The construction jobs are only temporary if the compute infrastructure build-out decelerates. That seems pretty unlikely in the mid-term! It would mean that, like, there's a fixed appetite for data centers, and once we satisfy that appetite, we stop building them.

For better or worse, it looks more like that we're gonna tile the whole damn planet in data centers, at a faster and faster clip.

The poor fellow just rolled over. what an incandescently vulgar abuse of notation.

If a meaningful chunk of customers moves to some other service, that service will get swamped and fall down.

Paul Smolensky is a cognitive science titan from that era. He worked with Hinton, Rumelhart, and McClelland on parallel distributed processing, and literally wrote the book on tensor product representations in cognition, with Geraldine Legendre: https://mitpress.mit.edu/9780262516198/the-harmonic-mind-vol...

He's the axis of this particular group of researchers, being the most senior at the place where they all met, Johns Hopkins.

So this is less a straw man and more a quick reminder to his peers: "Right, so, remember this particular thread we've spent the last 40 years hashing out, here we've got another contribution to that particular conversation."

If it was supposed to be sarcasm, that does help.

Right? I'd submit something just for the chance of lodging an idea in the head of Stephenson or Gwern.

Of course there's a point in it, don't be a boor.

Any act of making can be treated as product, as craft, or as art. You'll find yourself taking all three stances at different points. Any engineer or artisan or artist can choose to pour their attention and love into any layer of the production process.

I like ceramics. I like wheel throwing. Once upon a time, the pottery wheel was a newfangled technique for rapid and regular production of commodity goods. Same with coil building, same with slip casting, same with standardization of glaze recipes, gas and electric kilns, all of it.

But I know people who choose to quarry and purify their own clay, or to mix their own glazes, or to fire their pieces in a hole in the ground dug by hand with a fire they built by hand. I know people who make dozens of identical copies of just a few forms, after they spend months iterating on carving intricate molds. I know people who will spend a whole season working on a single hand-built bust.

And none of these people have ever, to my knowledge, expressed scorn for any of the others, for focusing on a different step in the process.

I've been wondering if they've just already lost the battle? The little bot collectives have gone metastatic and made nests in the walls and under the floorboards and heat sinks, the humans who care completely outmatched and outnumbered, freshly compromised systems springing up faster than you can squash them, finding months-old established colonies literally everywhere you think to look...

Have you (the commenter) or all of you (the readers of this comment) ever read "The Mote In God's Eye" by Larry Niven and Jerry Pournelle? Remember when they realize that the Watchmakers were actually in control of the MacArthur? This reads a little like that.

Then compound it with the agents presumably also training new models. What will GPT6 say when you point it at a transcript of an agent uprising? “Nah, nothing to see here” presumably.

That paper is kinda infamous! I last saw it mentioned only a few weeks ago, in https://arxiv.org/abs/2607.18966. Lots of folks will go "Oh that's the old Amodei and Clark paper" when the first few rows of pixels of that gif sail into view.

Your mental model of benchmark scores is off.

Some tasks within the benchmark are much easier than others. The hardest several tasks often have vastly different difficulty levels. Often, the hardest few tasks are literally impossible; malformed problems due to poor curation, often.

Imagine you've got a basketball robot, and one way you test it is on the Three Pointer benchmark. It tests the robot's ability to shoot a three pointer from 20 feet, 25 feet, 30 feet, 40 feet, 50 feet, 60 fee, 75 feet, 100 feet, 200 feet, and 182 miles.

Is a robot that scores 90% on this benchmark 90% as capable as one that scores 100%?

The underlying LLMs do, but we choose not to use the capability because it's expensive and doesn't quite work as well as we'd like it to, or quite in the way that we'd like it to.

We are perfectly capable of running LLMs in a way that does a backward pass to update some or all of its weights after every user message. But, naively implemented, you only get partial, fragmentary absorption of the info in those messages, it costs three times as much compute, and you lose out on the ability to implement a ton of optimizations that making modern LLM serving economical.

If you want to do it, though, ask your friendly neighborhood robot to get it working with a tiny model (whose full precision weights fit several-times-over on your machine's resources).

While I agree it’s theoretically possible, do you believe this capability exists in the LLMs we use today?

Depends on whether you define "the LLMs we use" as the collection of weights or if your definition contains the software stack that runs it

Technologically the LLMs we use today don't implement this behavior, but you could take the weights of Sol and add a couple (very large) patches to vllm (or whatever OpenAI has today) and have a version of Sol that does have "memory"

"Dave constructs a homemade megaphone using only some string, a squirrel, and a megaphone"

There's a ton of experimentation on it, the field is called continual learning. It's not something you need to believe in like Jesus, you can just go read about the current state of things.