This Changes Everything

I'm all in on AI now

(https://xoxohth.com/thread.php?thread_id=5913613&forum_id=2#50194735)

Reminds me of Peterman w his crypto shit but I guess in the end he was vindicated for a while

(https://xoxohth.com/thread.php?thread_id=5913613&forum_id=2#50194782)

A reporter who was present asked the Cuban, “How far do you see ahead?” Capa replied impressively, “About ten moves”. Then the reporter went over to Jaffe: “How far do you see ahead?” Much to everyone’s surprise, the reply was, “Only one move”. This didn’t make sense. “How could a player who can see only one move ahead defeat another who can delve so deeply?”

Here Jaffe explained: “I see only one move ahead, but it's always the best move.”

(https://xoxohth.com/thread.php?thread_id=5913613&forum_id=2#50194747)

Ur knowledge depth just isn't there

(https://xoxohth.com/thread.php?thread_id=5913613&forum_id=2#50194744)

It's an important question (even though it isn't precise or technical and that, like every other AI question, people can define it tons of different ways), because interpolation is perfectly sufficient to put us all out of work, and it's the chance of real innovation that's why we're willing to tolerate this tech.

But yeah I can't think of any other high-level reason to dwell on the "predict the next" angle, except that it makes the point to your audience on a subtextual level that it's only assembling existing knowledge.

(https://xoxohth.com/thread.php?thread_id=5913613&forum_id=2#50195755)

But interpolative vs. extrapolative is just a metaphor. Is a really awesome Second Circuit answer brief that's better than any lawyer in the world could put together "extrapolative"? It's certainly not in my definition, but whatever. The ultimate point here is "is this tech gonna give us crazy great shit that's unlike anything that humans would have been able to achieve otherwise?"

And I'm not saying the answer is no, but I am saying (1) if the answer were definitively believed to be no, you'd see a much stronger Butlerian Jihadist impulse in our politics, because ppl are mostly facilitating this tech's advancement in order to get wormhole-mediated travel to Edmunds' Planet at the crisp, energetic age of 168, not to become permanently unemployed by HAL 9000; and (2) the answer TO DATE is clearly no, chess grandmasterships and millennium prizes and other trinkets notwithstanding.

EDIT: To give your position some credit, the mathematicians I've read talk about this have said that the openAI solution to navier-stokes was extremely elegant and creative, not some kind of brute force thing. So that's cool.

(https://xoxohth.com/thread.php?thread_id=5913613&forum_id=2#50196247)

But the OECD 2030 report from earlier this year is still like "realistic outcomes vary from killing us all to transhumanist beings of pure light, or possibly things look the same as they do now." Having more confidence than that about what's going to happen is hubris.

EDIT: TO ME, the bearish AI arguments that are most persuasive (in ascending order) are (1) there being diminishing returns from increasing compute, which we're already seeing on a couple of levels (from compute-->benchmark performance, and then from benchmarks-->domain-specific performance); (2) it just running out of training data, and AI-created information trending either (a) interpolative to human data; or (b) being wrong or at least needing IRL testing to show that it's right, creating a bottleneck; and (3) related to 2b, that intelligence just turns out to not be a particularly meaningful bottleneck for most tasks.

But I'm also aware that these Yann LaCun / chollet-type arguments are just less accessible to me and I'm penalizing them for that.

(https://xoxohth.com/thread.php?thread_id=5913613&forum_id=2#50196345)

The major issue here is that pre-training has a hard, verifiable objective that labs can automatically optimize for with agents (cross entropy loss). They can optimize for speed of learning and generalization (data efficiency), and even if the range of human architecture ideas is too limited to close the gap with human learning, they can turn the learning algorithm into its own parametric function and optimize it (mimicking evolution on a genome). Whatever algorithm they find doesn’t have to be perfect considering we can train on orders of magnitude more data than humans see in a lifetime. current transformers are obviously grossly inefficient, but that likely won’t stay the case for long.

I really wouldn’t bet on this running out of steam soon and I think it’s probably dangerous to think that’s a realistic outcome considering it delays thinking about the likely near-term social and economic implications of this technology.

(https://xoxohth.com/thread.php?thread_id=5913613&forum_id=2#50196614)