Artificial Intelligence

Artificial Intelligence Artificial Intelligence is based in the view that the only way to prove you know the mind’s causal properties is to build it. In its purest form, AI research seeks to create an automaton possessing human intellectual capabilities and eventually, consciousness. There is no current theory of human consciousness which is widely accepted, yet AI pioneers like Hans Moravec enthusiastically postulate that in the next century, machines will either surpass human intelligence, or human beings will become machines themselves (through a process of scanning the brain into a computer). Those such as Moravec, who see the eventual result as the universe extending to a single thinking entity as the post-biological human race expands to the stars, base their views in the idea that the key to human consciousness is contained entirely in the physical entity of the brain. While Moravec (who is head of Robotics at Carnegie Mellon University) often sounds like a New Age psychedelic guru professing the next stage of evolution, most AI (that which will concern this paper) is expressed by Roger Schank, in that the question is not ‘can machines think?’ but rather, can people think well enough about how people think to be able to explain that process to machines? This paper will explore the relation of linguistics, specifically the views of Noam Chomsky, to the study of Artificial Intelligence. It will begin by showing the general implications of Chomsky’s linguistic breakthrough as they relate to machine understanding of natural language. Secondly, we will see that the theory of syntax based on Chomsky’s own minimalist program, which takes semantics as a form of syntax, has potential implications on the field of AI. Therefore, the goal is to show the interconnectedness of language with any attempt to model the mind, and in the process explain Chomsky’s influence on the beginnings of the field, and lastly his potential influence on current or future research.

Chomsky essentially founded modern linguistics in seeking out a systematic, testable theory of natural language. He hypothesized the existence of a language organ within the brain, wired with a deep structured universal grammar that is transmitted genetically and underlies the superficial structures of all human languages. Chomsky asserted that underlying meaning was carried in the universal grammar of deep structures and transformed by a series of operations that he termed transformational rules into the less abstract surface structures that was the spoken form of the various natural languages. He showed also that mental activities in general can and should be investigated independently of behavior and cognitive underpinnings. This idealization of the linguistic capability of a native speaker brought Chomsky to his nativist, internalist, and constructivist philosophical views of language and mind. This concept of generative grammar could be seen as a ‘machine’, in the abstract Turing sense, that can be used to generate all the grammatical sentences in a given language. Chomsky was searching for a formal method of describing the possible grammatical sentences of a language, as the Turing machine (more below) was used to specify what was possible in the language of mathematics.

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Chomsky’s transformational generative grammar (TGG) possessed the most influence on AI in that it was a specification for a machine that went beyond the syntax of a language, to their semantics, or the ways that meanings are generated. An ambiguous sentence like I like her cooking or flying planes can be dangerous could have a single surface structure from multiple deep structures, just as semantically equivalent sentences involving a transformation from active to passive voice or the like, could have different surface structures emerging from the same deep structure. Computational linguists and AI researchers saw that these rules, once understood, could be applied, or mechanized, with a formal mathematical system. Here, natural languages were strings of symbols constructed to different conventions, which needed to be converted to a universal human ‘machine code.’ From a computational viewpoint, language is an abstract system for manipulating symbols; the universal grammar could be purified in the sense of mathematics, in other words, being independent of physical reality. Semantics in this view would just be an application of the abstract syntax onto the real world. Chomskyan linguistics, as we shall see further on, does not acknowledge any application of syntax outside the internal realm of mind, semantics being one of the components of syntax.

The primary difficulty in AI work, and that which binds it so closely with philosophy, cognitive science, psychology, and computational and natural linguistics, is that in order to build a mind, we must understand that which we are building. While we understand the external functions which are carried out by the brain/mind (age old mind/body problem), we do not understand the mind itself. Therefore we could (though this is exceedingly difficult and has not yet been done fully) imitate the mind (or language) but not simulate it. That is not to say that this is impossible in the future, but rather that the current paradigm must be transcended and an entirely new way of understanding the mind and machines must be put forth. A computer imitating intelligence would be like an actor who plays someone smarter than himself, whereas simulation is only possible where there is a mathematical model, a virtual machine, representing the system being simulated.

Research with the goal of imitation is called weak AI and that with the goal of simulation is called strong AI. And so, as set forth by Chomsky, it is the goal of computational linguistics to create a mathematical model of a native speaker’s understanding of his language, as it is the goal of AI to create a mathematical model of the mind as a whole. This analogy is imbalanced in that computational linguistics is not a separate discipline, but rather could very well be the key to AI. In addition, the relationships between computational linguistics and linguistics, or of AI and cognitive psychology (or philosophy of mind) are not of dependence of one upon the other, but of interdependence. If AI researchers were to create a functional model of the human mind in a machine, this would provide (perhaps all-encompassing) insight into the nature of the human mind, just as a complete understanding of the human mind would allow for computational modeling.

The understanding of the interrelatedness of these fields is essential because in the end it will most likely be through a synthesis of work in the various fields that progress will be made. To return to the specifics of computational linguistics, we see that while Chomsky’s work was vastly responsible for spawning the modern field, the idea of natural language understanding (more on this below) has been intricately tied to AI since Alan Turing posed his Turing Test in 1950 (which, incidentally, he predicted would be passed by the year 2000) . This test, which would supposedly determine that a machine had attained intelligence, is essentially that a computer would be able to converse in a natural language well enough to convince an interrogator he was talking to a human being. Yet, as we discussed above, there is a great difference between a computer so extensively programmed as to be able to imitate linguistic ability (which in itself has thus far proven extremely difficult if not impossible) or another conscious cognitive function, and one which simulates it. For example, a computer voice recognition system (one far more perfected than those available in the present day) which has advanced pattern-recognition abilities and can respond to any natural language vocal command with the proper action, still would not be said to understand language.

The true sign of AI would be a computer who possessed a generative grammar, the ability to learn and to use language creatively. This possibility may not actually be possible, and Chomsky would be the first to argue that it wouldn’t, yet an examination into his more recent work in his minimalist program shows some strands of thought whose implications are far outside of his rationalist heritage, and which could be important to AI in the future. Attempts at language understanding in computers before Chomsky were limited to trials like the military-funded effort of Warren Weaver, who saw Russian as English coded in some strange symbols. His method of computer translation relied on automatic dictionary and grammar reference to rearrange the word equivalents. But, as Chomsky made very clear, language syntax is much more than lexicon and grammatical word order, and Weaver’s translations were profoundly inaccurate. Contrary to their original speculations in the dawn of the AI age (50’s-60’s), the most complex human capabilities have proven simple for machines, while the simplest things human children do almost mindlessly, such as tying shoes, acquiring language, or learning itself, prove the most difficult (if not impossible).

Numerous computer language modeling programs have been created, the details of which are not essential to the topic of this paper and will not be delved into, yet none as of yet can approach the Turing Test. Much difficulty arises from linguistic anomalies like the ambiguities mentioned above, as in the old AI adage time flies like an arrow; fruit flies like a banana. The early language programs, like Joseph Weizenbaum’s ELIZA (which was able to convince adult human beings that they were receiving genuine psychotherapy through a cleverly designed Rogerian system of asking leading questions and rephrasing important bits of entered data) had nothing to do with modeling of language. Rather, these were programs which were programmed to respond to input with a variable output of designed speech with no generative grammatical or lexical capability. Early attempts at computational linguistics, under Chomsky’s influence, attempted to model sentences by syntax alone, hoping that if this worked, the semantics could be worked out subsequently, and only once, for the deep structure.

However, as Chomsky showed much later on, semantics is part of syntax (the most important part), and thereby could not be dealt with post-syntactically. Not unsurprisingly, the only linguistic area where computers thus far have shown considerable ability is the area that humans find the most difficult, whereas the simplest human linguistic abilities remain elusive. Sentences known as recursive, or left or right-branching such as The monkey that the lion who had eaten the zebra wouldn’t eat ate the banana, have an infinite capacity for embeddings, allowing for the vastly superior memory of the computer to be more effective in parsing them. Understanding that Chomsky’s original breakthroughs (those of Syntactic Structures and his 60’s work) had profound impact on Artificial Intelligence, the remainder of this paper will speculate on the potential impact of his minimalist program and the nature of what I will call the syntactic mind. The premise of the argument is presented by SUNY Professor William Rapaport in his essay How to Pass a Turing Test: Syntactic Semantics, Natural Language Understanding, and First Person Cognition, as a rebuttal to John Searle’s Chinese Room argument, which Rapaport describes as: 1) Computer programs are purely syntactic. 2) Cognition is semantic.

3) Syntax alone is not sufficient for semantics. 4) Therefore, no purely syntactic computer program can exhibit semantic cognition. Rapaport responds by saying that syntax is sufficient for semantics, and if you accept that, then you discover that a purely syntactic computer program can exhibit semantic cognition; in other words, if semantics can be incorporated into syntax, then the computer program can simulate the cognitive mind. This is a bold statement, so let’s see how it is derived from Chomsky’s work. Syntax is defined as the relations among a set of markers (Rapaport refrains from calling them symbols as symbol implies an inherent connection to an external object), and semantics is the relations between the system of markers and other things, (their meanings).

His argument claims that if the set of markers is merged with the set of meanings, then the resulting set is a new set of markers, a sort of meta-syntax. The mechanism that the symbol-user (native speaker) uses to understand the relation between the old and new markers is a syntactic one. The simplest way to put all this would be that semantics must be understood syntactically, and is therefore a form of syntax. The crux of the argument is that a word (for example tree) does not signify an actual external tree-object, but rather signifies the internal representation tree found in the mind. This idea goes to back to Chomsky’s Lectures on Government and Binding where he introduces Relation R, elucidated by James McGilvray as reference, but without the idea that reference relates an LF [Logical Form, or SEM, semantic form] that stands between elements of an LF and these stipulated semantic values that serve to ‘interpret it’.

This relation places both terms of Relation R, LF’s and their semantic values, entirely within the domain of syntax, broadly conceived;. . .They are in the head. Chomsky’s internalism goes back to the Cartesian view that all sensory input is subjective and therefore nothing can be known outside of the mind. Therefore language cannot refer to external objects, but rather, either to its internal representations of them based on sensory input, or to concepts (like Unicorns) which have no external source to represent. So Chomsky’s internalism and nativism allow for the syntactic phrase in its semantic interface an internally constituted perspective that can play a role in individuating, and even constructing the things of a world.

The implications for AI lie in that the purely syntactic symbol manipulation of a computational system’s knowledge base suffices for it to understand natural language. The end-pursuit of strong AI is to model or simulate human consciousness. If syntax exists only inside a larger mental meta-syntax (rather than semantics) then the human consciousness is a world of signifiers, our mental reality suffers a permanent disengagement from the signified. It is not really the world which is known but the idea or symbol. . ., while that which it symbolizes, the great wide world, gradually vanishes into Kant’s unknowable noumena.

If we take the Chomsky/McGilvray idea of broad syntax one step farther, philosophically, we find that the labyrinth of signifiers which is the syntactic mind exists in a world in which there is no concept outside the mechanisms of representation. Strangely, the post-structuralist Jacques Derrida, who Chomsky despises, says the same thing. At the origin of language in the absence of a center of origin, everything became discourse. . .that is to say, when everything became a system where the central signified, the original or transcendental signified, is never absolutely present outside a system of differences.

The absence of the transcendental signified extends the domain and the interplay of signification ad infinitum. What Derrida is talking about by a transcendental signified is the semantic, external reality to which syntax refers. It is transcendental in that it transcends syntactic representation, it transcends the syntactic mind. The internalist view does not deny the existence of the external world, rather, when McGilvray refers to constructing the things of the world through language, it is the world of human consciousness to which he refers. In this theory, it is through Chomsky’s I …

Artificial Intelligence

As we get closer to the twenty-first century, we find that soon a computer will be another common household appliance. As the television was introduced in the 1950’s, it soon became an essential part of everyday life. It is now found in every home and an importance source for entertainment and for gaining information. In the next couple of years, the same will be said for computer. It is fast becoming as essential part of our everyday life. With the Internet becoming an important resource for gaining information with the touch of a button. Yet, this is just the beginning of the computer age. We now use components from computers to run other household appliances such as: microwave ovens, phones, alarm clocks, VCRs, and even television themselves have change to incorporate computer components. We even have cars with computers installed within them. Soon everything in home will be run, in some way by a computer. Yet, with the advancement in computers, engineers are still trying to find a way to create Artificial Intelligence. This would truly take the computer to the next level, but creating something of this magnitude is extremely difficult. Lets first take a look at what we have now.

As we look into businesses, we find that robotics has become as important asset for companies to stay in business. Robots can produce products more rapidly and more efficient than the human work force. Though robots cannot totally replace people in all work fields, they help in limiting mistakes, and boosting productivity. Still, robots have their limitations.

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To look at these limitations we first must know how computers work. Perhaps more than anything else, are the multi-purpose functionally and its ability to perform hundreds of very different tasks, which makes computers an essential part of our lives. Every computer has five functions: input, output, processing, information holding, and control. Before we get into the five functions, lets first take a quick look at the computer language. Computer language is binary number, basic 0 and 1. To look at this simply lets look at a light switch. A light switch, you could say, is also binary. On, will make the bulb receive electricity, which would make it illuminate. Off, will stop the electricity from getting to the bulb which would make it stop illuminating. This is a simple way of describing the binary language of a computer, but of course theres more to it than that. Though it uses binary language, it must also be able to understand human language. Therefore, computers must convert the alphabets and numbers into binary language.

To understand how computers convert the alphabets and numbers into binary language, we must explore what is called bytes. A bit is either 0 or 1, which is as I explained earlier the same as binary language. A byte is a series of eight bits, which converts the alphabets and the number series into binary language. For example, lets take the letter A, notice I use capital a, because both uppercase and lowercase has its own binary code. The computer knows the letter A as 01000001. Therefore, when you type the letter A in the computer, the computer is sent this code in place of the key that you typed. Converts this code into A, and displays the A on your monitor. This is just to give you a simple example of what the computer read when you type on the keyboard. Keep in mind that each letter of the alphabet (both upper and lowercases) has its own binary code, as do the numbers from 0 to 9, and symbols such as +, -, and ;, to name a few.

To get back to the five functions of the computer, lets explore each one. First we explore input. Input is a set of instructions, which tells the computer what to do. Such instructions are known as the programs. Input is also anything you type on the keyboard, click with your mouse, or scan on your scanner. However, without the program, anything you type on the keyboard is useless, and the same goes with any other input device. When you type on your keyboard, the program in the computer instructs the computer to convert these keyboard inputs into binary codes. For example, if you type the letter A, the program in the computer tells the computer that A equals 01000001. This way the computer understands what you are saying. In return, the computer sends out an output. An out is displayed on your monitor. Output is information, which is run through the computer and displays the result on your monitor. When the computer is running this calculation this is called, processing information. So when you press the key A, the computer receives 01000001, checks to see what is in the space 01000001, retrieves whats in that space and places it on the monitor. This is input, output, and processing.

So far, we covered three of the five functions of the computer. Now lets look at the information holding. This is a very important aspect of your computer. Without this, your computer would not be able to perform its tasks. There are basically two main information-holding devises, which are RAM and ROM; read-only memory is your programs. The place in your CPU where your instructions are held. This tells your computer what to do. RAM, random-access memory is basically a place to store information for a short period of time. RAM, is where your input is stored until the computer finds out what to do with it. When you type in A the controller goes store the information in RAM, then goes into ROM for instructions. Once ROM tells the computer where A is located, (that key is located in 01000001) the computer retrieves the information and then goes to RAM, (this is what you looking for) and displays it on the monitor. This is a very simple explanation of how your computer works. It is just to give an idea of the functions of the computer.

Now that we have a basic idea of how computers work, let us look into robotics. Robots are used for many tasks; some which are dangerous, some that people cannot accomplish easily, and some, which are just tedious, work. Still these tasks are important to a company to stay in business. Lets us look at an auto manufacturer for example. Let us say you needed to install seats into a car. You have an employee doing the tasks. The seats of a car are heavy. It would take a person much time to get the seat, bring them to the car, and place them in the appropriate place before they weld them to the body of the car. With a robotic arm, the tasks could be done in less than half the time it takes the individual to do it. Cutting down not only time but also the amount of money it took to build the car. With the cut down in time, more cars could be built in a day with the robot than with people alone. Now take into account the windshields, the different body part of the car such as doors, hoods, well you get the picture. You now get an idea of how important robots are to a car manufacturer. Car manufacturers, but also TV manufacturers, the Post Office, and the list go on.

Once you get an idea of how robots are programmed, you will see how difficult it is to create Artificial Intelligence. First we must explore how robots are programmed. Earlier you got a brief explanation of how programming works. Of course programming a robot is much more complicated. More complicated than you could imagine, because machines are stupid. For example, if you wanted a robot to open a door. If you were talking to a person, you would say open the door please, and the person would open the door. A robot would not know what open the door meant. You would have to explain the process to the robot through its program. Something like this:
Approach the door.

Stop twelve inches from the door.

Lift up your left hand and place it on the doorknob.

Grip the doorknob.

Turn the doorknob 45o radius.

Stop.

Pull the doorknob.

Take one step back.

Pull the doorknob.

Release the doorknob.

Etc.

Etc.

Well you get the picture.

Even these instructions might be a little vague fro a robot to understand. If now the robot knows how to open the door, it wouldnt know how to close the door. You would have to go through the same programming process of how to close a door. In other words every tasks which is done by a robot is do to its programming. A robot cannot deviate from its program. Some bosses would love to have employees like this, but like the saying goes Be careful what you wish for.

When we speak about Artificial Intelligence, we are talking about computers that act and think like people. You might say that should be simple, but its not. Until now computers do as there program instructs them to do. If you program a computer to add 1 + 1 equal 3, thats what the answer will equal to all the time for as long as the program stays the same. Not until you change the program will 1 + 1 not equal 3. The computer cannot go and find the information for itself.

Computers cannot do more than one thing at a time. It could have hundreds of programs to do different task, but it could only do one task at a time. Unlike the human brain which could process more than one thing at a time.

In addition, computers cannot learn on their own. As I said before computers only do what its program allows it to do. When engineers try to design Artificial Intelligence, I believe that this is where the break through is beginning.

There is an attractive similarity between computers and humans. It is almost impossible to resist the temptation to compare a CPU and memory to the human brain and I/O devices to our senses. Information flows into our memory through sight, sound, touch, taste, and smell. Our brain remembers the information, decides to take action, and send commands to our muscles so that we speak or move around. This analogy is the origin of the term electronic brain.

Assuming that things are alike because they look alike is a common error. In this case, although there are similarities in structure, computers and humans operate in fundamentally different ways.

The human brain, though operates similar to the CPU, it is in many ways different. Unlike the computer, when the brain receives input from different places of the body, different parts of a brain process these inputs. This is the major difference between the brain and a CPU. We have a brain, which can perform many different tasks at one time. Not until engineers create a CPU with the capability to perform multiple tasks, and learn on its own, will they be able to create an Artificial Intelligent being that could be compared to people.
Computers and Internet