Postulates to Artificial Intelligence

Gaurav Verma Artificial Intelligence Leave a Comment

This article forms an introductory discussion towards the upcoming concept of Artificial intelligence. This article is not a discussion aimed towards various tools, techniques and available technologies towards AI but defining what intelligence is and how one could identify artificial intelligence.

The core of this article is around defining intelligence and the turing test. The article also brushes topics around intelligence vs consciousness and concludes with an understanding of general AI.

Most of this article will involve thought experiments and present postulates towards the case of machine intelligence.

Let us start with an argument presented in the book “Pensées Philosophiques” by “Denis Diderot” “If they find a parrot who could answer to everything, I would claim it to be an intelligent being without hesitation.”

If this argument is to be extended to machines would a machine that would answer everything as a human being would be intelligent?

If this is intelligence then is human and computer intelligence the same or computer intelligence a simulation? Vs Is a human brain a computer?

Would a machine have consciousness and would it be able to feel? Is consciousness required for intelligence?

A final aspect to interpret intelligence is around dualism, it debates if mind or intelligence is physical or if it has non physical components.

These questions remain largely philosophical and are open to interpretation. They remain as yet unanswered and open to large interpretations.

Allen Turing was one of the first pioneers in the field of machine intelligence with a belief in intelligence being physical. He proposed “If a machine behaves as intelligently as a human being, then it is as intelligent as a human being

This brings us to the definition of an intelligent machine vis  A machine that can solve all problems a human can, this also forms the scope of artificial intelligence. To achieve this one needs to precisely define all aspects of learning and other features of intelligence so that a machine may be able to simulate it.

While above definitions bring us a step closer to comparing machine intelligence and human intelligence they lack precise mechanisms towards comparing the same.

Allen Turing in a paper in 1950 reduced the problem of defining intelligence into a simple question of conversation. The essence of this test is if a human interrogator is talking to either a human or a computer behind closed screens and is unable to distinguish between the human and computer then the said computer is intelligent.

A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460.

 

The new form of the problem can be described in terms of a game which we call the ‘imitation game.” It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either “X is A and Y is B” or “X is B and Y is A.” The interrogator is allowed to put questions to A and B thus:

We now ask the question, “What will happen when a machine takes the part of A in this game?” Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, “Can machines think?”

The consequence of this paper essentially boils down to if a machine can answer to a human as another human would then it may be considered intelligent.

One of the top criticism of the Turing test is the Chinese room test

Let us assume there exists a computer program that accepts input in chinese and produces response in chinese. It further is capable of passing the Turing test and the interrogator believes that the response is from an intelligent human.

However the question posed by Chinese room test is does the computer program understand chinese or is it simulating understanding of chinese? If the machine understands Chinese then it is a Strong AI and if it doesn’t then it is Weak AI.

This argument may be further extended to a human, let us assume that we replace the machine with a human being with an infinite library of all possible questions in Chinese with their answers, if such a human were to obtain a request and map it an appropriate answer, would such a human possess an understanding of Chinese? This brings the question of intelligence vs understanding.

There are further arguments towards the program itself in Chinese test, what if the program in question map and code every neuron in a Chinese brain? Human intelligence works with a limited capacity however building programs executing such simulations virtually have infinite capacity. Would intelligence require functioning in limited capacity and resources?

 

 

While multiple arguments for and against have been made for this test for example

  • Why should intelligence be defined in terms of human capabilities and limitations, should intelligence not be larger than human capacity.
  • Human intelligence is driven by experience, instincts and the unconscious mind that doesn’t follow any rules.
  • The turing test is unable to distinguish intelligence as defined to babies or children.

At the next level of debate is Artificial consciousness and Artificial self awareness. There is active research being done in these subjects however the subject material is beyond the scope of this article.

We wish to end this discussion to form a distinction between specialised AI and general AI. Systems such as machine learning and deep learning produce what is known as specialised AI. These systems are good at one thing they are trained to do for example playing chess or classification of images. Such a system would required to be retrained for a new class of problems.

Artificial General Intelligence is creation of machine intelligence that could do anything that a human can do. This would involve capabilities to solve a large class of problems. This is an active area of research and requires an understanding of human intelligence before it may be synthesized.

 



About the Author

Gaurav Verma

With over 16 years of experience, Gaurav has worked on immensely complex projects in a wide gamut of industries & technologies, having worked with organizations like Sapient & Grapecity among others. A graduate from the Delhi University and Indraprasth University with degrees in electronics and computers, he is currently fascinated by & working on projects involving AI and Machine Learning.

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