Earlier quoted context omitted.
Yeah, I'm definitely not saying this is already the ultimate solution. But if you look at their scaling experiments and extrapolate the results, it roughly shows how a model with number of parameters on the same order of magnitude as the human brain could more or less achieve overall human level performance. Of course that doesn't mean the architecture has to extrapolate, but it's definitely a path worth walking unle…
Except it is pretty naive to extrapolate like that. A high order polynomial looks flat if you zoom in enough. You can't extrapolate far beyond what you have empirical data for without potentially running into huge problems.
John Carmack's new AGI company, Keen Technologies, has raised a $20M round
451–460 of 643 posts
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#452Earlier quoted context omitted.
And consensus is never wrong!
Especially assertions of consensus provided without evidence of said consensus.
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#453Earlier quoted context omitted.
I know it maybe too much to ask but as a noob to this field I’d love to hear more about why AGI is not feasible even in 50 years.
I've seen a few posts here asking why it isn't possible. Research doesn't work like that, you can't just imagine something is possible and demand reasons why it isn't. How is it possible? It feels a bit like a lot of these fringe science things (electromagnetic fields causing harm etc) where people's argument is that nobody's shown it isn't true. Well if it's true, propose a mechanism and study it. You're not really…
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#454Earlier quoted context omitted.
> Who is we exactly? When I read these kind of threads, I believe it's "enthusiast" laypeople who follow the headlines but don't actually have a deep understanding of the tech. Of course there are the promoters who are raising money and need to frame each advance in the most optimistic light. I don't see anything wrong with that, it just means that there will be a group of techie but not research literate folks who a…
Can someone please explain like we are fifteen why AGI is impossible, at least right now? Or if not AGI, then something similar to a cat/etc mind? As far as I am imagining it, current models are pipelines of various trained networks (and more traditional filters in the mix) that operate like request-reply. Why can’t you just connect few different pipelines in a loop/graph and make an autonomous self-feeding entity? B…
The sperm whale has a brain that is several times larger than ours.
What do you do differently in your AGI design to get a human, gorilla, or whale brain?
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#455Earlier quoted context omitted.
Why is it so important to you that humans be the smartest beings on the planet?
Because if we aren’t, it leaves us liable to be exterminated or enslaved to suit the goals of the superior beings. (and I fundamentally believe that the existence of the human race is a good thing, and that slavery is bad).
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#456I don’t understand why you would want AGI. Even ignoring Terminator-esque worst case scenarios, AGI means humans are no longer the smartest entities on the planet. The idea that we can control something like that is laughable.
Terminator is of course fiction, but AGI being more agent-y than tool-y suggests we ought to be very careful in trying to design it so that its interests align with (even if not perfectly matching) our own interests. There are lots of reasons to be pessimistic about this at the moment, from outright control as you say being laughably unlikely, to the slow state of progress on formal alignment problems that e.g. MIRI has been working on for many years relative to the recent relatively fast progress in non-G AI capabilities that may help make AGI come sooner.
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#457Earlier quoted context omitted.
> we didn't have the theory or the models, there was no path forward and so it was impossible to predict. Now we have a fairly concrete idea of what a potential AGI might look like - an RL agent that uses a large transformer. Who is we exactly? As someone working in AI research I know no one that would agree with this statement, so im quite puzzled by that statement.
> As someone working in AI research Being in the tail end of my PhD, I want to second this sentiment. I'm not even bullish on AGI (more specifically HLI) in 50 years. Scale will only take you so far and we have to move past frequentism. Hell, causality research still isn't that popular but is quite important for intelligence. I think people (especially tech enthusiasts) are getting caught in the gullibility gap.
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#458Earlier quoted context omitted.
I believe that's the Turing Test, not necessarily a definition (or requirement) for AGI.
Where’s AGI defined? I’ve only seen it used in the context of “can pass the Turing test”
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#459Earlier quoted context omitted.
I work in AI and would roughly agree with it to first order. For me the key breakthrough has been seeing how large transformers trained with big datasets have shown incredible performance in completely different data modalities (text, image, and probably soon others too). This was absolutely not expected by most researchers 5 years ago.
past breakthrough doesn't guarantee future breakthroughs.
Re: John Carmack's new AGI company, Keen Technologies, has raised a $20M round
#460Earlier quoted context omitted.
One big issue is how exactly we'll continue to scale. Exponential growth is hard to maintain. An example: In the Chinchilla paper [1], the authors suggest that most big transformer models are undertrained, and that we will probably see diminishing returns in scaling up the size of networks if we don't also scale up the size of the datasets. They have a subanalysis where they extrapolate out how big datasets will need…
The scaling laws of large language models are very specific to language models and the way they're trained. The important thing that LLMs demonstrate is that transformers are capable of this kind of scale (where other approaches have not) In the RL space, a sufficiently complex, stochastic environment is effectively a data generator.
I haven't heard of any groups who are studying data constrained learning in the context of LLMs, but that will probably change as models get bigger. And at that point, architectures with better scaling laws may be right around the corner, or they may not. That's the pain of trying to project these things into the future.