liliumregale

Is it unfortunate, or just a different priority in a different place?

Those are also cities with a great public option for transit (unlike, say, Dallas or the Bay Area where different municipalities squabble). One might laud New York driving people away from car culture when the subways/buses can serve more people, greener.

That's a really unfortunate framing. The cat isn't out of the bag; otherwise, there wouldn't be efforts to scale up data centers to support forecasted demand.

The inevitability narrative is a self-fulfilling prophecy, and it ends if we decide not to keep telling it.

Could it be that the goal is not to be competitive, but to be responsible?

I'm sorry, this article reads like AI slop.

It has all the hallmarks: grandiose writing ("everything changed"), the classic "It wasn't X, it was Y" (about five times in the first minute of reading the article), undue emphasis on symbolism...

All those indicators are coupled with an unpleasant level of obsession with how statistical models are "a new kind of mind"—even after claiming to "strip away the hype cycle".

Regularization as a concept is taught in introductory ML classes. A simple example is called L2 regularization: you include in your loss function the sum of squares of the parameters (times some constant k). This causes the parameter values to compete between being good at modeling the training data and satisfying this constraint--which (hopefully!) reduces overfitting.

The specific regularization techniques that any one model is trained with may not be publicly revealed, but OAI hardly deserves credit for the concept.

We absolutely should treat it just like any other software tool.

Let's distinguish between papers and preprints, please. arXiv has contributed to a blurring of the distinction. The arXiv preprints are useful but should always be taken with a grain of salt. There is nearly no filtering done on things uploaded to arXiv.

Everyone accessing someone's uncritically reviewed work is a bittersweet gift.

This repo is a joke, right? I'd be embarrassed peddling this as AGI.

We're a long way from AGI existing at all. Even if you disagree, it's agreed upon that we're not there yet. For this repo to call its offering AGI is even more dramatic of a mischaracterization than saying I'm an Olympic sprinter because I went for a morning jog.

What can I say? Hucksters gonna huckster.

Google absolutely has their own internal models that do exactly this. It wouldn't surprise me if Microsoft indeed does have an internal Copilot that is trained on their data, but even on the smallest risk that they leak their code, they wouldn't share that particular model.

Yes - it's mentioned, but doesn't the framing below make it sound like they're still advocating for this paper?

> In essence, it's advisable to take the paper’s reported figures with a grain of salt, particularly as they cannot be precisely reproduced as described. Nonetheless, this approach continues to deliver unexpectedly well.

A "grain of salt" is different from "critical evaluation flaw," and if the reproduction's results are true, then the method doesn't after all "deliver unexpectedly well".