Try to teach the competitor to do some things that make sense to humans and some things that do no make sense to humans, from wildly different fields. If the competitor seems to be confused by things which are confusing to people and learns things which are not confusing, it is more likely to be thinking instead of parroting.
For example, you could explain why no consistent logical system can trust itself, and the ask the competitor if they think their way of thinking is consistent; if they think it isn’t, Ask them if they think that they could prove literally anything using their way of thinking. If they think it is, ask them if they would believe everything that they can prove to be true.
Thinking entities will tend to believe that they can’t prove things which are false, and thus that everything that they can prove is true. Calculating entities run I to trouble with those concepts.
Less meta, one could explain the magical thinking expressed in The Secret and ask why some people believe it and others don’t, along with asking why the competitor does or doesn’t.
To test for general intelligence, you can’t test on the specific skill the bot’s trained in.
I think we might have different definitions of what “general intelligence” is. I thought it meant something like, “being able to solve novel problems in some domain”; in this case, our domain is “human conversation”. I may be willing to extend the definition to say, ”...and also possessing the capacity to learn how to solve problems in some number of other domains”.
Your definition, though, seems to involve solving problems in any domain. I think this definition is too broad. No human is capable of doing everything; and most humans are only good at a small number of things. An average mathematician can’t compose music. An average musician can’t do calculus. Some musicians can learn calculus (given enough time and motivation), but others cannot. Some mathematicians can learn to paint; others cannot.
Perhaps you mean to say that humans are not generally intelligent, and neither are AIs who pass the Turing Test ? In this case, I might agree with you.
(Most) humans posses a certain level of general intelligence. Human groups, augmented by automation tools, and given enough time, possess a much more advanced general intelligence. The “no free lunch theorems” imply that it’s impossible to get a fully general intelligence in every environment, but we come pretty close.
I’ve somewhat refined my views of what would count as general intelligence in a machine; now I require mainly that it not be extremely stupid in any area that humans possess minimal competence at. Out-of-domain tests are implicit ways of testing for this, without doing the impossible task of testing the computer in every environment.
My tests would be: have a chatterbot do calculus. Have a muscial bot chat. Have a calculus bot do music.
To test for general intelligence, you can’t test on the specific skill the bot’s trained in.
Try to teach the competitor to do some things that make sense to humans and some things that do no make sense to humans, from wildly different fields. If the competitor seems to be confused by things which are confusing to people and learns things which are not confusing, it is more likely to be thinking instead of parroting.
For example, you could explain why no consistent logical system can trust itself, and the ask the competitor if they think their way of thinking is consistent; if they think it isn’t, Ask them if they think that they could prove literally anything using their way of thinking. If they think it is, ask them if they would believe everything that they can prove to be true.
Thinking entities will tend to believe that they can’t prove things which are false, and thus that everything that they can prove is true. Calculating entities run I to trouble with those concepts.
Less meta, one could explain the magical thinking expressed in The Secret and ask why some people believe it and others don’t, along with asking why the competitor does or doesn’t.
I think we might have different definitions of what “general intelligence” is. I thought it meant something like, “being able to solve novel problems in some domain”; in this case, our domain is “human conversation”. I may be willing to extend the definition to say, ”...and also possessing the capacity to learn how to solve problems in some number of other domains”.
Your definition, though, seems to involve solving problems in any domain. I think this definition is too broad. No human is capable of doing everything; and most humans are only good at a small number of things. An average mathematician can’t compose music. An average musician can’t do calculus. Some musicians can learn calculus (given enough time and motivation), but others cannot. Some mathematicians can learn to paint; others cannot.
Perhaps you mean to say that humans are not generally intelligent, and neither are AIs who pass the Turing Test ? In this case, I might agree with you.
(Most) humans posses a certain level of general intelligence. Human groups, augmented by automation tools, and given enough time, possess a much more advanced general intelligence. The “no free lunch theorems” imply that it’s impossible to get a fully general intelligence in every environment, but we come pretty close.
I’ve somewhat refined my views of what would count as general intelligence in a machine; now I require mainly that it not be extremely stupid in any area that humans possess minimal competence at. Out-of-domain tests are implicit ways of testing for this, without doing the impossible task of testing the computer in every environment.