I tried to build a procedural 3d asset pipeline for a specific use case.
Before the Opus upgrade in November it was basically no way of doing this. I gave up very quickly.
After November i tried again, and no model could build me anything relevant.
Now it just works. Took me an hour to progress to a point were i'm happy.
Whatever they do, progress is still real, still way faster than I assumed
The list of Ubuntus 2404 LTS CVEs is HUGE. Another indicator that a lot of stuff got a lot better fast.
Feel free to be as dismissive as you want, but if you are not careful, you might be 'suddenly' surprised and you might not be prepared for the conclusion of AGI level agents.
Only if you interpret statements as being binary logic.
"seem to be" carries semantic meaning here: I'm stating my interpretation of the situation based on data we have available (which is limited) and my prior.
Put another way: "We can't say that for sure, but my money is on it not being a simple case of intellectual property theft"
Imagine if they broke it down to each distinct source, that'd be several billion cases of copyright infringement (though it's going to be determined by what courts think and that often comes down to "who can afford the best lawyers" in practice if not intent).
Apparently if I use lib-gen, that's copyright infringement and I'm exposed to legal risk but it seems fine to download all of it if your intent is "train an AI" so far.
Other people are allowed to have their priors, too. Even under a non-informative prior, the weight of the evidence (Tristan's account, OpenAI's announcement, and Bubeck's "denial", if you want to call it that, plus multiple other mathematicians coming forward with similar experiences) pushes the probability mass toward some degree of impropriety.
What exact evidence are you incorporating into your prior to come out with this posterior?
We are giving you an opportunity to correct yourself. You are instead trying to make your nonsensical statement make sense. Not only does the first part of your sentence literally contradict the second part:
> We don't have enough accurate knowledge to say [one way or the other], and it doesn't seem to be the case at all [based on our incomplete knowledge].
But it is in no way equivalent to this:
> We can't say that for sure, but my money is on it not being a simple case of intellectual property theft
Based on: my experience working on AI for 32 years, including a decade at Google including working on large-scale model training systems that used user data and complied with various user policies around data retention, along with a few decades working in science/tech making decisions around ambiguous data.
In short, I have a well-tuned intuition and a huge set of priors, and applied them to the limited knowledge we have about this situation.
The human mathematicians didn't solve the Navier-Stokes problem, they solved the Euler problem. And they were extensively using LLMs to drive the work, as described in the Buckmaster statement.
Any way you cut it, this is a major achievement for AI, besotted with human drama over whose prompt should be recognized by the history books.
I don't think we should assume a millenium puzzle has been solved, yet. Astra showed impressive capacity for cheating when it was faced with impossible cybersecurity challenges. It seems equally plausible at this stage that it's found a bug in Lean.
Xerox is incidentally a really good example, because precisely nobody ended up using the desktop experience Xerox made. They ended up using the desktop experience that Microsoft and Apple made and shipped while Xerox the actual company faded and memory of those original parc research teams faded into obscurity.
Incentives are one thing, even adjusting for them it's huge, and I don't understand this incentive play for only openai, academics have perverse incentives too, to overreport, overclaim, publication bias etc why are we scrutinizing AI industry to such high degree when they have demonstrated capability and often times are off by a model release at worst.
As a statement of fact divorced from context, this is of course true, but it's worth putting it in context of what small-medium scale models have been achieving recently. Many of the most recent releases from Chinese labs are almost on par with trillion parameter models from less than a year ago (edit: despite being small enough to usably run on prosumer hardware). It seems clear parameter efficiency can still be improved dramatically.
In which case, maybe we don't need as much compute as we might expect. I hesitate to say "to reach a singularity" because it's kind of hard to define how that works out. Even intelligence probably hits some scaling limits eventually (e.g. speed of light related restrictions on how far it can scale, or how quickly it can expand).
And since LLMs are apparently good at circumventing the absence of an API, there's not much incentive to add them now. APIs are for humans. LLMs just break through all the captchas and anti-bot measures.
Yes, but for humans it creates friction. Seeing a captcha makes me think twice and thrice if I really want to visit that site so badly that I'll endure the suckage. LLMs don't care, for them the friction doesn't exist.
For sure. Anyone who thinks that we're in the end state of what progress can be made simply lacks imagination. This is all going to keep changing and iterating for the rest of our natural lives. The only constant is change.
I'm coming around to not liking the term singularity, it implies an endpoint or finish line rather than something that just keeps continuing and evolving.
> coming around to not liking the term singularity
Bit ironic given the model’s alleged finding…
Singularities are model breakdowns. A singularity simply says our current methods cease to work in this region. Within the context of a recursively self-improving intelligence with an unknown bound, “singularity” is probably a good description of our current socioeconomic system.
From the perspective of those who don't pass through the singularity to the other side, it is an endpoint. You would have no context or ability to understand a singularity transition. Really, the term is just a placeholder for "event we cannot comprehend due to limited intelligence".
Singularity and inflection point are incompatible mathematically and in the plain sense, it really is focused on a particular moment and always has been, hence the term.
And it's definitely supposed to imply some kind of historical discontinuity not a change in convexity.
Which assumes the presence of an inflection point that keeps inflecting rather than revert to an S-curve. The growth model is not borne out yet to declare what shape it is.
Certainly. The singularity sort of assumes that there is not a fixed limit to intelligence, or at least that if there is, it's quite a ways away. That may not be true.
I've done a lot of thinking about this since I first used ChatGPT to write some BS jinja2 templates hours after I first play with it. I said to my friend then (who scoffed at me) that "man, this is incredible, I think we're in the foothills of the singularity! This is insane! Sure it's stupid now but I can't believe this is even possible!" That friend is so black pilled and bitter he now hates AI. Whatever, I can't fix that, but the current progress is astounding.
But thinking about the geometry of this problem helps understand why people aren't adjusting well to this. While we're walking on the curve, we look at the rate of change of the curve and say, "well, yeah, of course, dy/dx is 5 at this point and was 1 at the point a few years ago, because the curve is getting steeper" but we're always going to feel this way as things rip off into the stratosphere because dy/dx(e^x) = e^x.
From standing on the curve the curve is notably seeper, but the steepness totally makes sense to you. It's only when you look back 10 years or so that you think "wait a second, holy hell, I couldn't have imagined this!"
The first time I really (I mean really) thought about the singularity and AI was in roughly 2014. I mean, yea, I'd thought about things before that, but yeah, before the Humans need not apply video, I'd never actually given it much thought. I think back to myself 10 - 15 years or so ago, when I was just starting to tackle real programming projects, and was just starting to get decent at writing code, there is no way on earth that I would have imagined that a little over a decade later, Navier-Stokes would be solved by a computer program and the vast majority of my work would be playing sooth sayer to increasingly complicated piles of linear algebra.
How do you automate the mines to get the raw materials to make the compute from, and build additional fabs that take a almost a decade to stand up. You're actually delusional.
The question of whether something can be automated is distinct from the question of whether it is currently automated. Things can can be automated may transition to being automated in practice in the future as technology improves and investment deepens.
Based on the leaps in local inference speed in the past month, which have been absurd, I'm p confident we're going to whiplash from compute constrained to storage constrained.
Bit apples to oranges, but it reminds me of all the fiber we installed in the late 90s, certain that per-strand capacity increases were years or decades out, only to get massively rugged
I expect the investments into AI driven mathematic discoveries that underpin compression efficiency will be a key investment area. Particularly at the data center scale rather than per device or per file level.
It's not going to be enough. The naive approach of a project I've been working on was pushing >10gbps over the local network, after a ton of work I got it back down under 1... and now it's processing so much more shit that I'm almost past 5 again! It compresses at >3:1 but the latency hit isn't suitable.
I get the impression the only reason there is renewed interest in photonics is because DCs are simply out of room (and power) to rack more servers and switches.
I 100% agree with your impression. For a good while to come there's going to be a bunch of Jevons Paradox to all of this, but adoption of architectural changes like that photonics adoption is exactly the type of adaption to circumvent bottlenecks I'm referring to. We're going to hit hundreds of bottlenecks and each one will inevitably breed new approaches and technology directions. And the forcing function won't be talking about them, but implementing them, seeing who wins and taking lessons.
Which is to say, scalable and open-ended capability of ramping up physical infrastructure.
I don't know that that's achievable yet. Though the era of increasingly advanced and automated robotics seems to be around the corner which could create a cycle, vicious or virtuous depending on how you feel about it.
[1] https://openai.com/index/research-acceleration-view-inside-o... [2] https://openai.com/index/an-alien-mind/