you could ask it to make videos of itself skating or whatever

I don't think he meant it that way. I read it as "make a video montage of a meme" or the like. The point being that such a task exercises more elements of "human intelligence" than just chatting, like lexical and visual metaphors, perception of vision and movement, (at least a little bit of) imagination, planing and execution of a technical task, (presumably) using other software purposefully, etc. It is much harder to plan for and "fake" (whatever that means) all of that than to "fake" a text-only test with chat-bot techniques.

Of course, a blind (for instance) real man might not be able to do that particular task, but he will be able to justify that by being convincingly blind in the rest, and would be able to perform something analogous in other domains. (Music or even reciting something emphatically, or perhaps some tactile task that someone familiar with being blind might imagine.) The point I think is not to tie it to a particular sense or the body, but just to get a higher bandwidth channel for testing, one that would be so hard to fake in close to real time that you'd pretty much have to be smarter to do it.

Testing for consciousness seems to be so hard that text chat is not enough (or at least we're close to being better at faking it than testing for it), so I guess Stuart suggests we take advantage of the "in-built optimizations" that let us do stuff like fake and detect accents or infer distances from differences in apparent height (but is some contexts, status or other things). Things that we don't yet fake well, and even when we do, it's hard to mix and integrate them all.

I read it as "make a video montage of a meme" or the like.

If you told me personally to do that, I may not pass the test, either. And I personally know several humans who cannot, f.ex., "use other software purposefully". I think these kinds of challenges are a form of scope creep. We are not trying to test whether the AI is a polymath, just whether it's human or not.

It is much harder to plan for and "fake" (whatever that means) all of that than to "fake" a text-only test with chat-bot techniques.

I disagree; th... (read more)

The flawed Turing test: language, understanding, and partial p-zombies

by Stuart_Armstrong 2 min read17th May 2013184 comments


There is a problem with the Turing test, practically and philosophically, and I would be willing to bet that the first entity to pass the test will not be conscious, or intelligent, or have whatever spark or quality the test is supposed to measure. And I hold this position while fully embracing materialism, and rejecting p-zombies or epiphenomenalism.

The problem is Campbell's law (or Goodhart's law):

The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor."

This applies to more than social indicators. To illustrate, imagine that you were a school inspector, tasked with assessing the all-round education of a group of 14-year old students. You engage them on the French revolution and they respond with pertinent contrasts between the Montagnards and Girondins. Your quizzes about the properties of prime numbers are answered with impressive speed, and, when asked, they can all play quite passable pieces from "Die Zauberflöte".

You feel tempted to give them the seal of approval... but they you learn that the principal had been expecting your questions (you don't vary them much), and that, in fact, the whole school has spent the last three years doing nothing but studying 18th century France, number theory and Mozart operas - day after day after day. Now you're less impressed. You can still conclude that the students have some technical ability, but you can't assess their all-round level of education.

The Turing test functions in the same way. Imagine no-one had heard of the test, and someone created a putative AI, designing it to, say, track rats efficiently across the city. You sit this anti-rat-AI down and give it a Turing test - and, to your astonishment, it passes. You could now conclude that it was (very likely) a genuinely conscious or intelligent entity.

But this is not the case: nearly everyone's heard of the Turing test. So the first machines to pass will be dedicated systems, specifically designed to get through the test. Their whole setup will be constructed to maximise "passing the test", not to "being intelligent" or whatever we want the test to measure (the fact we have difficulty stating what exactly the test should be measuring shows the difficulty here).

Of course, this is a matter of degree, not of kind: a machine that passed the Turing test would still be rather nifty, and as the test got longer, and more complicated, as the interactions between subject and judge got more intricate, our confidence that we were facing a truly intelligence machine would increase.

But degree can go a long way. Watson won on Jeopardy without exhibiting any of the skills of a truly intelligent being - apart from one: answering Jeopardy questions. With the rise of big data and statistical algorithms, I would certainly rate it as plausible that we could create beings that are nearly perfectly conscious from a (textual) linguistic perspective. These "super-chatterbots" could only be identified as such with long and tedious effort. And yet they would demonstrate none of the other attributes of intelligence: chattering is all they're any good at (if you ask them to do any planning, for instance, they'll come up with designs that sound good but fail: they parrot back other people's plans with minimal modifications). These would be the closest plausible analogues to p-zombies.

The best way to avoid this is to create more varied analogues of the Turing test - and to keep them secret. Just as you keep the training set and the test set distinct in machine learning, you want to confront the putative AIs with quasi-Turing tests that their designers will not have encountered or planed for. Mix up the test conditions, add extra requirements, change what is being measured, do something completely different, be unfair: do things that a genuine intelligence would deal with, but an overtrained narrow statistical machine couldn't.