Science hinges on measurement; so if AGI is a scientific pursuit, it must be possible to measure what it means to achieve it. Given the variety of approaches to AGI, it is hardly surprising that there are also multiple approaches to quantifying and measuring the achievement of AGI. However, things get a little simpler if one restricts attention to the subproblem of creating "human-level" AGI. When one talks about AGI beyond the human level, or AGI that is very qualitatively different from human intelligence, then the measurement issue becomes very abstract – one basically has to choose a mathematical measure of general intelligence, and adopt it as a measure of success. This is a meaningful approach, yet also worrisome, because it's difficult to tell, at this stage, what relation any of the existing mathematical measures of general intelligence is going to have to practical systems. When one talks about human-level AGI, however, the measurement problem gets a lot more concrete: one can use tests designed to measure human performance, or tests designed relative to human behavior. The measurement issue then decomposes into two subproblems: quantifying achievement of the goal of human-level AGI, and measuring incremental progress toward that goal. The former subproblem turns out to be considerably more straightforward.

The issue of metrics is closely tied up with the issue of "environments" for AGI systems. For AGI systems that are agents interacting with some environment, any method of measuring the general intelligence of these agents will involve the particulars of the AGI systems' environments. If an AGI is implemented to control video game characters, then its intelligence must be measured in the video game context. If an AGI is built with solely a textual user interface, then its intelligence must be measured purely via conversation, without measuring, for example, visual pattern recognition. And the importance of environments for AGI goes beyond the value of metrics. Even if one doesn't care about quantitatively comparing two AGI systems, it may still be instructive to qualitatively observe the different ways they face similar situations in the same environment. Using multiple AGI systems in the same environment also increases the odds of code-sharing and concept-sharing between different systems. It makes it easier to conceptually compare what different systems are doing and how they're working.

It is often useful to think in terms of "scenarios" for AGI systems, where a "scenario" means an environment plus a set of tasks defined in that environment, plus a set of metrics to measure performance on those tasks. At this stage, it is unrealistic to expect all AGI researchers to agree to conduct their research relative to the same scenario. The early-stage manifestations of different AGI approaches tend to fit naturally with different sorts of environments and tasks. However, to whatever extent it is sensible for multiple AGI projects to share common environments or scenarios, this sort of cooperation should be avidly pursued.

A variety of metrics, relative to various different environments, may be used to measure achievement of the goal of "human-level AGI." Examples include: • the classic Turing Test, conceived as (roughly) "fooling a panel of college-educated human judges, during a one hour long interrogation, that one is a human being". • the Virtual World Turing Test occurring in an online virtual world, where the AGI and the human controls are controlling avatars (this is inclusive of the standard Turing Test if one assumes the avatars can use language). • Shane Legg's AIQ measure, which is a computationally practical approximation to the algorithmic information theory based formalization of general intelligence given by Legg and Hutter. • Text compression – the idea being that any algorithm capable of understanding text should be transformable into an algorithm for compressing text based on the patterns it recognizes therein. This is the basis of the Hutter Prize, a cash prize which rewards data compression improvements on a specific 100 MB English text file. • the Online University Student Test, where an AGI has to obtain a college degree at an online university, carrying out the same communications with the professors and the other students as a human student would (including choosing its curriculum). • the Robot University Student Test, where an AGI has to obtain a college degree at a physical university, carrying out the same communications as a human student would, and also moving about the campus and handling relevant physical objects in a sufficient manner to complete the coursework. • the Artificial Scientist Test, where an AGI that can do high-quality, original scientific research, including choosing the research problem, reading the relevant literature, writing and publishing the paper, etc. (this may be refined to a Nobel Prize Test).

Each of these approaches has its pluses and minuses. None of them can sensibly be considered necessary conditions for human-level intelligence, but any of them may plausibly be considered sufficient conditions. The latter three have the disadvantage that they may not be achievable by every human – so they may set the bar a little too high. The former two have the disadvantage of requiring AGI systems to imitate humans, rather than just honestly being themselves; and it may be that accurately imitating humans when one does not have a human body or experience, requires significantly greater than human level intelligence. Regardless of the practical shortcomings of the above measures, though, I believe they are basically adequate as precisiations of "what it means to achieve human-level general intelligence."

While postulating criteria for assessing achievement of full human-level general intelligence seems relatively straightforward, positing good tests for intermediate progress toward the goal of human-level AGI seems much more difficult. That is: it is not clear how to effectively measure whether one is, say, 50 percent of the way to human-level AGI? Or, say, 75 or 25 percent? What I have found via a long series of discussions on this topic with a variety of AGI researchers is that: • It's possible to pose many "practical tests" of incremental progress toward human-level AGI, with the property that if a proto-AGI system passes the test using a certain sort of architecture and/or dynamics, then this implies a certain amount of progress toward human-level AGI based on particular theoretical assumptions about AGI. • However, in each case of such a practical test, it seems intuitively likely to a significant percentage of AGI researchers that there is some way to "game" the test via designing a system specifically oriented toward passing that test, and which doesn't constitute dramatic progress toward AGI.

A series of practical tests of this nature were discussed and developed at a 2009 gathering at the University of Tennessee, Knoxville, called the "AGI Roadmap Workshop," which led to an article in AI Magazine titled Mapping the Landscape of Artificial General Intelligence. Among the tests discussed there were: • The Wozniak "coffee test": go into an average American house and figure out how to make coffee, including identifying the coffee machine, figuring out what the buttons do, finding the coffee in the cabinet, etc. • Story understanding – reading a story, or watching it on video, and then answering questions about what happened (including questions at various levels of abstraction) • Passing the elementary school reading curriculum (which involves reading and answering questions about some picture books as well as purely textual ones) • Learning to play an arbitrary video game based on experience only, or based on experience plus reading instructions • Passing child psychologists' typical evaluations aimed at judging whether a human preschool student is normally intellectually capable

One thing we found at the AGI Roadmap Workshop was that each of these tests seems to some AGI researchers to encapsulate the crux of the AGI problem, and to be unsolvable by any system not far along the path to human-level AGI – yet seems to other AGI researchers, with different conceptual perspectives, to be something probably game-able by narrow-AI methods. And of course, given the current state of science, there's no way to tell which of these practical tests really can be solved via a narrow-AI approach, except by having a lot of researchers and engineers try really hard over a long period of time.

Complementing the above tests that are heavily inspired by human everyday life, there are also some more computer science oriented evaluation paradigms aimed at assessing AI systems going beyond specific tasks. For instance, there is a literature on multitask learning, where the goal for an AI is to learn one task quicker given another task solved previously. There is a literature on shaping, where the idea is to build up the capability of an AI by training it on progressively more difficult versions of the same tasks. Also, Achler has proposed criteria measuring the "flexibility of recognition" and posited this as a key measure of progress toward AGI. While we applaud the work done in these areas, we also note it is an open question whether exploring these sorts of processes using mathematical abstractions, or in the domain of various machine-learning or robotics test problems, is capable of adequately addressing the problem of AGI. The potential problem with this kind of approach is that generalization among tasks, or from simpler to more difficult versions of the same task, is a process whose nature may depend strongly on the overall nature of the set of tasks and task-versions involved. Real-world humanly-relevant tasks have a subtlety of interconnectedness and developmental course that is not captured in current mathematical learning frameworks nor standard AI test problems.

To put it a little differently, it is possible that all of the following hold: • the universe of real-world human tasks may possess a host of "special statistical properties" that have implications regarding what sorts of AI programs will be most suitable • exploring and formalizing and generalizing these statistical properties is an important research area; however, • an easier and more reliable approach to AGI testing is to create a testing environment that embodies these properties implicitly, via constituting an emulation of the most cognitively meaningful aspects of the real-world human learning environment Another way to think about these issues is to contrast the above-mentioned "AGI Roadmap Workshop" ideas with the "General Game Player (GGP)" AI competition, in which AIs seek to learn to play games based on formal descriptions of the rules. Clearly doing GGP well requires powerful AGI; and doing GGP even mediocrely probably requires robust multitask learning and shaping. But it is unclear whether GGP constitutes a good approach to testing early-stage AI programs aimed at roughly humanlike intelligence. This is because, unlike the tasks involved in, say, making coffee in an arbitrary house, or succeeding in preschool or university, the tasks involved in doing simple instances of GGP seem to have little relationship to humanlike intelligence or real-world human tasks.

So, an important open question is whether the class of statistical biases present in the set of real-world human environments tasks, has some sort of generalizable relevance to AGI beyond the scope of human-like general intelligence, or is informative only about the particularities of human-like intelligence. Currently we seem to lack any solid, broadly accepted theoretical framework for resolving this sort of question.

A question raised by these various observations is whether there is some fundamental reason why it's hard to make an objective, theory-independent measure of intermediate progress toward advanced AGI, which respects the environment and task biased nature of human intelligence as well as the mathematical generality of the AGI concept. Is it just that we haven't been smart enough to figure out the right test – or is there some conceptual reason why the very notion of such a test is problematic? Why might a solid, objective empirical test for intermediate progress toward humanly meaningful AGI be such a difficult project? One possible reason could be the phenomenon of "cognitive synergy" briefly noted above. In this hypothesis, for instance, it might be that there are 10 critical components required for a human-level AGI system. Having all 10 of them in place results in human-level AGI, but having only 8 of them in place results in having a dramatically impaired system – and maybe having only 6 or 7 of them in place results in a system that can hardly do anything at all.

Of course, the reality is not as strict as the simplified example in the above paragraph suggests. No AGI theorist has really posited a list of 10 crisply-defined subsystems and claimed them necessary and sufficient for AGI. We suspect there are many different routes to AGI, involving integration of different sorts of subsystems. However, if the cognitive synergy hypothesis is correct, then human-level AGI behaves roughly like the simplistic example in the prior paragraph suggests. Perhaps instead of using the 10 components, you could achieve human-level AGI with 7 components, but having only 5 of these 7 would yield drastically impaired functionality – etc. To mathematically formalize the cognitive synergy hypothesis becomes complex, but here we're only aiming for a qualitative argument. So for illustrative purposes, we'll stick with the "10 components" example, just for communicative simplicity.

Next, let's additionally suppose that for any given task, there are ways to achieve this task using a system that is much simpler than any subset of size 6 drawn from the set of 10 components needed for human-level AGI, but works much better for the task than this subset of 6 components (assuming the latter are used as a set of only 6 components, without the other 4 components). Note that this additional supposition is a good bit stronger than mere cognitive synergy. For lack of a better name, I have called this hypothesis "tricky cognitive synergy". Tricky cognitive synergy would be the case if, for example, the following possibilities were true: • creating components to serve as parts of a synergetic AGI is harder than creating components intended to serve as parts of simpler AI systems without synergetic dynamics • components capable of serving as parts of a synergetic AGI are necessarily more complicated than components intended to serve as parts of simpler AI systems

These certainly seem reasonable possibilities, since to serve as a component of a synergetic AGI system, a component must have the internal flexibility to usefully handle interactions with a lot of other components as well as to solve the problems that come its way. If tricky cognitive synergy holds up as a property of human-level general intelligence, the difficulty of formulating tests for intermediate progress toward human-level AGI follows as a consequence. Because, according to the tricky cognitive synergy hypothesis, any test is going to be more easily solved by some simpler narrow AI process than by a partially complete human-level AGI system. At the current stage in the development of AGI, we don't really know how big a role "tricky cognitive synergy" plays in the general intelligence. Quite possibly, 5 or 10 years from now someone will have developed wonderfully precise and practical metrics for the evaluation of incremental progress toward human-level AGI. However, it's worth carefully considering the possibility that fundamental obstacles, tied to the nature of general intelligence, stand in the way of this possibility.