When asked their opinions about “human-level artificial intelligence” —aka“artificial general intelligence” (AGI)¹— many experts understandably reply that these terms haven’t yet been precisely defined, and it’s hard to talk about something that hasn’t been defined.²In this post, I want to briefly outline an imprecise but useful “working definition” forintelligencewe tend to use at MIRI. In a future post I will write about some useful working definitions forartificial general intelligence.

Imprecise definitions can be useful

Precise definitions are important, but I concur with Bertrand Russell that

[You cannot] start with anything precise. You have to achieve such precision… as you go along.

PhysicistMilan Ćirkovićagrees, andgivesan example:

The formalization of knowledge — which includes giving precise definitions — usually comes at the end of the original research in a given field, not at the very beginning. A particularly illuminating example is the concept ofnumber, which was properly defined in the modern sense only after the development of axiomatic set theory in the… twentieth century.³

For a more AI-relevant example, consider the concept of a “self-driving car,” which has been given a variety of vague definitionssince the 1930s. Would a carguided by a buried cablequalify? What about amodified 1955 Studebakerthat could use sound waves to detect obstacles and automatically engage the brakes if necessary, but could only steer “on its own” if each turn was preprogrammed? Does that count as a “self-driving car”?

What about the “VaMoRs” of the 1980s that could avoid obstacles and steer around turns using computer vision, but weren’t advanced enough to be ready for public roads? How about the 1995Navlabcar that drove across the USA and was fully autonomous for 98.2% of the trip, or the robotic cars which finished the 132-mile off-road course of the2005 DARPA Grand Challenge, supplied only with the GPS coordinates of the route? What about the winning cars of the2007 DARPA Grand Challenge, which finished an urban race while obeying all traffic laws and avoiding collisions with other cars? DoesGoogle’s driverless carqualify, given that it has logged more than 500,000 autonomous miles without a single accident under computer control, but still struggles with difficult merges and snow-covered roads?⁴

Our lack of a precise definition for “self-driving car” doesn’t seem to have hindered progress on self-driving cars very much.⁵And I’m glad we didn’t wait to seriously discuss self-driving cars until we had a precise definition for the term.

Similarly, I don’t think we should wait for a precise definition of AGI before discussing the topic seriously. On the other hand, the term is useless if it carriesnoinformation. So let’s work our way toward a stipulative, operational definition for AGI. We’ll start by developing an operational definition forintelligence.

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1. I use the HLAI and AGI interchangeably, but lately I’ve been using AGI almost exclusively, because I’ve learned that many people in the AI community react negatively to any mention of “human-level” AI but have no objection to the concept of narrow vs. general intelligence. See also Ben Goertzel’s commentshere.↩
2. Asked when he thought HLAI would be created, Pat Hayes (a past president ofAAAI)replied: “I do not consider this question to be answerable, as I do not accept this (common) notion of ‘human-level intelligence’ as meaningful.” Asked the same question, AI scientistWilliam Utherreplied: “You ask a lot about ‘human level AGI’. I do not think this term is well defined,” while AI scientistAlan Bundyreplied: “I don’t think the concept of ‘human-level machine intelligence’ is well formed.”↩
3. Sawyer (1943)gives another example: “Mathematicians first used the sign √-1, without in the least knowing what it could mean, because it shortened work and led to correct results. People naturally tried to find out why this happened and what √-1 really meant. After two hundreds years they succeeded.”Dennett (2013)makes a related comment: “定义你的条款,先生!No, I won’t. That would be premature… My [approach] is an instance ofnibblingon a tough problem instead of trying to eat (and digest) the whole thing from the outset… InElbow Room, I compared my method to the sculptor’s method of roughing out the form in a block of marble, approaching the final surfaces cautiously, modestly, working by successive approximation.”↩
4. With self-driving cars, researchers did use many precise external performance measures (e.g. accident rates, speed, portion of the time they could run unassisted, frequency of getting stuck) to evaluate progress, as well as internal performance metrics (speed of search, bounded loss guarantees, etc.). Researchers could see that these bits of progress were in the right direction, even if their relative contribution long-term was unclear. And so it is with AI in general. AI researchers use many precise external and internal performance measures to evaluate progress, but it is difficult to know the relative contribution of these bits of progress toward the final goal of AGI.↩
5. Heck, we’ve had pornography for millennia andstillhaven’t been able to define it precisely. Encyclopedia entries for “pornography”oftensimplyquote Justice Potter Stewart: “I shall not today attempt further to define the kinds of material I understand to be [pornography]… but I know it when I see it.”↩