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 el...
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 aki...
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 o...
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I feel like much of the argumentation in favor of those doom scenarios relies on formerly reasonable, but now outdated issues that we have faced in simpler systems, precisely because they were simpler.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 extraordinar... (read more)
To the extent that LLMS are trained on tokens output by humans in the IQ range ~50-150, the expected behavior of an extremely large LLM is to do an extremely accurate simulation of token generation by humans in the IQ range ~50-150, even if it has the computational capacity to instead do a passable simulation of something with IQ 1000. Just telling it to extrapolate might get you to say IQ 200 with passable accuracy, but not to IQ 1000. However, there are fairly obvious ways to solve this: you need to gene... (read more)