The General Artificial Intelligence Problem Solver

One of the first attempts of artificial intelligence was in 1956. Alan Newval and Herbert A. Simon created a computer program that he called a general problem solicitor. This program was designed to solve any problems that can be presented as mathematical formulas. One of the major parts of the general problem solve was called by the Newle and Simon called the physical symbol system concept (PSSH). He argued that the symbols were the key to general intelligence. If you can get a program to add these symbols adequately, you will have a machine that will behave similar to human intelligence.

How we interact with the world, symbols play a big role in it. When we see a stop sign, we know how to stop and want to see traffic. When we see the “cat”, we know that it is a small crying kitten that smells. If we see a chair, we know that it is an object to sit. When we see a sandwich, we know it is something to eat and we may also feel hungry. Newval and Simon argued that by making sufficient quantity of these connections, machines will behave like us. He thought that an important part of human argument is just to add symbols – that our language, thoughts and concepts are just wide groups of interconnected symbols.

But not everyone agreed with this idea. In 1980, the philosopher John Siral argued that just adding symbols cannot be considered intelligence. To support his argument against the claim that computers think or have the ability to be able to think at least someday, they created an experiment called Chinese room arguments. In this experiment, imagine yourself, an English-speaking, locked in a window-free room, with a narrow slot on the door through which you can pass the note. You have a book filled with long lists of statements in Chinese, a floor covered with Chinese letters and instructions that if you are given a certain order of Chinese letters, you have to answer with a statement related to the book.

Outside the room, someone who speaks fluently Chinese, writes a note on a sheet of paper and gives it to you through a slot on the door. You don’t know what is written in it. You go through a tedious process of looking for statement in your book and in response to the sequence of Chinese letters on the note. Using letters from the floor, you stick the statement on a sheet of paper and give it to the person through the slot to the person who gave you the original message. The original Chinese speaker who gives you a note believes that both of you are talking and you are intelligent. However, Ciral argues that it is far from intelligence, because you cannot speak Chinese, and you have no understanding of the notes being received or sent.

You can make a similar experiment with your smart phone. If you ask Siri or Cortana how she is feeling, she would say that she is feeling fine, but does not mean that she is feeling fine or feeling anything. She does not even understand the question. She is just matching your question with answers that are considered acceptable and choosing one of them. A major defect of matching symbols is one that is referred to as a combination explosion – the rapid growth of symbol combinations that make the match more difficult. Just imagine how many types of questions people can ask and the same question can have different answers. In the example of the Chinese room, you will have a steady growing book of potential input and output, which will take you more and more time to get the correct answer. Despite these challenges, the symbol matching remained the foundation stone of AI for 25 years. However, the symbol matching AI has been unable to keep pace with the increasing complexity of applications. Initial machines had trouble matching all possibilities, and when they could do so, the process took a lot of time.

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