AI.

Photoshop source code + OSI license = open source

Photoshop binary = open weight

Photoshop SAAS web app = closed model like GPT, Opus/Fable etc.

There is nothing “open source” about the Chinese models in question. All they’re doing is allowing you to run their binary yourself instead of through their API.

Linus Torvalds:

AI is a tool, just like other tools we use. And it’s clearly a useful one.

It may not have been that “clearly” even just a year ago, but it’s no longer in question today.

There are other questions around AI (like what the economy of it will actually look like in the end), but “is it useful” is no longer one of those questions. Anybody who doubts that clearly hasn’t actually used it.

Yes, it can also be a somewhat painful tool, both for maintainer workloads and just from a “it keeps finding embarrassing bugs” standpoint.

But the solution is not to put your head in the sand and sing “La La La, I can’t hear you” at the top of your voice like some people seem to do.

The solution is to make sure those LLM tools help maintainers instead of just causing them pain. There’s no question on that side.

We’re not forcing anybody to use it, but I will very loudly ignore people who try to argue against other people from using it.

And no, AI isn’t perfect. But Christ, anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time.

Because it’s not like natural intelligence is always all that great either.

Ben Landau-Taylor talking about how we need more organisers to continue creating events for the consumption of social fabric.

Lots of people have a sort of consumer attitude towards their communities, where they take everything for granted. I saw things this way when I was young. A social scene is an automatic feature of the world that appears on its own, like a wild blueberry bush. It starts sprouting parties and dinners and conferences and reading groups as naturally as the bush sprouts berries.

Surprisingly, it turns out things don’t actually work that way. In fact, events happen when someone puts in the legwork to organize them. And one of the most reliable laws of the universe is that, if something takes a little bit of legwork, then most people just won’t do it. A scene’s leaders are mostly the people who actually bother to put in the work.

The work of organizing is underappreciated by many people. But the other organizers are very attuned to this, and absolutely will notice who else is picking up the burdens.

I’ve come to believe that part of today’s problem of social alienation is a problem of too many free riders. Lots of people want to consume social fabric, but our social scripts telling people to produce social fabric have largely fallen by the wayside. I don’t know how to solve this undersupply at the scale of society. But you can solve it at the scale of your own community by just supplying it.

Satya Nadella talks about how:

In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.

He then goes on to share suggestions on how to confront the paradox.

In the cloud era, enterprises accumulated data. In the AI era, they accumulate learning. The trust boundary must evolve accordingly, from protecting information to protecting the mechanisms through which organizations learn, adapt, and compound intelligence. There are a few things every enterprise must do to ensure this:

• Control: Create your private evals, because evals define what “good” looks like inside the organization. Also, retain ownership of your organization’s memory, traces, feedbacks, decisions, and institutional context, and ability to use outputs of models from your own tasks and queries.
• Capability: Build your own proprietary learning environments within the tenant boundary to train or tune models, where models learn against real workflows without exposing the company’s knowledge.
• Choice: Ensure the orchestration layer is decoupled from any single model. Ask yourself: If any one model you are using is taken away, do you still have the ability to operate and optimize for your evals using other models? Does your company “veteran” capability remain with you even if a given “generalist” model is taken away?
• Cost: By decoupling the orchestration layer, you are also able to bring together context, models, and tasks in the most efficient and cost-effective way without sacrificing quality.
• Compound: Bring these four together and you create your own continuous learning loop (i.e. hill climbing machine) that will allow your AI investments to compound the value of your firm.

In other words, a company should be able to use a model without giving up the knowledge that makes it unique. That is the reverse information paradox we need to confront.