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Comment by nh23423fefe | Hacker News Reader

It's only a matter of time until the technological singularity

> The WM of a self-referential NN, however, can keep rapidly modifying all of itself during runtime. In principle, such NNs can meta-learn to learn, and metameta-learn to meta-learn to learn, and so on, in the sense of recursive self-improvement.

Everyone who doubts is hanging everything on "in principle" being too hard. Seems ridiculous to me, a failure of imagination.

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"It suddenly stopped self-improving." "What happened?" "It ... it looks like it found a way to autogenerate content that is optimized for its reward function and now binges on it 24/7..." ><

"It seems to have optimized for reruns of Breaking Bad, and exhibits markedly elevated energy consumption during the car chase scenes."

I think there's some context missing when we're talking about the singularity, this is the whole Marcus "AI is hitting a wall" debate (maybe I'm reading part of that reference in your comment and it isn't there). For different people "hitting the wall" means different things and we're not really communicating well with one another. Marcus is concerned about AGI while others are concerned about ML in general and what it can do. So LLMs and LGMs (like Dall-E) are showing massive improvements and seem to be counter to Marcus's claim. But from the other side, there's still issues with solving AGI with things like causal learning and symbolic learning. But what's bugged me a bit about Marcus's claim is that those areas are also rapidly improving. I just think it is silly to say that Dall-E is a proof that Marcus is wrong rather than pointing towards our improvements in causal learning. But I guess few are interested in CL and it isn't nearly as flashy. I know Marcus reads HN, so maybe you don't think we've been making enough strides in CL/SL? I can agree that it doesn't get enough attention, but ML is very hyped at this point.

Schmidhuber has written about recursive self-improvement since his diploma thesis in the 80s: "Evolutionary principles in self-referential learning, or on learning how to learn: The meta-meta-... hook".

Your quote sounds like it could just as well have been from that thesis.

There's a saying that goes, as soon as we can build it, it's not AI any more

"In principle" is not only (1) hard in practice but also even in principle it is (2) limited by the capacity of the NN. It's (2) which gives me some reassurance.

Even if the algorithms were here to do that job, where will the hardware come from? I'm staring at almost a decade of commercial processor (i7) lineage right now and the jump has been from 4 to 6 cores with no change in clock speed (maybe the newer one is even slower actually). There definitely won't be any singularity jumping off unless today's hardware gets another few decades of Moore's law, and that is not happening.

Well, the code that was published alongside the article is written in Python and CUDA, so you're not looking at the right kind of processor to start.

My 5-year-old, consumer-grade GPU does 1.5 MHz * 2300 cores, whereas the equivalent released this year does 1.7 Mhz * 8900 cores. Granted, not the best way to measure GPU performance, but it is roughly keeping pace with Moore's law, and it's going to be a better indicator of the future than Intel CPU capabilities, especially for machine learning applications.

So you are saying that we can get another 3 decades of Moore's law by switching to GPUs made of silicon rather than CPUs made of silicon? Well fuck, problem solved then. I was completely unaware it was so easy.

Yeah, but mostly for matrix multiplications

GHz not Mhz for your GPU specs.

It's a bit dishonest to be looking at specifically the manufacturer who spent most of the past decade enjoying its effective monopoly on desktop CPUs as a reference for how computers have improved. Even moreso since 4 and 6 core CPUs are not representative of the high end systems used to train even current state of the art ML models.

Greetings, time traveller. Welcome to 2022! We've had 16-cire consumer CPUs for almost half a decade. And even higher core counts on the higher end platforms. But you already knew all that, didn't you

I'll give you the benefit of the doubt and assume you just haven't looked at the CPU market in the last 5 years, but AMD kicked Intels ass, and Intel is also finally being forced to compete now. There's even a 16c/32t CPU on AMDs standard consumer socket: https://www.amd.com/en/products/cpu/amd-ryzen-9-5950x

Singularity is not that important. Scale and locality is. Information that has to travel across the world suffers from misordering/relativity. Same for across the room or across a single wire that is nondeterministically but carelessly left unplugged. An oracle doesn’t help in that case. Instead what you want is a new kind of being.

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