[samsartor]

All past data shows is exponential growth in the cost of AI systems, not an exponential growth in capability. Capabilities have certainly expanded, but that is hard to measure. The growth curve is just as likely to be sigmoid-shaped. Just a phase transition from "computers process information strictly procedurally" to "computers use fuzzy logic sometimes too". And if we've exhausted all the easy wins, that explains the increased interest in alternative scaling paths.

Obviously predicting the future is hard, and we won't know where this stops till we get there. But I think a degree of skepticism is warranted.

[DennisP]

Once AI becomes self-improving, using its intelligence to make itself more intelligent, exponential progress seems like the logical consequence. Any lack of exponential progress before it becomes self-improving doesn't have much bearing on that.

It certainly will be sigmoid-shaped in the end, but the top of the sigmoid could be way beyond human intelligence.

[throwaway0123_5]

I'm not completely convinced of this, even in the presence of AGI that is peak-human intelligence in all ways (lets say on-par with the top 1% researchers from top AGI labs, with agency and online learning are fully solved). One reason for this is what the sibling comment argues:

> Exponentially smarter AI meets exponentially more difficult wins.

Another is that it doesn't seem like intelligence is the main/only bottleneck to producing better AIs right now. OpenAI seems to think building a $100-500B data center is necessary to stay ahead*, and it seems like most progress thus far has been from scaling compute (not to trivialize architectures and systems optimizations that make that possible). But if GPT-N decides that GPT-N+1 needs another OOM increase in compute, it seems like progress will mostly be limited by how fast increasingly enormous data centers and power plants can be built.

That said, if smart-human-level AGI is reached, I don't think it needs to be exponentially improving to change almost everything. I think AGI is possibly (probably?) in the near-future, also believing that it won't improve exponentially doesn't ease my anxiety about potential bad outcomes.

*Though admittedly DeepSeek _may_ have proven this wrong. Some people seem to think their stated training budget is misleading and/or that they trained on OpenAI outputs (though I'm not sure how this would work for the o models given that they don't provide their thinking trace). I'd be nervous if it was my money going towards Stargate right now.

[DennisP]

Well we do have an existence proof that human-level intelligence can be trained and run on a few thousand calories per day. We just haven't figured out how to build something that efficient yet.

[snackbroken]

The inference and on-line fine tuning stage can run on a few thousand calories a day. The training stage has taken roughly 100 TW * 1bn years ≈ 10²⁸ calories.

[DennisP]

Hmm I'm not convinced that human brains have all that much preprogrammed at birth. Babies don't even start out with object permanence. All of human DNA is only six billion bits, which wouldn't be much even if it encoded neural weights instead of protein structures.

[snackbroken]

Human babies are born significantly premature as a compromise between our upright gait and large head-to-body ratio. A whole lot of neurological development that happens in the first couple of years is innate in humans just like in other mammals, the other mammals just develop them before being born. E.g. a foal can walk within hours of being born.

Babies are born with a fully functioning image recognition stack complete with a segmentation model, facial recognition, gaze estimator, motion tracker and more. Likewise, most of the language model is pre-trained and language acquisition is in large part a pruning process to coalesce unused phonemes, specialize general syntax rules etc. Compare with other animals that lack such a pre-trained model - no matter how much you fine-tune a dog, it's not going to recite Shakespeare. Several other subsystems come online in the first few years with or without training; one example that humans share with other great apes is universal gesture production and recognition models. You can stretch out your arm towards just about any human or chimpanzee on the planet and motion your hand towards your chest and they will understand that you want them to come over. Babies also ship with a highly sophisticated stereophonic audio source segmentation model that can easily isolate speaking voices from background noise. Even when you limit yourself to just I/O related functions, the list goes on from reflexively blinking in response to rapidly approaching objects to complicated balance sensor fusion.

[pdimitar]

I agree with your take and would slightly refine it to remark that having in mind how protein unfolding / producing works in our bodies, I'd say our genome is heavily compressed and we can witness decompression with an electronic microscope (how RNA serves like a command sequence determining the resulting protein).

[apitman]

The human genome has 6 billion bases, not 6 billion bits. Each base can take one of 4 values, so significantly more data than binary. But maybe not enough of a difference to affect your point.

[accra4rx]

self improving only when it knows how to test itself . if the test is predictable outcome defined by humans most companies are going to fine tune to pass self improving test , but what happens next . Improvement is vague in terms of who seeks the benefit and may not fall as how humans have thought over millions of years of evolution.

[samsartor]

I think we are already way past single-human intellence. No one person understands (or could possibly understand) the whole system from the silicon up. Even if you had one AI "person" a 100x smarter than their coworkers, who can solve hard problems at many levels of the stack, what could they come up with that generations of tens of thousands of humans working together haven't? Something surely, but it could wind up being marginal. Exponentially smarter AI meets exponentially more difficult wins.

[PhilipRoman]

>No one person understands (or could possibly understand) the whole system from the silicon up.

I'm not a fan of this meme that seems to be very popular on HN. Someone with knowledge in EE and drivers can easily acquire enough programming knowledge in the higher layers of programming, at which point they can fill the gaps and understand the entire stack. The only real barrier is that hardware today is largely proprietary, meaning you need to actually work at the company that makes it to have access to the details.

[samsartor]

Good point. I agree actually, many people do put the work in to understand the whole stack. But one person could not have built the whole thing themselves obviously. All I was trying to say is we already live with superhuman intelligences every day, they are called "teams".

[yencabulator]

Your argument is that no one person can build a whole cargo container ship, hence cargo container ships are intelligent? The whole of humanity cannot build from scratch a working human digestive track, hence human digestive track is more intelligent than all of humanity?

Things can be complex without being intelligent.

[samsartor]

Nope, not my point. My point was that even if we get superhuman AGI, the effect of self-improvement may not be that large.