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.
This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.
Welcome. This week we discuss the second section in the reading guide, Forecasting AI. This is about predictions of AI, and what we should make of them.
This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. My own thoughts and questions for discussion are in the comments.
There is no need to proceed in order through this post. Feel free to jump straight to the discussion. Where applicable, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).
Reading: Opinions about the future of machine intelligence, from Chapter 1 (p18-21) and Muehlhauser, When Will AI be Created?
Summary
Opinions about the future of machine intelligence, from Chapter 1 (p18-21)
Bostrom discusses some recent polls in detail, and mentions that others are fairly consistent. Below are the surveys I could find. Several of them give dates when median respondents believe there is a 10%, 50% or 90% chance of AI, which I have recorded as '10% year' etc. If their findings were in another form, those are in the last column. Note that some of these surveys are fairly informal, and many participants are not AI experts, I'd guess especially in the Bainbridge, AI@50 and Klein ones. 'Kruel' is the set of interviews from which Nils Nilson is quoted on p19. The interviews cover a wider range of topics, and are indexed here.
(paper download)
2006
AGI-09
Polls are one source of predictions on AI. Another source is public statements. That is, things people choose to say publicly. MIRI arranged for the collection of these public statements, which you can now download and play with (the original and info about it, my edited version and explanation for changes). The figure below shows the cumulative fraction of public statements claiming that human-level AI will be more likely than not by a particular year. Or at least claiming something that can be broadly interpreted as that. It only includes recorded statements made since 2000. There are various warnings and details in interpreting this, but I don't think they make a big difference, so are probably not worth considering unless you are especially interested. Note that the authors of these statements are a mixture of mostly AI researchers (including disproportionately many working on human-level AI) a few futurists, and a few other people.
(LH axis = fraction of people predicting human-level AI by that date)
Cumulative distribution of predicted date of AI
As you can see, the median date (when the graph hits the 0.5 mark) for human-level AI here is much like that in the survey data: 2040 or so.
I would generally expect predictions in public statements to be relatively early, because people just don't tend to bother writing books about how exciting things are not going to happen for a while, unless their prediction is fascinatingly late. I checked this more thoroughly, by comparing the outcomes of surveys to the statements made by people in similar groups to those surveyed (e.g. if the survey was of AI researchers, I looked at statements made by AI researchers). In my (very cursory) assessment (detailed at the end of this page) there is a bit of a difference: predictions from surveys are 0-23 years later than those from public statements.
Armstrong and Sotala (p11) summarize a few research efforts in recent decades as follows.
Note that the problem of predicting AI mostly falls on the right. Unfortunately this doesn't tell us anything about how much harder AI timelines are to predict than other things, or the absolute level of predictive accuracy associated with any combination of features. However if you have a rough idea of how well humans predict things, you might correct it downward when predicting how well humans predict future AI development and its social consequences.
As well as just being generally inaccurate, predictions of AI are often suspected to subject to a number of biases. Bostrom claimed earlier that 'twenty years is the sweet spot for prognosticators of radical change' (p4). A related concern is that people always predict revolutionary changes just within their lifetimes (the so-called Maes-Garreau law). Worse problems come from selection effects: the people making all of these predictions are selected for thinking AI is the best things to spend their lives on, so might be especially optimistic. Further, more exciting claims of impending robot revolution might be published and remembered more often. More bias might come from wishful thinking: having spent a lot of their lives on it, researchers might hope especially hard for it to go well. On the other hand, as Nils Nilson points out, AI researchers are wary of past predictions and so try hard to retain respectability, for instance by focussing on 'weak AI'. This could systematically push their predictions later.
We have some evidence about these biases. Armstrong and Sotala (using the MIRI dataset) find people are especially willing to predict AI around 20 years in the future, but couldn't find evidence of the Maes-Garreau law. Another way of looking for the Maes-Garreau law is via correlation between age and predicted time to AI, which is weak (-.017) in the edited MIRI dataset. A general tendency to make predictions based on incentives rather than available information is weakly supported by predictions not changing much over time, which is pretty much what we see in the MIRI dataset. In the figure below, 'early' predictions are made before 2000, and 'late' ones since then.
Cumulative distribution of predicted Years to AI, in early and late predictions.
We can learn something about selection effects from AI researchers being especially optimistic about AI from comparing groups who might be more or less selected in this way. For instance, we can compare most AI researchers - who tend to work on narrow intelligent capabilities - and researchers of 'artificial general intelligence' (AGI) who specifically focus on creating human-level agents. The figure below shows this comparison with the edited MIRI dataset, using a rough assessment of who works on AGI vs. other AI and only predictions made from 2000 onward ('late'). Interestingly, the AGI predictions indeed look like the most optimistic half of the AI predictions.
Cumulative distribution of predicted date of AI, for AGI and other AI researchers
We can also compare other groups in the dataset - 'futurists' and other people (according to our own heuristic assessment). While the picture is interesting, note that both of these groups were very small (as you can see by the large jumps in the graph).
Cumulative distribution of predicted date of AI, for various groups
Remember that these differences may not be due to bias, but rather to better understanding. It could well be that AGI research is very promising, and the closer you are to it, the more you realize that. Nonetheless, we can say some things from this data. The total selection bias toward optimism in communities selected for optimism is probably not more than the differences we see here - a few decades in the median, but could plausibly be that large.
These have been some rough calculations to get an idea of the extent of a few hypothesized biases. I don't think they are very accurate, but I want to point out that you can actually gather empirical data on these things, and claim that given the current level of research on these questions, you can learn interesting things fairly cheaply, without doing very elaborate or rigorous investigations.
“Assume for the purpose of this question that such HLMI will at some point exist. How likely do you then think it is that within (2 years / 30 years) thereafter there will be machine intelligence that greatly surpasses the performance of every human in most professions?” See the paper for other details about Bostrom and Müller's surveys (the ones in the book).
In-depth investigations
If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some taken from Luke Muehlhauser's list:
How to proceed
This has been a collection of notes on the chapter. The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!
Next week, we will talk about two paths to the development of superintelligence: AI coded by humans, and whole brain emulation. To prepare, read Artificial Intelligence and Whole Brain Emulation from Chapter 2. The discussion will go live at 6pm Pacific time next Monday 29 September. Sign up to be notified here.