The question “When Will AI Be Created?” is an interesting starting-point, but it is not sufficiently well-formulated for a proper Bayesian quantitative forecast.
We need to bring more rigor to this work.
The tactic from Luke’s article that has not been done in sufficient detail is “decomposition.”
My attention has shifted from the general question of “When will we have AGI,” to the question of what are some technologies which might become components of intelligent systems with self-improving capabilities, and when will these technologies become available.
Properly understanding these components and predicting when they will arrive will require a much more deliberative process than a survey.
We will have to work through creating forecasts about a series of enabling technologies one-by-one, and also analyze when the creation of one technology enables another.
We are going to have to build a nice stack of Bayes’ nets and also do some advanced regression analysis, because each year’s progress depends on the last.
I think that most people would be skeptical about such an analysis, unless they had seen similar techniques successfully applied in more mundane cases of technological forecasting. Even retrodictions along these lines could be quite helpful for establishing credibility. Without a lot of practice applying similar techniques to similar situations, I would not expect the results to be more informative than more straightforward methodologies.
While I think that these more elaborate techniques are interesting and that such an exercise would be very valuable (successful or not), as far as I know it hasn’t been done with enough rigor to be useful. I suspect that if it were done you would quickly default to rough-and-ready techniques rather than something theoretically sophisticated. But that isn’t necessarily a bad thing (the interesting part of the proposal seems to be in the breaking the question down, not in the formal techniques used to put it back together), and I’m sure you would learn a lot.
Overall I would guess that fleshing out the evidence that forecasters and experts are relying on, improving the quality of discussion, and improving the quality of elicitation, is a more promising direction (in the short term).
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.
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.
I’m fully in favor of more rigor, though I think less rigorous approaches are still informative, in the current state of very little research having been done.
In what way do you think the question should be better formulated? ‘AI’ seems to need defining better, but is this all that you mean there?
Could you give three examples of “very specific questions about specific technologies”, and perhaps one example of a dependency between two technologies and how it aids prediction?
So, suppose we just want to forecast the following: I place a really good camera with pan, zoom and a microphone in the upper corner of a room. The feed goes to a server farm, which can analyze it.
With no trouble, today we can allow the camera to photograph in infrared and some other wavelengths. Let’s do that.
When we enter a room, we also already have some information. We know whether we’re in a home, an office, a library, a hospital, a trailer or an airplane hanger. For now, let’s not have the system try to deduce that.
OK, now I want the server farm to be able to tell me exactly who is in the room, what are all of the objects in it, what are the people wearing and what are they holding in their hands. Let’s say I want that information to correctly update every ten seconds.
The problem as stated is still not fully specified, and we should assign some quantitative scales to the quality of the recognition results.
When people are trying to figure out what is in a room, they can also move around in it, pick up objects and put them down.
So, we have a relationship between object recognition and being able to path plan within a room.
People often cannot determine what an object is without reading the label. So, some NLP might be in the mix.
To determine what kind of leaf or white powder is sitting on a table, or exactly what is causing the discoloration in the grout, the system iwould require some very specialized skills.
Object recognition relies on sensors, computer memory and processing speed, and software.
Sensors:
Camera technology has run ahead very quickly. I believe that today the amount of input from cameras into the server farm can be made significantly greater than the amount of input from the eye into the brain.
I only put a single camera into my scenario, but if we are trying to max out the room’s ability to recognize objects, we can put in many.
Likewise, if the microphone is helpful in recognition, then the room can exceed human auditory abilities.
Machines have already overtaken us in being able to accept these kinds of raw data.
Memory and Processing Power:
Here is a question that requires expert thinking: So, apparently machines are recording enough video today to equal the data stream people use for visual object recognition, and computers can manipulate these images in real-time.
What versions of the object recognition task require still more memory and still faster computers, or do we have enough today?
Software
Google Goggles offers some general object recognition capabilities.
We also have voice and facial recognition.
One useful step would be to find ways to measure how successful systems like Google Goggles and facial recognition are now, then plot over time.
Getting them specific enough is pretty challenging. Usually you have to go through a several rounds of discussion in order to have a well-formulated question.
Let me try to do one in object recognition, and you can try to critique.
We need to chart as many plausible pathways as we can think of for algorithmic and neuromorphic technologies, and for specific questions within each AI sub-domain.
Thank you. To be clear, you think these are the most promising approaches to predicting the event we are interested in (some better specified version of ‘human-level AI’)?
How expensive do you think it would be to do this at the level of detail you are suggesting? Who would ideally do it?
The question “When Will AI Be Created?” is an interesting starting-point, but it is not sufficiently well-formulated for a proper Bayesian quantitative forecast.
We need to bring more rigor to this work.
The tactic from Luke’s article that has not been done in sufficient detail is “decomposition.”
My attention has shifted from the general question of “When will we have AGI,” to the question of what are some technologies which might become components of intelligent systems with self-improving capabilities, and when will these technologies become available.
Properly understanding these components and predicting when they will arrive will require a much more deliberative process than a survey.
We will have to work through creating forecasts about a series of enabling technologies one-by-one, and also analyze when the creation of one technology enables another.
We are going to have to build a nice stack of Bayes’ nets and also do some advanced regression analysis, because each year’s progress depends on the last.
I think that most people would be skeptical about such an analysis, unless they had seen similar techniques successfully applied in more mundane cases of technological forecasting. Even retrodictions along these lines could be quite helpful for establishing credibility. Without a lot of practice applying similar techniques to similar situations, I would not expect the results to be more informative than more straightforward methodologies.
While I think that these more elaborate techniques are interesting and that such an exercise would be very valuable (successful or not), as far as I know it hasn’t been done with enough rigor to be useful. I suspect that if it were done you would quickly default to rough-and-ready techniques rather than something theoretically sophisticated. But that isn’t necessarily a bad thing (the interesting part of the proposal seems to be in the breaking the question down, not in the formal techniques used to put it back together), and I’m sure you would learn a lot.
Overall I would guess that fleshing out the evidence that forecasters and experts are relying on, improving the quality of discussion, and improving the quality of elicitation, is a more promising direction (in the short term).
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.
There is a lot to answer in this skeptical post, but I do not think the effort should be discouraged.
I’m fully in favor of more rigor, though I think less rigorous approaches are still informative, in the current state of very little research having been done.
In what way do you think the question should be better formulated? ‘AI’ seems to need defining better, but is this all that you mean there?
We need to ask many, very specific questions about specific technologies, and we need to develop maps of dependencies of one technology on another.
Could you give three examples of “very specific questions about specific technologies”, and perhaps one example of a dependency between two technologies and how it aids prediction?
So, suppose we just want to forecast the following: I place a really good camera with pan, zoom and a microphone in the upper corner of a room. The feed goes to a server farm, which can analyze it. With no trouble, today we can allow the camera to photograph in infrared and some other wavelengths. Let’s do that.
When we enter a room, we also already have some information. We know whether we’re in a home, an office, a library, a hospital, a trailer or an airplane hanger. For now, let’s not have the system try to deduce that.
OK, now I want the server farm to be able to tell me exactly who is in the room, what are all of the objects in it, what are the people wearing and what are they holding in their hands. Let’s say I want that information to correctly update every ten seconds.
The problem as stated is still not fully specified, and we should assign some quantitative scales to the quality of the recognition results.
When people are trying to figure out what is in a room, they can also move around in it, pick up objects and put them down.
So, we have a relationship between object recognition and being able to path plan within a room.
People often cannot determine what an object is without reading the label. So, some NLP might be in the mix.
To determine what kind of leaf or white powder is sitting on a table, or exactly what is causing the discoloration in the grout, the system iwould require some very specialized skills.
Continuing the example:
Object recognition relies on sensors, computer memory and processing speed, and software.
Sensors:
Camera technology has run ahead very quickly. I believe that today the amount of input from cameras into the server farm can be made significantly greater than the amount of input from the eye into the brain.
I only put a single camera into my scenario, but if we are trying to max out the room’s ability to recognize objects, we can put in many.
Likewise, if the microphone is helpful in recognition, then the room can exceed human auditory abilities.
Machines have already overtaken us in being able to accept these kinds of raw data.
Memory and Processing Power:
Here is a question that requires expert thinking: So, apparently machines are recording enough video today to equal the data stream people use for visual object recognition, and computers can manipulate these images in real-time.
What versions of the object recognition task require still more memory and still faster computers, or do we have enough today?
Software
Google Goggles offers some general object recognition capabilities.
We also have voice and facial recognition.
One useful step would be to find ways to measure how successful systems like Google Goggles and facial recognition are now, then plot over time.
With that work in hand, we can begin to forecast.
Prior to forecasting when technology will be successful, we have to define the parameters of the test very precisely.
Getting them specific enough is pretty challenging. Usually you have to go through a several rounds of discussion in order to have a well-formulated question.
Let me try to do one in object recognition, and you can try to critique.
We need to chart as many plausible pathways as we can think of for algorithmic and neuromorphic technologies, and for specific questions within each AI sub-domain.
Thank you. To be clear, you think these are the most promising approaches to predicting the event we are interested in (some better specified version of ‘human-level AI’)?
How expensive do you think it would be to do this at the level of detail you are suggesting? Who would ideally do it?
We’ll have a start-up phase where we specify the project, select software and brainstorm some model templates.
After that, we’ll be able to get a better handle on costs.
We’re talking about a serious scientific effort with dozens of people.
Fortunately, we can begin with less, and we can get quite far.
People with experience in Bayesian forecasting need to work with academic, industry and government experts in AI sub-domains and computer hardware.
I envision a forecast calibration and validation process, and a periodic cycle of updates every 1-3 years.