And I’d say that even if someone had a correct general idea how to build an AGI (an assumption that by itself beggars belief given the current state of the relevant science), developing an actual working implementation with today’s software tools and methodologies would be sort of like trying to build a working airplane with Neolithic tools.
This is the sort of sentiment that has people predict that AGI will be built in 300 years, because “300 years” is how difficult the problem feels like. There is a lot of uncertainty about what it takes to build an AGI, and it would be wrong to be confident one way or the other just how difficult that’s going to be, or what tools are necessary.
We understand both airplanes and Neolithic tools, but we don’t understand AGI design. Difficulty in basic understanding doesn’t straightforwardly translate into the difficulty of solution.
We understand both airplanes and Neolithic tools, but we don’t understand AGI design. Difficulty in basic understanding doesn’t straightforwardly translate into the difficulty of solution.
That is true, but a project like OpenCog can succeed only if: (1) there exists an AGI program simple enough (in terms of both size and messiness) to be doable with today’s software technology, and (2) people running the project have the right idea how to build it. I find both these assumptions improbable, especially the latter, and their conjunction vanishingly unlikely.
Perhaps a better analogy would be if someone embarked on a project to find an elementary proof of P != NP or some such problem. We don’t know for sure that it’s impossible, but given both the apparent difficulty of the problem and the history of the attempts to solve it, such an announcement would be rightfully met with skepticism.
You appealed to inadequacy of “today’s software tools and methodologies”. Now you make a different argument. I didn’t say it’s probable that solution will be found (given the various difficulties), I said that you can’t be sure that it’s Neolithic tools in particular that are inadequate.
It’s hard to find a perfect analogy here, but both analogies I mentioned lend support to my original claim in a similar way.
It may be that with the present state of math, one could cite a few established results and use them to construct a simple proof of P != NP, only nobody’s figured it out yet. Analogously, it may be that there is a feasible way to take present-day software tools and use them to implement a working AGI. In both cases, we lack the understanding that would be necessary either to achieve the goal or to prove it impossible. However, what insight and practical experience we have strongly suggests that neither thing is doable, leading to conclusion that the present-day software tools likely are inadequate.
In addition to this argument, we can also observe that even if such a solution exists, finding it would be a task of enormous difficulty, possibly beyond anyone’s practical abilities.
This reasoning doesn’t lead to the same certainty that we have in problems involving well-understood physics, such as building airplanes, but I do think it’s sufficient (when spelled out in full detail) to establish a very high level of certainty nevertheless.
This is the sort of sentiment that has people predict that AGI will be built in 300 years, because “300 years” is how difficult the problem feels like. There is a lot of uncertainty about what it takes to build an AGI, and it would be wrong to be confident one way or the other just how difficult that’s going to be, or what tools are necessary.
We understand both airplanes and Neolithic tools, but we don’t understand AGI design. Difficulty in basic understanding doesn’t straightforwardly translate into the difficulty of solution.
That is true, but a project like OpenCog can succeed only if: (1) there exists an AGI program simple enough (in terms of both size and messiness) to be doable with today’s software technology, and (2) people running the project have the right idea how to build it. I find both these assumptions improbable, especially the latter, and their conjunction vanishingly unlikely.
Perhaps a better analogy would be if someone embarked on a project to find an elementary proof of P != NP or some such problem. We don’t know for sure that it’s impossible, but given both the apparent difficulty of the problem and the history of the attempts to solve it, such an announcement would be rightfully met with skepticism.
You appealed to inadequacy of “today’s software tools and methodologies”. Now you make a different argument. I didn’t say it’s probable that solution will be found (given the various difficulties), I said that you can’t be sure that it’s Neolithic tools in particular that are inadequate.
It’s hard to find a perfect analogy here, but both analogies I mentioned lend support to my original claim in a similar way.
It may be that with the present state of math, one could cite a few established results and use them to construct a simple proof of P != NP, only nobody’s figured it out yet. Analogously, it may be that there is a feasible way to take present-day software tools and use them to implement a working AGI. In both cases, we lack the understanding that would be necessary either to achieve the goal or to prove it impossible. However, what insight and practical experience we have strongly suggests that neither thing is doable, leading to conclusion that the present-day software tools likely are inadequate.
In addition to this argument, we can also observe that even if such a solution exists, finding it would be a task of enormous difficulty, possibly beyond anyone’s practical abilities.
This reasoning doesn’t lead to the same certainty that we have in problems involving well-understood physics, such as building airplanes, but I do think it’s sufficient (when spelled out in full detail) to establish a very high level of certainty nevertheless.