The Turing Test: Not enough?

 

     Of late, there have been declarations of Artificial Intelligence (AI) becoming sentient. And even louder protestations that it isn't true. Within computer science, there has been an assumption that this can be evaluated by use of what is called "The Turing Test". Created by Alan Turing, a British mathematician and considered to be a founding person in AI, in 1950 -- it was initially called an imitation test. The idea was that if you were unable to distinguish the conversation of a program from the conversation of a person the program could be considered sentient.

     This was in 1950. People conversed on a regular basis in 1950. The criteria of being able to distinguish a person from a program, by use of analysis of conversation, seemed reasonable. Forward the clock to 2022 and that criteria no longer seems to be sufficient. For those who say that AI has reached the criteria set out by Alan Turing -- they are probably correct. There are programs that speak language BETTER than an average person in that language. According to the Turing test, they are sapient. But, according to our current experiences of programs, machine learning, and AI -- it seems that it would be hard to consider them to have had a passing grade for sentiency.

     Which leaves us to the question, once again, of what is sentience? Some of the recent declarations indicate the appearance of emotions, desires. Are these what distinguish sentiency from mechanical responses? Of course, one can move to the religious, spiritual, and theological and say that what we are searching for is a soul. But, if it is a matter of a soul then we are in even worse shape for evaluation. There is no consensus as to what a soul is. If it does exist, where does it reside? If it does exist, is it immortal or connected to something associated with a living mind and only in existence as long as that mind and body exist?

     Science fiction and fantasy -- those genres of "what if" -- have approached such a subject for many decades. But general agreement, even less the matter of consensus, is nowhere to be found.

     If we use the criteria of a desire for survival and a desire for progeny then the state of AI has not been achieved. But, if those are to be criteria, should a goal of reaching such be striven for? Mary Shelley's Frankenstein reflects many matters: human hubris, the difficulty of accepting others, fear of the stranger (xenophobia). But, as seen in the dystopia of the Terminator series, a desire for survival and progeny coordinated with a superiority in speed and execution may prove to be an ultimate competitor to the human species.

     So, let's move back to the start. If we use the Turing Test, alone, as a criterion for sentiency -- we are probably there. If that is not sufficient, what qualities should be measured -- and how should they be measured? And, in some situations, perhaps creating that which meets such criteria may be something that would be a mistake to achieve?

AI/ML/DL/Life : Learning from Mistakes and Feedback

 

     My sons are presently in an "online only" situation for their college classes. In this, they are certainly not alone. We are fortunate that the college has had a good preventative routine that has kept on-campus infections considerably below the state average -- though we are all looking forward to being able to directly interact with teachers and classmates.

     Teachers have had a very difficult time in dealing with abruptly changed circumstances. Some have handled it well -- others not so well. All three of my younger sons have had classes (one has had three such classes) that accepts homework, quizzes, and tests -- AND DOES NOT GET ANYTHING BACK.

     Of course, I can't be certain how these teachers handled their classes when they were face-to-face. But I share in the frustrations of my sons when they have no idea what they have done right or what they have done wrong and need to correct. They cannot correct mistakes, they cannot improve and, in total, they cannot learn and the course has been rather hit-or-miss for value and the teachers, themselves, did not add any value. It would have been just as valuable, much less expensive, and much less frustrating to have taken a different online course with prerecorded content.

     Feedback is absolutely required for growth. Sometimes that feedback is direct, and physical. The old saying of letting the child burn themselves on the stove once and there won't be a second time is true (though we would all prefer that they learn before hurting themselves). Or, if you back up over a cliff while taking a selfie -- you will hopefully learn a valuable lesson if you survive. Some situations give direct feedback of a very serious nature.

     With the above example, a good teacher will cover the material on the material submitted. The best feedback is individual corrections, feedback, and (in those rare cases when it is possible) tutoring to overcome inaccuracies and problems. The next best is class coverage of the material, indicating the correct answers and, if time, how they were achieved. The largest problem with that is that, for online courses, the student may not have a copy of what they had submitted. A third, but still marginally acceptable, method of feedback is to post the questions and answers from which the student can hopefully learn.

     More often, the feedback is not direct. In this case, it must be interpreted. People start avoiding you after you have made a careless, or thoughtless, comment. Your gas mileage starts to decrease after you have neglected maintenance for too long of a period. You start panting, and wheezing, and may have chest or arm pains after years of not following a healthy, balanced, diet. There are a lot of such indirect feedback situations.

     With indirect feedback, questions and expert advice is often desirable. If your social situation has changed, ask a friend who will still talk with you and give honest feedback. If your gas mileage is dropping, take the car to a mechanic (of course, if you had done that regularly then this special visit might not have been necessary). You should have regular tests and physician checkups to make sure that your body is still on the right path for continued health.

     All of these things are necessary for people to learn, and grow. The same thing is true for all sub-specialities under the umbrella of "artificial intelligence" to one extent or other. Deep learning, which is a specialty under machine learning, tries to create its own feedback loops without human interactions.

     Some of us remember when a famous AI program competed on the quiz show "Jeopardy". If it got an answer correctly, it got it very quickly. But, if it didn't answer correctly, the answer was often totally wrong. The human audience didn't always see any correspondence between clue and answer at all (presumably the algorithms did see such). I am sure progress has been made since then but it is something to be constantly aware of when the human element is removed from the feedback loop.

      Neural networks are often used within deep learning. They are designed to work according to the way brains work -- by strengthening links between portions of data based upon usage and correlation. A correct correlation between data makes the link stronger and an incorrect correlation between data makes the link weaker. The definition of correct and incorrect is left to the designer/programmer/counselor. And the definition of correct versus incorrect may also vary depending on the status, and worldview, of the person creating the definition.

     The more interaction with a human for feedback, the better the chance of appropriate growth of an AI's ability to react -- but the greater the chance that it will adapt in the ways the human does. Somewhat a matter of creating a "clone" which thinks/reacts as the person who is giving the feedback. But, without the feedback from humans, the greater the chance of correlation between things that does not easily correlate with the real world.

     What are your thoughts? Create an autonomous system that self-corrects and brings in its own directions of feedback? This allows for faster growth and adaptation but only initial control. Or feedback from humans that slows down the process but increases relevancy and perhaps bias?