I’d also point out that any forecast that relies on our current best guesses about the nature of general intelligence strike me as very unlikely to be usefully accurate—we have a very weak sense of how things will play out, how the specific technologies involved will relate to each other, and (more likely than not) even what they are.
It seems that many tend to agree with you, in that, on page 9 of the Muller—Bostrom survey, I see that 32.5 % of respondents chose “Other method(s) currently completely unknown.”
We do have to get what data we can, of course, like SteveG says, but (and I will qualify this in a moment), depending on what one really means by AI or AGI, it could be argued that we are in the position of physics at the dawn of the 20th century, vis a vie the old “little solar system” theory of the atom, and Maxwell’s equations, which were logically incompatible.
It was known that we didn’t understand something important, very important, yet, but how does one predict how long it will take to discover the fundamental conceptual revolution (quantum mechanics, in this case) that opens the door to the next phase of applications, engineering, or just “understanding”?
Now to that “qualification” I mentioned: some people of course don’t really think we lack any fundamental conceptual understanding or need a conceptual revolution-level breakthrough, i.e. in your phrase ‘...best guesses about the nature of general intelligence’ they think they have the idea down.
Clearly the degree of interest and faith that people put in “getting more rigor” as a way of gaining more certainty about a time window, depends individually on what “theory of AI” if any, they already subscribe to, and of course the definition and criterion of HLAI that the theory of AI they subscribe to would seek to achieve. For brute force mechanistic connectionists, getting more rigor by decomposing the problem into components / component industries (machine vision / object recognition, navigation, natural language processing in a highly dynamically evolving, rapidly context shifting environment {a static context, fixed big data set case is already solved by Google}, and so on) would of course get more clues about how close we are.
But if we (think that) existing approaches lack something fundamental, or we are after something not yet well enough understood to commit to a scientific architecture for achieving it (for me, that is “real sentience” in addition to just “intelligent behavior” -- what Chalmers called “Hard problem” phenomena, in addition to “Easy problem” phenomena), how do we get more rigor?
How could we have gotten enough rigor to predict when some clerk in a patent office would completely delineate a needed change our concepts of space and time, and thus open the door to generations of progress in engineering, cosmology, and so on (special relativity, of course)?
What forcasting questions would have been relevant to ask, and to whom?
That said, we need to get what rigor we can, and use the data we can get, not data we cannot get.
But remaining mindful that what counts as “useful” data depends on what one already believes the “solution” to doing AI is going to look like.… one’s implicit metatheory about AI architecture, is a key interpretive yardstick also, to overlay onto the confidence levels of active researchers.
This point might seem obvious, as it is indeed almost being made, quite a lot, though not quite sharply enough, in discussing some studies.
I have to remind myself, occasionally, forecasting across the set of worldwide AI industries, is forecasting; a big undertaking, but it is not a way of developing HLAI itself. I guess we’re not in here to discuss the merits of different approaches, but to statistically classify their differential popularity among those trying to do AI. It helps to stay clear about that.
On the whole, though, I am very satisfied with attempts to highlight the assumptions, methodology and demographics of the study respondents. The level of intellectual honesty is quite high, as is the frequency of reminders and caveats (in varying fashion) that we are dealing with epistemic probability, not actual probability.
It seems that many tend to agree with you, in that, on page 9 of the Muller—Bostrom survey, I see that 32.5 % of respondents chose “Other method(s) currently completely unknown.”
We do have to get what data we can, of course, like SteveG says, but (and I will qualify this in a moment), depending on what one really means by AI or AGI, it could be argued that we are in the position of physics at the dawn of the 20th century, vis a vie the old “little solar system” theory of the atom, and Maxwell’s equations, which were logically incompatible.
It was known that we didn’t understand something important, very important, yet, but how does one predict how long it will take to discover the fundamental conceptual revolution (quantum mechanics, in this case) that opens the door to the next phase of applications, engineering, or just “understanding”?
Now to that “qualification” I mentioned: some people of course don’t really think we lack any fundamental conceptual understanding or need a conceptual revolution-level breakthrough, i.e. in your phrase ‘...best guesses about the nature of general intelligence’ they think they have the idea down.
Clearly the degree of interest and faith that people put in “getting more rigor” as a way of gaining more certainty about a time window, depends individually on what “theory of AI” if any, they already subscribe to, and of course the definition and criterion of HLAI that the theory of AI they subscribe to would seek to achieve.
For brute force mechanistic connectionists, getting more rigor by decomposing the problem into components / component industries (machine vision / object recognition, navigation, natural language processing in a highly dynamically evolving, rapidly context shifting environment {a static context, fixed big data set case is already solved by Google}, and so on) would of course get more clues about how close we are.
But if we (think that) existing approaches lack something fundamental, or we are after something not yet well enough understood to commit to a scientific architecture for achieving it (for me, that is “real sentience” in addition to just “intelligent behavior” -- what Chalmers called “Hard problem” phenomena, in addition to “Easy problem” phenomena), how do we get more rigor?
How could we have gotten enough rigor to predict when some clerk in a patent office would completely delineate a needed change our concepts of space and time, and thus open the door to generations of progress in engineering, cosmology, and so on (special relativity, of course)?
What forcasting questions would have been relevant to ask, and to whom?
That said, we need to get what rigor we can, and use the data we can get, not data we cannot get.
But remaining mindful that what counts as “useful” data depends on what one already believes the “solution” to doing AI is going to look like.… one’s implicit metatheory about AI architecture, is a key interpretive yardstick also, to overlay onto the confidence levels of active researchers.
This point might seem obvious, as it is indeed almost being made, quite a lot, though not quite sharply enough, in discussing some studies.
I have to remind myself, occasionally, forecasting across the set of worldwide AI industries, is forecasting; a big undertaking, but it is not a way of developing HLAI itself. I guess we’re not in here to discuss the merits of different approaches, but to statistically classify their differential popularity among those trying to do AI. It helps to stay clear about that.
On the whole, though, I am very satisfied with attempts to highlight the assumptions, methodology and demographics of the study respondents. The level of intellectual honesty is quite high, as is the frequency of reminders and caveats (in varying fashion) that we are dealing with epistemic probability, not actual probability.