Difference between revisions of "Architecture"
| Line 11: | Line 11: | ||
if progress happens via compute, is that architecture or content? if progress happens via larger/better datasets, is that architecture or content? | if progress happens via compute, is that architecture or content? if progress happens via larger/better datasets, is that architecture or content? | ||
| − | if you come up with a simple learning algorithm that then has to spend a lot of time interacting with the world and using compute, in order to become smart, is that architecture or content? I feel like we need to distinguish the "top level" algorithm from the "secondary level/discovered-by-top-level" algorithm. The top level architecture could be very important, even if the secondary level is basically lots and lots of content. | + | if you come up with a simple learning algorithm that then has to spend a lot of time interacting with the world and using compute, in order to become smart, is that architecture or content? I feel like we need to distinguish the "top level" algorithm from the "secondary level/discovered-by-top-level" algorithm. The top level architecture could be very important, even if the secondary level is basically lots and lots of content. "It seems to me for this particular argument to carry, it's not enough to say you need content. There has to be no master trick to learning or producing content." [https://docs.google.com/document/pub?id=17yLL7B7yRrhV3J9NuiVuac3hNmjeKTVHnqiEa6UQpJk] |
[[Category:AI safety]] | [[Category:AI safety]] | ||
Revision as of 09:52, 6 May 2020
In discussions about AI alignment (especially AI takeoff), the term architecture is used to mean ...
What is meant by terms like "mental architecture", "cognitive architecture", the "architecture of the AI", etc?
Robin Hanson: "I think our dispute in part comes down to an inclination toward architecture or content. That is, one view is that there's just a clever structure and if you have that basic structure, you have the right sort of architecture, and you set it up that way, then you don't need very much else, you just give it some sense organs, some access to the Internet or something, and then it can grow and build itself up because it has the right architecture for growth. Here we mean architecture for growth in particular, what architecture will let this thing grow well? [...] My opinion [...] is that it's largely about content. There are architectural insights. There are high-level things that you can do right or wrong, but they don't, in the end, add up to enough to make vast growth. What you need for vast growth is simply to have a big base. [...] I think that for minds, what matters is that it just has lots of good, powerful stuff in it, lots of things it knows, routines, strategies, and there isn't that much at the large architectural level." [1]
is the difference between humans and chimpanzees mostly about architecture or content? there's also the question of humans now vs humans thousands of years ago, where it seems clear that the difference is culture/"content".
"It seems to me for this particular argument to carry, it's not enough to say you need content. There has to be no master trick to learning or producing content." [2]
if progress happens via compute, is that architecture or content? if progress happens via larger/better datasets, is that architecture or content?
if you come up with a simple learning algorithm that then has to spend a lot of time interacting with the world and using compute, in order to become smart, is that architecture or content? I feel like we need to distinguish the "top level" algorithm from the "secondary level/discovered-by-top-level" algorithm. The top level architecture could be very important, even if the secondary level is basically lots and lots of content. "It seems to me for this particular argument to carry, it's not enough to say you need content. There has to be no master trick to learning or producing content." [3]
