51–60 of 79 posts
In modern polarized environment you have to be hot or cold. In this example you either have to be AI doomer, or not. Nuances are not allowed, you will be voted down by both sides of argument.
People flock together in groups and sneer at one another with superiority.
Earlier quoted context omitted.
We have self-driving cars already, but they're not evenly distributed ... and they have an unsatisfying amount of human input behind the scenes.
Well, I mean, if you're willing to stretch the definition to beyond breaking, sure, but we don't have anything that the average person in the street would think of as a self-driving car. Anyone claiming a self-driving car today is a bit high on their own marketing, and there's no clear route to getting beyond that point.
https://youtu.be/-Rxvl3INKSg is a news reporter's video of taking a ride in one, so you can see what it's like to ride in one. She's a news reporter and thus not your average person because she has access to Waymo because she's a reporter, but members of the general public are also getting access and are able to do this as well. After watching that, I think a reasonable average person could believe that self driving cars are here.
As was said upthread, the future is here but unevenly distributed.
The latest LLMs are remarkable, but I think the AI Doomer claims are still a reach. My prediction is that the current AI boom will fizzle out into a handful of useful products and a lot of smoke from VC cash. Where are the moon bases? Supersonic jets? Asteroid mines? Self driving cars? We’ve gotten over excited so many times in the past.
> Self driving cars? This one in particular, I find fascinating, because we've literally just _had_ a hype cycle over it (I remember people telling me they'd be here any day now a decade ago), and now we're onto another AI hype cycle, almost immediately. Which comes first, AGI, or the self-driving car? :) (I think your other examples are qualitatively different; moon bases, supersonic (passenger) jets and asteroid mi…
It’s interesting so many people in the field thought neural networks would not lead to AI. Back when I started working with them around 2013, I thought we’d have AGI by 2030 (which was an especially quacky view back then that has now become an only slightly quacky view), but I also believed there was absolutely no way neural networks would be the approach that got us there. They seemed like fancy regression or curve interpolators—good perhaps for approximating molecular energies in an efficient way but not capable of having a conversation with me.
The “magic” that I thought neural networks were missing was algorithmic capability. Sure, they are universal function approximators, but that’s a bit of a hack theorem, and I didn’t think there was a realistic way to make use of that mathematical oddity; how could one practically train a neural network to compute a SHA256 hash? In fact, I still don’t think NNs can do that. But what I failed to realize, however, is that perhaps they could write the code to generate the hash. In retrospect, it seems kind of obvious, because of course the human brain can’t compute a hash function either—we just write code to do it as well.
Computer programs today seem to fall into one of two categories:
- “soft”: statistical learning or iterative linear algebra (NNs, SVMs, spectral methods, Monte Carlo, embeddings)
- “hard”: rigid, algorithmic, discontinuous (cryptography, mathematical proof systems, discrete optimization)
While most processes that occur in the human brain are probably characterizable as “soft”, I thought the uniqueness of human intelligence was due to a small but powerful amount of “hard” processing—and that we required a breakthrough in this area to achieve human-level AI. The release of GPT 3 immediately killed this viewpoint for me.
That said, while I now believe we may be able to achieve AGI using “soft” computation alone, I still think that achieving optimal AGI will require extremely “hard”, algorithmic computation. Optimal AGI would be that which performs better than any other computable algorithm on a very general problem space given some reasonable objective function. It’s quite possible there are many different starting points (non-optimal AIs) for getting there—neural networks being one of them—but as these systems recursively improve themselves, my guess is that they all end up converging on one universal, algorithmic, optimal AGI.
"For that matter, hardly anyone predicted the staggering, civilization-altering trajectory of neural network performance from roughly 2012 onwards." Whether the trajectory will be civilization-altering is yet to be seen. Let me put it this way: I have serious doubts.
And this is just a few non-transformer machine learning approaches!
"For that matter, hardly anyone predicted the staggering, civilization-altering trajectory of neural network performance from roughly 2012 onwards." Whether the trajectory will be civilization-altering is yet to be seen. Let me put it this way: I have serious doubts.
It's already been common-behaviour altering in many ways. I'm not sure if it counts as civilisation altering, but the way we create art, get creative review, find and consume large documents have suddenly changed. Not for the majority yet, but it's only a matter of time. For the first time we can get a knowledgeable ad-hoc assistant anywhere, immediately. Even if it's been bad in many ways, it's improving almost ever…
I disagree for your first two points. I do use generative AI to create image for my SWADE/DnD campaigns, but this is not art, this is illustration. There is no story behind the style, there is no story behind the color choice, or the meaning of the portraits.
AI was able to imitate Art since at least imagemagik. Kazimir Malevich's art, at least. Music is the same. Unless you think a DJ mixing up without mods or adding anything is an artist, in this case you might consider AI music art. I consider it entertainment.
Never got anything creative from ChatGPT, even when i was paying for it.
Bing chat (and copilot) is great to replace google, scaffold a lot of my code, and definitely boosted my productivity somewhat. But there's a reason why i don't use it to write my scenarii (still use it to spellcheck and syntax check), and even dialogs i have given up on (faster to prepare it myself in the end).
Earlier quoted context omitted.
> Self driving cars? This one in particular, I find fascinating, because we've literally just _had_ a hype cycle over it (I remember people telling me they'd be here any day now a decade ago), and now we're onto another AI hype cycle, almost immediately. Which comes first, AGI, or the self-driving car? :) (I think your other examples are qualitatively different; moon bases, supersonic (passenger) jets and asteroid mi…
We have self-driving cars already, but they're not evenly distributed ... and they have an unsatisfying amount of human input behind the scenes.
This article is absolutely pointless. The statement was from 2009. Back then, neither side had any real evidence in favour of their argument. Deep learning and AI were still purely academic experiments, with zero practicality. And it had been like this for half a century. Back then, even the best models couldn't tell cats from dogs. Today every high schooler with a laptop can solve that problem thanks to abundant har…
Not at all, but maybe you didn't read far enough to get to it.
> The statement was from 2009. Back then, neither side had any real evidence in favour of their argument. Deep learning and AI were still purely academic experiments, with zero practicality.
That's exactly what the article says:
I was explaining that there was no trend you could knowably, reliably project into the future such that you’d end up with human-level AI. And in a sense, I was right.
and then it explicitly states the point:
The trouble, with hindsight, was that I placed the burden of proof only on those saying a dramatic change would happen, not on those saying it wouldn’t. Note that this is the exact same mistake most of the world made with COVID in early 2020.
I would sum up the lesson thus: one must never use radical ignorance as an excuse to default, in practice, to the guess that everything will stay basically the same.
Earlier quoted context omitted.
> Self driving cars? This one in particular, I find fascinating, because we've literally just _had_ a hype cycle over it (I remember people telling me they'd be here any day now a decade ago), and now we're onto another AI hype cycle, almost immediately. Which comes first, AGI, or the self-driving car? :) (I think your other examples are qualitatively different; moon bases, supersonic (passenger) jets and asteroid mi…
Supersonic passenger jets have another problem, the Concorde demonstated it: it made so much noise countries forbid it from flying over their land.