Case analysis - A critique on Turing’s original refutations
In this piece, I try to analyze the refutations (against various arguments) given by Turing in his classic article on the ‘Imitation Game’, and determine what still holds given the present state of AI. The following arguments and analysis are with reference to the original Turing Article located at - http://www.loebner.net/Prizef/TuringArticle.html Towards the end, I present my views on the current state of success at passing the Turing Test and what future warrants on the subject.
1.) Theological objection: - To me it seems that the theological arguments have always banked on faith rather than being based on logic and reason. However, this objection may still carry some weight to those who can define and clarify the idea of a soul and then be able to link this idea to thinking process. Without these definitions, linking the thinking process with man’s immortal soul is a highly generalized assumption based on the notion that an all-powerful God is able to somehow put this into effect. However, this very assumption is contradicted later on by claiming that the same God can’t put those souls somewhere else, in this instance - machines. Overall, this objection seems too pretentious to carry substantial weight now. Moreover, Turing’s refutations in the original paper seemed to be quite rational to put this case to rest.
2.) The “Heads in the Sand” objection: - This objection in itself (as stated in the original paper) carries no weight now. However, it is somewhat related to the ethical issues that are now often connected with AI, when we pause considering whether we can develop AI, and ask ourselves - Should we create AI and ponder upon the various implications of advanced / strong AI if it were to co-exist with humankind. But, since the current discussion is about ‘Can machines think’, we would rest this case here.
3.) The Mathematical objection: - Actually, this argument doesn’t really seem like a reason to object the test since it tentatively puts machines only at a disadvantage w.r.t humans and thus this test may be a decent criteria to determine the validity of that objection. Anyhow, as far as computing capacities are concerned, we’ve reached very high orders from what was available back in Turing’s time. Thus, ideally, such machines can be build which would decently respond to the objections in this point.
4.) The argument from consciousness: - In his original paper, Turing has handled this argument from the perspective of feeling as it relates to thinking and he describes this objection as - ‘whether machines can feel that they are thinking‘. Within this context, his refutations seem valid since he links these objections to solipsism (claim that only self can be verified), which he then claims is noone’s win since it makes idea communication difficult.
However, there is a broader context to this argument now, especially after thought experiments like Chinese Room by Searle - http://en.wikipedia.org/wiki/Chinese_Room . These experiments broaden the scope of the argument by relating consciousness to mind, bringing the notion of intentionality & states, and illustrating that machines can’t have minds irrespective of their intelligent behavior.
One may argue that Turing would have reasoned that even the neurons within human brains aren’t conscious in the sense of understanding what they are doing, and it somehow appears that the overall system as a whole is behaving intelligently and there is nothing more to it than appearance. However, since these ideas are not the part of his original paper, these objections would still hold.
5.) Arguments from various disabilities: - This argument is essentially a combination of various objections and expectations each of which are discusses below. For many of these, we would require an exact definition of the word to relate it to AI and for this purpose, we would refer this source - http://www.answers.com : -
- Being kind, friendly, loving: These are abstract words that require contextual definitions. If these are implied by being helpful and useful, then certainly this objection will not hold since there are many machines, softwares etc, that help people automate their work. Applications of AI to advanced robotics has produced many helpful robots - http://www.helpfulrobots.com/ .
However, if we think of kindness, friendship and falling in love from a conscious point of view, then it would again require a mind and intention. It would thus fall into the consciousness argument discussed above.
- Being resourceful: As per our source, resourceful means - able to act effectively or imaginatively, especially in difficult situations. Clearly, most of the existing softwares meet the criteria of efficiency. There are various agent programs that deal with different situations intelligently as well - http://en.wikipedia.org/wiki/Windows_Live_Agents . Imagination is again dependent on how we define it, but overall this criteria seems to be met now.
- Being beautiful: If this concerns external beauty, there are certainly beautiful machines created by careful manufacturing, etc. Internal beauty is again abstract and would belong to the consciousness argument, as in being aware of an internal state.
- Having initiative: Most AI machines or softwares have been created with an ultimate goal to take the initiative from humans and automate processes.
- Having a sense of humour: There have been some efforts towards artificial comedy - http://www.clone3d.net/ .
- Telling right from wrong and learning from experience and doing something really new: Machine learning and causalreasoning are two of the core fields in AI research and have been implemented in many systems. Doing something new can be a direct consequence of machine learning or learning from experience. So, this objection doesn’t hold now.
- Making mistakes: Since AI programs are written by humans, they can be buggy and hence their actual output can deviate from expected. This can be classified as making mistakes.
- Making someone fall in love: This is quite achievable using meticulous and aesthetical design, and giving a pleasing appearance to a useful machine.
- Using words properly: NLP and voice recognition programs are a case in point.
- Having as much diversity of behavior as a man: This objection still holds and would continue to hold until certainmilestones are reached in brain and mind simulation .
- Being the subject of its own thought: Thought requires a clear contextual definition here, but, in general, this could be similar to providing a program source as an input to itself and observe the output. Again, doable and hence the objection doesn’t hold.
6.) Lady Lovelace’s Objection: The gist of this objection can be summed up in one sentence - machines can never do anything really new so as to take us (humans) by surprise and Turing gave a classic refutation to this by claiming that this is equivalent to saying that there is nothing new under the sun since every new discovery or invention is linked to some past event or chain of events. So, basically he tried to give a context to what should be considered as really new and within that context, this objection is clearly not true today. There are programs capable of giving surprises as well as creating unique and new scenarios. A case in point is the origin of Butterfly effect - http://en.wikipedia.org/wiki/Butterfly_effect .
7.) Argument from Continuity in the Nervous System: This argument needed refutation on grounds of fairness in the original paper at the time Turing wrote it. However, now with neuro-computing models in existence, this argument can be made redundant in principle.
8.) The Argument from Informality of Behavior: The crux of the argument is - If each man had a definite set of rules of conduct by which he regulated his life, he would be no better than a machine. But there are no such rules, so men cannot be machines. Turing refutation on this still holds since we still haven’t found the absolute laws of behavior that regulate life, something similar to the M-theory, or the theory of everything - http://en.wikipedia.org/wiki/M-theory .
9.) The Argument from Extrasensory Perception: The argument itself seems pretty weak and containing very less substantiation. There are varied opinions on phenomena like telepathy, precognition, etc, and like the argument the refutation is also pretty weak (telepathy-proof room). Anyhow, some people may still have this objection, however it remains a very subjective issue.
New Issues: -
Inherent problems with the Turing Test and Comprehensive Turing Test:
With the advancement of AI to fields like machine vision and perception, one may demand a more comprehensive test. This is since humans generally don’t use question-answering as a means to find whether someone is able to ‘think‘. We use more simplistic (or elaborate, if we consider machines) methods like sight, sound of voice, etc and our notion of thinking is implied by many other apparent things than just plain response. A case in point are the systems like CAPTCHA - http://www.captcha.net/ . This brings back the old issue on whether the Turing Test is a real test of intelligence at all. It seems that the inherent problem with the test is that a program can fool an interrogator by playing on his psychology. This task is much more easier and doesn’t definitely require intelligence equivalent to the mind, or thought process. A simple re-use of the question-phrases can be used as an effective way of letting an interrogator know that the program is actually a person belonging to a particular genre. A case in point is Eliza - http://en.wikipedia.org/wiki/ELIZA . Of late, there have been similar concerns raised over expertise of judges in the Loebner competition. As far as the current state of success with passing the Turing Test is concerned, it may be noted that Loebner is the only official competition for judging this and most of the effort in AI is actually not focused on passing this test. This current state of AI may not have a one-to-one co-relation with the whether this test has been passed or not. However, current state of AI does give us an idea regarding whether this test can be passed if all effort was directed towards it. The 2007 winner for Loebner award was UltraHal, which is basically a software assistant and was re-programmed to enter the competition so that it could specifically fool the judges (ref the interview here - http://aidreams.co.uk/forum/index.php?page=67 ) . So, again the chance of fooling a judge is subjective and dependent on the skill of judge as described above in the case of Eliza bot. It may be argued that the Turing test has already been passed by some programs since they fooled a set of judges in Loebner, but the credibilty of judges would always be in question and so will be the exact rules and regulations under the competition was held. In all, I would say there’s a chance of about 25% for a machine to pass the test, considering strict Turing guidelines and maybe in next 50 years, we would make substantial advances in allied fields of AI to eventually pass the test satisfactorily, however, this again depends on how much effort is put in actually solving this problem in specific.
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