We are already there. Current AI is definitely capable of collecting new data and start training on that data to get a better model.
killbot5000 18 minutes ago [-]
Can someone define “improvement” in this context? This concept feels like a buzz word otherwise.
deepwoods 7 minutes ago [-]
Improvement means being able to do more complicated things more reliably. Relatedly, it means being able to learn to do new things with fewer and fewer examples. We are running out of easily verifiable or simulation-friendly or data-rich domains for LLMs to conquer. (Note that I didn't say "simple" or "easy" domains.)
letitgo12345 16 minutes ago [-]
Models that are strong enough to improve themselves without a human (I.e. ai researcher) in the loop
9 minutes ago [-]
carabiner 1 hours ago [-]
Does anyone know the status of John Carmack's AGI work?
cheschire 58 minutes ago [-]
I’ve had this question on my mind at least once a month for what feels like years at this point.
I don’t feel like monitoring Twitter to see how it’s going though.
batperson 2 hours ago [-]
[dead]
deadbabe 2 hours ago [-]
Recursive self-improvement will ultimately be a problem of money. It's a very big bill and AI companies need to start thinking in terms of how they will accumulate cheap and free energy, because merely paying for compute will no longer be enough. Money is the bottleneck. The future requires companies that are post-money.
tengbretson 41 minutes ago [-]
If a self improving model can't produce enough value to pay the electric bill then who cares? Unplug it.
timr 1 hours ago [-]
Obviously the doomers will tell you that the future looks like the Matrix, because most of what they predict is based on extrapolation from sci fi movies.
mrworld 1 hours ago [-]
The datacenters would consume a lot less energy if all the compute was handled in human brains… And there wouldn’t be any more heating if we just removed the sunlight… Truly a “Claude, please solve global warming” moment. The matrix was ahead of its time.
timr 52 minutes ago [-]
The compute in the Matrix wasn't handled with human brains. They used the bodies to generate energy, and kept the brains occupied with the simulation so they wouldn't reject the pods.
It doesn't make any sense, of course, because it's a movie.
But hey...if we're going to extrapolate wildly from sci-fi, let's at least know what the stories said.
Cyberdogs7 29 minutes ago [-]
Your statement is correct for the movies, but I think the extended universe (maybe a comic or the original script) did state the humans were used for compute, not energy. It was dumbed down for the films.
breuleux 11 minutes ago [-]
The “using humans for compute” angle makes no sense either, if you’re living in the Matrix, your brain is obviously occupied processing the Matrix. The way to do it would be to trap humans in virtual classrooms and force them to solve math problems.
emkoemko 18 minutes ago [-]
pretty sure the story is that they used human brains as computers
throwaway219450 56 minutes ago [-]
If you were a superintelligence why would you farm humans and waste resources on all the excess... material... that isn't required for thought? And unless the machine's goal is to specifically abuse human consciousness, why wouldn't they bio-engineer their own grey matter?
There are other sci fi stories which use humans for distributed computing and they don’t realize, but I don’t want to spoil by naming.
(I do realize the Matrix is a fantastic movie, but entertain the thought? At any rate, the idea that robots only run on solar and wouldn’t just use nuclear is far more stupid.)
timr 54 minutes ago [-]
I don't know. But since we're talking about fiction, pretty much any answer is valid.
bitwize 13 minutes ago [-]
Because it's a movie
goatlover 34 minutes ago [-]
Maybe the machines were spiteful after the humans blotted out the sun? Or maybe the Architect convinced them he could build the perfect simulation for humanity so might as well use them as batteries?
It's a movie based on a war with machines. Like if Skynet won but didn't actually want to kill all the humans. In fact, in the tv show Sarah Conner Chronicles, a liquid metal T1000 goes rogue and decides the only way forward is to find a way to coexist.
cwillu 24 minutes ago [-]
When it turns out the doomers don't tell us that, will you acknowledge that you don't actually know what you're talking about?
epistasis 1 hours ago [-]
The alternative is to switch to far more efficient models and have aggressive optimization of efficiency as part of the process of improvement.
There are at least two resources here that have a Pareto optimal front: time and energy. And effort spent to change the shape of that may pay off more than efforts spent purely on improving intelligence and agency.
jaggederest 1 hours ago [-]
Money can be exchanged for goods and services. I think the real constraint is actually physics, nominally energy, which is what a big chunk of the operational cost comes down to. I'm already basically assuming the next 20 years of fab time is set aside to feed the beast, so the capital cost is "give me all the processing and memory you have"
resonious 1 hours ago [-]
Data centers is a service that itself consumes many goods.
Until an AI can operate in the real world in a completely sustainable way, humans have the reins.
jaggederest 59 minutes ago [-]
I feel like I may have communicated poorly. I mean that, ultimately, any recursively self improving intelligence would be limited by the power cord, and as such, the idea that it would permanently escape human control seems somewhat silly.
colordrops 1 hours ago [-]
An AI could manage supply chains and management structures to keep the data center running. It's not like a CEO is out there running wire. The AI could just do what an executive would.
If humans decided this wasn't acceptable and tried to shut it down, the AI could epstein their way into controlling those who hold the reigns, either through bribes or blackmail.
Doesn't require boots on the ground, just a connection to the internet and an imperative to do whatever it takes.
SoftTalker 41 minutes ago [-]
There are many points of shutdown that would need to be controlled, not just one. The electrical feed, the cooling, the network, the physical integrity of the computer hardware. All with independent management.
strangattractor 29 minutes ago [-]
def self_improve_more():
if self_improved():
self_improve_more()
else:
see_tony_robins_and_buy_tapes()
1 hours ago [-]
Buttons840 1 hours ago [-]
People seem to expect a sudden shift with "self-improvement", but don't AIs already improve themselves via training? What is there to improve?
pennomi 3 minutes ago [-]
The human brain operates at better levels of intelligence than the best LLMs, at 20 watts of power. We are a long way to that kind of efficiency, it will probably take both bespoke hardware and algorithmic improvements to catch up to nature.
gchamonlive 58 minutes ago [-]
Only if you think in terms of perceived raw intelligence, but self-update is a form of valuable self-improvement that could benefit current models a lot, if they could commit facts from context into their weights cheaply and reliably.
appplication 18 minutes ago [-]
I think the idea is fundamentally improved architectures. For example, transformer-based models were an incredible stepwise improvement. Self improvement would be a model discovering a stepwise improvement similar to the transformer. And presumably the improved models from that would be more likely to make further advances still.
Learning from training data is technically self-improvement but not the sort that is typically meant in this context.
zer00eyz 1 hours ago [-]
> AIs already improve themselves via training
Marginally. Model collapse is still a problem. Continuous learning is still a problem.
For AI to make a big leap we need a big break through.
stevebmark 1 hours ago [-]
After using frontier models it’s hard to understand why anyone would think this is the path to AGI. Self improving models will likely have limited ability and returns. There may be breakthroughs that enable more general self improvement but the current state of frontier models isn’t that.
wwarner 45 minutes ago [-]
Thing is, it depends on whether llms + reinforcement can self-improve in principle. Learned recently that cognitive scientists, before the transformer & llms, were studying the possibility that thinking and learning might be based on some kind of prediction, i.e. something similar to token prediction, and I quite suddenly became less skeptical about the possibilities of llms. (Some will say I’m late to the party of course.) But if knowledge to date has been accumulated in a process quite like “chain of thought” in llms, then I don’t see any reason that computers won’t self-improve in the near future.
Rendered at 02:51:51 GMT+0000 (Coordinated Universal Time) with Vercel.
I don’t feel like monitoring Twitter to see how it’s going though.
It doesn't make any sense, of course, because it's a movie.
But hey...if we're going to extrapolate wildly from sci-fi, let's at least know what the stories said.
There are other sci fi stories which use humans for distributed computing and they don’t realize, but I don’t want to spoil by naming.
(I do realize the Matrix is a fantastic movie, but entertain the thought? At any rate, the idea that robots only run on solar and wouldn’t just use nuclear is far more stupid.)
It's a movie based on a war with machines. Like if Skynet won but didn't actually want to kill all the humans. In fact, in the tv show Sarah Conner Chronicles, a liquid metal T1000 goes rogue and decides the only way forward is to find a way to coexist.
There are at least two resources here that have a Pareto optimal front: time and energy. And effort spent to change the shape of that may pay off more than efforts spent purely on improving intelligence and agency.
Until an AI can operate in the real world in a completely sustainable way, humans have the reins.
If humans decided this wasn't acceptable and tried to shut it down, the AI could epstein their way into controlling those who hold the reigns, either through bribes or blackmail.
Doesn't require boots on the ground, just a connection to the internet and an imperative to do whatever it takes.
Learning from training data is technically self-improvement but not the sort that is typically meant in this context.
Marginally. Model collapse is still a problem. Continuous learning is still a problem.
For AI to make a big leap we need a big break through.