I feel like y’all are taking the abstractions a bit too far.
Real ~humanish level AIs (GPT4, et al), that exist right now, are capable of taking what you say and doing exactly what you mean via a combination of outputting English words and translating that to function calls in a robotic body.
While it’s very true that they aren’t explicitly programmed to do X given Y, so that you can mathematically analyze it and see precisely why it came to the conclusion, the real world effect is that it understands you and does what you want. And neither it, nor anyone else can tell you precisely why or how. Which is uncomfortable.
But we... (read 678 more words →)
I kind of think a leap in logic is being made here.
It seems like we’re going from:
A moderately smart quasi-AGI that is relatively well aligned can reliably say and do the things we mean because it understands our values and why we said what we said in the first place and why we wanted it to do the things we asked it to do.
(That seems to be the consensus and what I believe to be likely to occur in the near future. I would even argue that GPT4 is as close to AGI as we ever get, in that it’s superhuman and subhuman aspects roughly average out to something akin to a... (read 738 more words →)
Thanks for your comment!
As for the image thing, it's more of a metaphor than a literal interpretation of what I'm talking about. I'm thinking of a multidimensional matrix representation; you can think of that a bit like an image (RGB on pixels) and use similar techniques as what are used by actual image software; but it's not a literal JPEG or BMP or whatever. The idea is to be able to take advantage of compression algorithms, etc. to make the process more efficient.
The training data for the supervisory network is the input, outputs, and parameter deltas of the target network. The idea is to learn the which parameters change based on which... (read 396 more words →)
While I have been contemplating this subject for quite some time, this is my first attempt at public communication of it. I have been thinking about AI implementation, as well as safety. I've been looking at ideas of various experts in the field and considering how things might be combined and integrated to improve either performance or safety (preferably both). One important aspect of that is legibility of how neural networks work. While I've been thinking about it for some time, a YouTube video I watched last night helped me crystallize the how of implementing it. It's an interview with the authors of this paper on Arxiv. In the paper, they show... (read 1234 more words →)
Excellent posts, you and several others have stated much of what I’ve been thinking about this subject.
Sorcerer’s Apprentice and Paperclip Scenarios seem to be non-issues given what we have learned over the last couple years from SotA LLMs.
I feel like much of the argumentation in favor of those doom scenarios relies on formerly reasonable, but now outdated issues that we have faced in simpler systems, precisely because they were simpler.
I think that’s the real core of the general misapprehension that I believe is occurring in this realm. It is extraordinarily difficult to think about extremely complex systems, and so, we break them down into simpler ones so that we can examine them... (read 1386 more words →)