Can computers think? What is thinking, exactly, and how does one recognize it? What is the correlation, if any, between thinking and consciousness? Could a computer be conscious? For years, science fiction writers have used these questions as material for their stories, from domestic robots who do all the housework to automated spaceships colonizing and mining the galaxy in the name of industry.
Meanwhile, the scientific community has been slowly but steadily moving towards the point where the answers to these questions become visible. Predictions for the future in the field of artificial intelligence have traditionally been overly optimistic, but as computers become increasingly adept at simulating reason, the coming century will inevitably bring with it new ideas about what it means to be human.
The practice of ruminating on the nature of thought and consciousness is nearly as old as the human race itself. Discussions of free will and fate can be found in nearly every religion and philosophy on the face of the earth — are humans in control of their actions, or is the universe merely an exceedingly complex machine, all of whose actions are pre-determined? Recently, the development of quantum theory has suggested what to many seems the worst alternative — that the universe is deterministically random, and nothing can be either controlled or predicted.
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While this line of reasoning brings to mind such glib responses as asking why criminals such be punished, if they have no control over their actions (the answer, of course, is that judges can’t control their own actions either), it also leads to a deeper understanding of the issues involved in automated reasoning and the possibilities therein. No easy definition of thought exists, and one can only experience a single version of it in a lifetime.
No one can even be sure that the others he or she sees are, in fact, conscious beings. It is difficult to imagine what a computer would have to do to prove that it is conscious, or what a seemingly rational human would have to do to prove that he or she is not.
The formal study of artificial intelligence — intelligent action exhibited by non-organic objects — had its inauspicious beginnings nearly 170 years ago with Charles Babbage’s conception of his Analytical Engine in 1833, an early prototype of the programmable computer (Johnson 61). Lady Ada Lovelace, upon learning of the engine, remarked that it would be possible to communicate with it as one would with a human if only the cardboard cards that stored its instructions were punched the right way.
She went on to study the capabilities of algebraic manipulation that such a machine would have, and experimented with techniques of writing programs, earning her the distinction of being popularly known as the first computer scientist. Although engineering difficulties prevented Babbage from completing the engine, it paved the way for further developments in the field of numerical computation. While the Analytical Engine was the first attempt at instantiation of what would now be known as a computer, it was by no means the first conception of one.
People had long recognized the difficulties inherent in the study of mathematics and formal logical systems without any kind of automation and used simple tools such as the abacus to remedy them. In 1617, mathematician John Napier developed Napier’s Bones, a collection of digits printed on the bone that was the. the forerunner of the slide rule (Kurzweil 161).
In what is the first reference in recorded literature to something recognizable as artificial intelligence today, a dialogue by Plato recounts Socrates asking Euthryphro for a fail-proof algorithm to determine the nature of piety (Dreyfus 67). An algorithm, or finite collection of discrete steps leading to a determined end, is at the heart of every computer program.
History of Artificial Intelligence
Despite extensive research into the theory of computers in the late 19th and early 20th centuries, electronic programmable computers were not successfully built until the early 1940s. These computers, the most notable of which was the ENIAC, consisted of large banks of vacuum tubes and were used by the US Military to calculate missile ballistics tables. Computers were used as essentially nothing more than huge number crunchers for ten years, until they gained interest among the scientific community.
In 1950, British mathematician Alan Turing published his paper “Computing Machinery and Intelligence,” in which he outlines his idea for a ‘Turing test’ to determine whether or not a computer is intelligent. Haugeland describes the test as a game in which a person communicates with two subjects, one human and one computer.
Both subjects attempt to make the tester identify them as human. When Turing says, the tester is able to correctly identify the human no more than half the time, the computer has won and should be thought of as intelligent (6). Turing’s previous work in the 1930s had been pivotal in laying the groundwork of artificial intelligence; Turing and Alonzo Church proved independently that Turing machines, the simplest possible computers, were capable of solving any algorithm, including theoretically all those that go on inside the human head, if given enough time and memory.
The first practical efforts at creating an intelligent program began in the 1950s as well, primarily with game-playing computers. The first notable chess program, MANIAC., was completed in 1956 by Stanislaw Ulam at the scientific research center in Los Alamos that had, 15 years earlier, spearheaded the creation of the atomic bomb. According to chess master Alex Bernstein, MANIAC played a “respectable beginner’s game” and was occasionally able to beat an opponent with little experience (qt. in Hogan 103).
In the field of checkers, Arthur Samuel developed Checkers Player as a fund-raising effort for IBM. Although Samuel admitted that he didn’t particularly enjoy checkers, his program won the Connecticut state championship before being beaten by Parslow, the program developed at Duke University that would eventually beat the world champion (Hogan 102).
Also in 1956, Logic Theorist was created by Allen Newell, J.P. Shaw, and Herbert Simon. The program used a recursive search technique to find proofs for mathematical propositions and was able to come up with several completely original proofs of some of the theorems in Principia Mathematica, a seminal book of mathematics.
In the following year, Newell, Shaw, and Simon broadened their approach and attempted to create a program that would accomplish the same sorts of tasks as Logic Theorist in the realm of the real world. The result, the General Problem Solver, proved unable to solve any but the simplest problems (Kurzweil 199). The problems of General Problem Solver, and of many other attempts to increase the practical ability of artificial intelligence, will be discussed in the next section.
Artificial intelligence became big business in the 1960s and 1070s as development trends emphasized specialization overgeneralization. Programmers realized that imparting all the information a program would need to function acceptably in a real-world situation was far beyond the scope of their ability, but that it was relatively easy to encode large amounts of knowledge about one specific discipline. Programs that used this method became known as expert systems and were particularly useful in the fields of medicine and technical support.
Expert systems used books of rules programmed by human experts in the subject to ask and answer simple questions in an effort to locate the cause of any given problem and propose a solution. An expert system meant to include all knowledge possessed by an average adult was begun at Stanford University in the mid-1960s, but it is still in the primary development stage 35 years later. Due to their infallibility and expendability, expert systems often proved more effective and reliable than their human counterparts within their narrow areas of knowledge.
This specialization occurred in other fields of artificial intelligence as well. Huge amounts of data from thousands of widely disparate sources were collected and stored each day, and some way of picking out meaningful patterns from among the trillions of bits of information was needed. Data mining programs were the answer to this dilemma, but here too it became evident that a general-purpose pattern recognition system such as the human eye would be impractical, and individual solutions were developed for applications such as financial analysis and cataloging of astronomical objects.
This trend has continued throughout the 1990s, and nearly every computer program on the market has some amount of what would 50 years ago have been termed artificial intelligence. Many of these have become quite competent within their specific ranges of knowledge, but the drop-off of ability once they reach the edges of this knowledge is complete and immediate.
The result has been that a rising level of intelligence and seeming awareness by computer programs has become expected and is taken for granted. Several practitioners of artificial intelligence have speculated that full general intelligence will never be realized on a computer only because once a computer becomes good at something it is no longer regarded by the public as an activity that requires intelligence.
At the beginning of the new millennium, the world champion chess player is a computer; robotics and artificial vision have advanced to the point that in a few years the world ping-pong champion will, in all likelihood, be a robot; a robot has explored Mars, beaming pictures back to NASA headquarters, long before any human will ever walk on the Red Planet; an electronic paper-clip painlessly guides confused users through the operation of Microsoft Word, the state-of-the-art in word processing (and several hundred thousand times larger in terms of memory than the first word processing programs). Why is it that, throughout all of this, a sense of reason remains conspicuously absent? Will these patchwork solutions ever be fit together to provide a well-rounded intelligence? Is there anything that will remain elusively out of the grasp of computers?
Approaches to Artificial Intelligence
When computers were first developed, it was clear that they possessed huge mathematical capabilities. Their speed and accuracy at complex calculations had never before been seen. Many researchers in the field regarded it as just a matter of time before computers surpassed humans in the area of intelligence as well. For years, machines had far outstripped humans in strength and stamina. Now computers made it clear that humans’ ability in the calculation was nowhere near what could be achieved.
Why should general reason and common sense, which seemed to be acquired by humans nearly effortlessly, be any different? What the initial scientists and programmers failed to realize was that the shift from the kind of logical rationalism displayed by a computer to the creative associationism exhibited by humans is not a natural extension of similar concepts but a complete paradigm shift, and that success in one, however astonishing, does not portend success in the other.
Since the 1940s, computers have infiltrated themselves within human society, but many misconceptions continue to hold sway. The information has become the lifeblood of the modern world, just as factories were earlier and just as land was before that. A person with a computer in the early 21st century is potentially infinitely more powerful than any other person in the millions of years of human history. Still, computers were designed to be good at what humans are bad at, not at what comes naturally, and they work accordingly.
A bulldozer is obviously much better than any human at some things, most notably at moving large amounts of dirt from place to place, but if one asks it what the fourth root of 1,783 is, it won’t even venture a guess. The same holds true for computers. They can answer almost (6.49812162563) instantaneously the above question but are notoriously difficult beings with which to have an intelligent conversation.
Efforts to simulate human thought patterns by traditional means in computers are misguided. The hardware of all electronic computers today consists of the same general structure, known as the von Neumann architecture. Data and instructions are located at specific discrete addresses in memory and are located and processed one at a time by a central processor. Variations such as parallel processing, to execute multiple instructions per cycle, or virtual memory, to store more data than the physical amount of memory allows for, are all minor changes to this basic design (Artificial Intelligence 104).
To utilize this structure, programs must be clearly delineated as a set of logical, definite steps, and human reasoning processes, for the very reason that they are acquired and assimilated in the human mind so effortlessly, resist this type of delineation. (This is not to say that it could not be done; assuming no metaphysical basis for consciousness exists, it would be possible to map the structure of a brain down to the level of the elementary particle and iterate the positions of each, but as such a solution would require by today’s standards a hard-drive larger than the universe and more time than that hard-drives constituent atoms would have before deteriorating, it is probably not the optimal answer.)
A good example of this computational ineptness is the field of natural language translation. Artificial intelligence gurus in the 1950s and 1960s believed that translation would be one of the first areas to succumb to computers — what is it, anyway, but simple word replacement to account for vocabulary and word shuffling to account for grammar? Initial efforts in English-Russian translation quickly revealed that language is far more amorphous than was previously thought, often resulting in hilarity: In two famous examples, “The spirit is willing but the flesh is weak,” became “The vodka is good but the meat is rotten,” and an engineering paper discussing hydraulic rams became a long discourse about water-goats (Kurzweil 406). While computers have become hundreds of times faster and programming methods have been greatly refined since these efforts, the translation still has its share of difficulties.
AltaVista’s orline translation service, which translates to and from English, French, German, Italian, Portuguese, and Spanish, renders the preceding sentence about vodka and water-goats, translated into Spanish and back, as “The initial efforts in the translation English-Russian revealed quickly that the language is more amorphous distant than was thought previously, often giving by result hilarity: In two famous examples, ‘the alcohol is arranged but the meat is weak’ became ‘the vodka is good but the meat is putrefaction’, and the hydraulics rams discussing of paper of engineering became a long speech on water-goats.” Clearly, these programs still leave a great deal to be desired.
AltaVista falls into the same trap as the original program translating ’spirit’ and ‘flesh,’ and another ambiguity appears since the same word in Spanish can be used for ‘willing’ and ‘arranged.’ Curiously, it appears that the words ‘rotten’ and ‘hydraulic’ are in only the English-Spanish dictionary and not vice versa (the failure of the reverse translation in the latter case could be due to the fact that the correct spelling of ‘hydraulics’ is with an ‘i’).
What is true for language translation only becomes more pronounced when the scope of a program is broadened. The General Problem Solver, mentioned above, is one such example. Given a very narrow range of options, it can find an optimal solution: If it knows that it is a monkey, who wants to eat a banana that is too high for it to reach, and that there is a chair on the other side of the room, and that it can move around, pick things up, carry them, and stand on top of them, it will succeed in getting to the banana. But a real monkey has a nearly limitless repertoire of action — it could scream at the banana, do cartwheels, stick its tail in its mouth — and yet it still carries the chair to the banana and climbs on top of it (Hogan 241).
Two techniques for remedying the problems caused by a computer’s rote mechanical processes, both in a state of relative infancy, hold promise. The first, neural networking, is an attempt to mimic the actual functioning of the human brain, A neural network consists of several cells, or neurons, all interlinked. Neurons can receive inputs and fire outputs, which then affect the state of other neurons. If the sum of all the inputs a neuron receives surpasses a certain level, that neuron will fire.
By tweaking the behavior of each neuron, complex problems can be solved quickly. Data, instead of being stored in discrete locations, is spread around the network, so the system is far more resilient to hardware failure — much as the human brain is often able to compensate for the loss of certain abilities if a part of it is damaged. Applications of neural networks currently include pattern recognition (optical character recognition, or scanning printed text, has reached a state of near perfection) and business management aids, especially risk assessment tools (Johnson 46).
The second technique is the evolutionary program development. To develop a regular program, one or more programmers write code telling the computer exactly what to do. The programmers themselves have designed the algorithm used and understand what each part of it is meant to accomplish. In order to develop an evolutionary program, however, the programmers start with a more or less random algorithm and a program to determine how fit that algorithm is for a certain task.
The algorithm is then mutated and mated with other algorithms to produce a new generation of algorithms; the fittest algorithms of each succeeding generation are mated together. After many generations, a usable program has been developed, with code written by the computer itself Evolutionary development has been used extensively in hardware development and chip design, and is a basic premise of artificial life, simulating primitive forms of life on a computer (Benedict 263).
Both of these approaches have shown an aptitude for the kind of reasoning needed by general artificial intelligence, and as the scientific community comes to the realization that traditional program design is prohibitive for all but the most limited of intelligence, these and other methods will be further studied, implemented, and adopted. Neural networks, evolutionary program development, and other approaches currently being developed truly represent the kind of paradigm shift needed to unite creativity and concept association with logic and order.
Several scientists have argued against artificial intelligence on both ethical and scientific grounds. Computers could never possess intelligence, such scientists say, and if they did, they could certainly never be conscious. The nature of consciousness and knowledge, as suggested earlier, is truly unknown. Be this as it may, nothing suggests that these traits are specific to organic material, or that they cannot be reproduced in silicon. The moral duties of computer scientists are more complicated. Many say that, even if the capability for artificial intelligence exists, it should not be developed.
The risk a race of intelligent computers would pose to humanity is too great to be ignored. It must be noted, however, that ethics, especially on the edges of scientific development, have rarely prevented people from doing things, and as a result of one of those developments, despotic rulers, over whom the general public holds less power than they would over artificial intelligence, have long had the capability to annihilate life on earth.
Assuming artificial intelligence can be and is developed, what will be the ramifications for society? Several occurrences exist, known as historic singularities, which involve sufficient change and variability to make long-range prediction impossible or very nearly so. The development of language was one such singularity in human history, the discovery of fire another.
Others have included the invention of the wheel and the printing press, Columbus’s stumbling across America, the Industrial Revolution, and the invention of computers. Future singularities could include the full realization of human cloning, large-scale expeditions into space, contact with ex-terrestrials, the development of time-travel and light-speed travel, manipulation of consciousness, the mass merging of consciousness, and the end of the universe.
Artificial intelligence could be another. Current specialized intelligence and expert systems will continue to improve, making various aspects of life easier, but the existence of a full and general intelligence implies such great variability that the consequences it would have cannot be accurately predicted. Artificial intelligence of this magnitude is still years away and may not appear within the next century. In the meantime, with the growing intelligence of computer applications, the increasing automation of many tasks, the spread and evolution of the Internet, and the increasing ease of global communication, the line between computer and human will grow vague and blurred. To one without significant computer experience, artificial intelligence will seem to be already extant. Humans have created magic countless times throughout history, and wit-bin a few years each new development is taken for granted. The gradual emergence of artificial intelligence will come as no surprise to the general public, and scientists will continue to speculate about the lack of development even as their computers are widely intelligent by the standards of ten years earlier.
- “AltaVista Translation Services.” Internet: *http:babelfish.altavista.com*. Accessed 2 May 1999. Software developed by AltaVista and Systran.
- Artificial Intelligence. Time-Life Books: Alexandria, Virginia, 1986.
- Benedict, Michael, ed. Cyberspace: First Steps. The MIT Press: Cambridge, Massachusetts. 1991.
- Dreyfus,Hubert. What Computers Still Can’t Do. The MIT Press: Cambridge, Massachusetts. 1993.
- Freedman, David. Brain-makers. Simon & Schuster: New York. 1994.
- Forsyth, Richard, and Chris Naylor. The Hitch-Hiker’s Guide to Artificial Intelligence. Chapman and Hall / Methuen: London. 1985.
- Haugeland, David. Artificial Intelligence: The Very Idea. The MIT Press: Cambridge, Massachusetts. 1985.
- Hogan, James. Mind Matters. The Ballantine Publishing Group: New York, 1997.
- Johnson, George. Machinery of the Mind. Times Books: New York. 1986
- Kurzweil Raymond. The Age of Intelligent Machines. The MIT Press: Cambridge, Massachusetts. 1990
- Penrose, Roger. The Emporer’s New Mind. Oxford University Press: Oxford, England. 1989.
- Reitman, Edward. Creating Artificial Life: Self-Organization. Windcrest / McGraw-Hill: New York. 1993.
Example #2 – Natural Language Processing: Computers Really Comprehend Human Languages
It is a sub-kind of AI (Artificial Intelligence) that is centered around empowering PCs to comprehend and process human dialect and the dialects given by the client. It is a sub-type of AI (Artificial Intelligence) that is focused on enabling computers to understand and process human language and the languages provided by the user.
Can Computers Really Understand Languages
Since the introduction of PCs, software engineers have been endeavoring to compose programs that can comprehend dialects like English and some other dialect. Well the reason is clear as people have been recording things for a considerable length of time it would be extremely useful if a PC could read and see everything that information given by us. PCs can truly really comprehend English the manner in which the people however PCs can complete a great deal! In certain restricted regions.
The things you can do with Natural Language Processing (NLP) appears like a genuine enchantment. You may have the capacity to make things way significantly less demanding by utilizing NLP strategies.
Since the birth of computers, programmers have been trying to write programs that can understand languages like English and any other language. Well the reason is obvious as humans have been writing things down for centuries it would be really helpful if a computer could read and understand all that data given by us Computers can really truly understand English the way the humans but computers have the ability to do a lot! In certain limited areas. The things you can do with Natural Language Processing (NLP) seems like real-life magic. You might be able to make things way much easier by using NLP techniques.
The first NLP Application was invented in 1948
- Dictionary lookup system (developed at Birkbeck College, London). In 1949, NLP was used for American Interest
- WWII code breaker by Warren Weaver (He viewed German as English in codes). In 1950, Machine Translation was developed (Russian to English. In 1966, It was Over-Promised under-delivered.
Introduction to NLP
Natural language processing is a zone of research and application that investigates how PCs can be utilized to comprehend and control common dialect content or discourse to do valuable things. NLP scientists plan to assemble information on how people comprehend and utilize dialect with the goal that proper devices and strategies can be created to influence PCs to comprehend and control normal dialects to perform wanted undertakings.
The establishments of NLP lie in various orders, to be specific, PC and data sciences, etymology, arithmetic, electrical and electronic designing, man-made consciousness and apply autonomy, and brain research. Utilizations of NLP incorporate various fields of study, for example, machine interpretation, common dialect content preparing and synopsis, UIs, multilingual cross-language information retrieval (CLIR), discourse acknowledgment, and master frameworks.
The way toward perusing and understanding English is exceptionally mind-boggling and that is not notwithstanding considering that English doesn’t pursue legitimate and reliable principles. For example: What does this headline news means mean?”Ecological controllers flame broil entrepreneur over illicit coal fires. “Are the controllers questioning an entrepreneur about burning coal illegally? Or are the controllers literally cooking the entrepreneur? This seems funny but the fact is parsing English with a computer is really a complicated matter.
The procedure of extricating significance from the information
Process of extracting meaning from the data: Doing anything confounded in machine adapting more often than not implies building a pipeline. The thought is to separate your concern into little pieces and after that utilization machine figuring out how to unravel each littler piece independently. At that point by anchoring together a few machine learning models that feed into one another, you can do extremely muddled things. that is precisely the technique we will use for NLP. We’ll separate the way toward understanding English into little pieces and perceive how each one functions.
Building an NLP pipeline Step-by-Step
Let us have a look on a piece of text from Wikipedia:
“London is the capital and most populous city of England and the United Kingdom. Standing on the River Thames in the south east of the island of Great Britain, London has been a major settlement for two millennia. It was founded by the Romans, who named it Londinium”.
This passage contains a few helpful certainties. It would be extraordinary if a PC could read this content and comprehend that London is a city, London is situated in England, London was settled by Romans et cetera. Be that as it may, to arrive, we need to initially show our PC the most essential ideas of composed dialect and afterward climb from that point.
Step 1: Sentence division
Sentence segmentation: The initial phase in the pipeline is to break the content separated into discrete sentences. That gives us this:
- “London is the capital and most populous city of England and the United Kingdom. ”
- “Standing on the River Thames in the south east of the island of Great Britain, London has been a major settlement for two millennia. ”
- “Standing on the River Thames in the south east of the island of Great Britain, London has been a major settlement for two millennia. ”We can accept that each sentence in English is a different idea or thought. It will be significantly less demanding to compose a program to comprehend a solitary sentence than to comprehend an entire passage.
- Coding a Sentence Segmentation model can be as straightforward as part separated sentences at whatever point you see an accentuation check. In any case, present day NLP pipelines frequently utilize more unpredictable systems that work notwithstanding when a record isn’t arranged neatly.
Step 2: Word tokenization
Since we’ve part parted our report into sentences, we can process them each one in turn. We should begin with the main sentence from our archive: “London is the capital and most crowded city of England and the United Kingdom.
London is the capital and most populous city of England and the United Kingdom. ”The subsequent stage in our pipeline is to break this sentence into isolated words or tokens. This is called tokenization. This is the outcome: “London”, “is”, “the”, “capital”, “and”, “most”, “crowded”, “city”, “of”, “England”, “and”, “the”, “United”, “Kingdom”, “. ” “London”, “is”, “ the”, “capital”, “and”, “most”, “populous”, “city”, “of”, “England”, “and”, “the”, “United”, “Kingdom”, “. ”
Tokenization is anything but difficult to do in English. We’ll simply part separated words at whatever point there’s a space between them. Furthermore, we’ll likewise regard accentuation stamps as discrete tokens since accentuation additionally has meaning.
Step 3: Foreseeing parts of discourse for every token
Predicting parts of speech for each token:Next, we’ll take a look at every token and attempt to figure its piece of speech, whetherspeech, whether it is a thing, a verb, a modifier et cetera. Knowing the job of each word in the sentence will enable us to begin to make sense of what the sentence is discussing.
We can do this by sustaining each word (and some additional words around it for setting) into a pre-prepared grammatical feature order show.
The grammatical form demonstrate was initially prepared by sustaining it a huge number of English sentences with each word’s grammatical feature officially labeled and having it figure out how to repeat that conduct. Remember that the model is totally founded on statistics, it doesn’t really comprehend what the words mean similarly that people do. It just knows how to figure a grammatical form in view of comparative sentences and words it has seen previously.
Subsequent to handling the entire sentence, we’ll have an outcome like this: LONDON IS THE CAPITAL AND MOST POPULUS Proper Noun Verb Determiner Noun Conjunction Adverb Adjective. With this data, we would already be able to begin to gather some exceptionally fundamental significance. For instance, we can see that the things in the sentence incorporate “London” and “capital”, so the sentence is presumably discussing London.
Step 4: Text lemmatization
In English (and most dialects), words show up in various structures. Take a gander (look) at these two sentences: I had a horse. I had two horses. The two sentences discuss the thing horse, horse; however however, they are utilizing diverse expressions. When working with content in a PC, it is useful to know the base type of each word so you realize that the two sentences are discussing a similar idea. Generally, the strings “horse” and “horses” look like two very surprising words to a PC. In NLP, we call discovering this procedure lemmatization, figuring out the most essential shape or lemma of each word in the sentence. A similar thing applies to verbs.
We can likewise lemmatize verbs by finding their root, unconjugated frame. So “I had two horses” progresses toward becoming “I [have] two [horse]. ” Lemmatization is regularly done by seeing up a table of the lemma types of words in light of their grammatical feature and conceivably having some custom principles to deal with words that you’ve never observed. This is what our sentence looks like after lemmatization includes the root type of our verb: This is what our sentence looks like after lemmatization includes the root type of our verb.
Step 5: Recognizing Stop Words
Next, we need to think about the significance of each word in the sentence. English has a great deal of filler words that seem much of the time like “and”, “the”, and “a”. While doing insights on content, these words present a ton of commotion since they show up far more often than different words. Some NLP pipelines will hail them as stop words — that is, words that you should need to sift through before doing any measurable examination.
Stop words are normally recognized by just by checking a hardcoded rundown of known stop words. In any case, there’s no standard rundown of stop words that is proper for all applications. The rundown of words to overlook can differ contingent upon your application. For instance, on the off chance that you are building a musical crew internet searcher, you need to ensure you don’t disregard “The”. Since not exclusively does “The” show up in a ton of band names, there’s an acclaimed 1980’s musical gang called the!
Step 6: Reliance Parsing
The following stage is to make sense of how every one of the words in our sentence identify with one another. This is called reliance parsing. The objective is to construct a tree that allots a solitary parent word to each word in the sentence. The foundation of the tree will be the fundamental verb in the sentence. Be that as it may, we can go above and beyond.
Notwithstanding distinguishing the parent expression of each word, we can likewise anticipate the kind of relationship that exists between those two wordsThis parse tree demonstrates to us that the subject of the sentence is the thing “London” and it has a “be” association with “capital”. We at long last know something useful — London is a capital! What’s more, on the off chance that we pursued the total parse tree for the sentence (past what is appeared), we would even discover that London is the capital of the United Kingdom.
Step 6b: Recognizing noun phrase
Up until now, we’ve treated each word in our sentence as a different entity. We can utilize the data from the reliance parse tree to consequently bunch together words that are on the whole discussing a similar thing.
Regardless of whether we do this progression relies upon our ultimate objective. Yet, it’s frequently a speedy and simple approach to rearrange the sentence on the off chance that we needn’t bother with additional insight about which words are modifiers and rather care more about extricating complete thoughts.
Step 7: Named Entity Recognition
Since we’ve done everything that diligent work, we can at last move past grade-school language structure and begin really extricating thoughts. The objective of Named Entity Recognition, or NER, is to identify and name these things with this present reality idea that they represent. But NER frameworks aren’t simply completing a straightforward word reference query. Rather, they are utilizing the setting of how a word shows up in the sentence and a measurable model to figure which sort of thing a word speaks to.
Step 8: Coreference Resolution
Now, we as of now have a helpful portrayal of our sentence. We know the parts of discourse for each word, how the words identify with one another, and which words are discussing named substances. In any case, despite everything we have one major issue. English is loaded with pronouns — words like he, she, and it.
These are alternate routes that we use as opposed to working out names again and again in each sentence. People can monitor what these words speak to in view of the setting. Yet, our NLP display doesn’t comprehend what pronouns mean since it just inspects one sentence at any given moment.
This is only a modest taste of what you can do with NLP. While NLP is a generally ongoing zone of research and application, when contrasted with other data innovation approaches, there have been adequate triumphs to date that propose that NLP-based data get to advancements will keep on being a noteworthy zone of innovative work in data frameworks now and far into what’s to come.
Recently, the media has spent an increasing amount of broadcast time on new technology. The focus of high-tech media has been aimed at the flurry of advances concerning artificial intelligence (AI). What is artificial intelligence and what is the media talking about? Are these technologies beneficial to our society or mere novelties among business and marketing professionals? Medical facilities, police departments, and manufacturing plants have all been changed by AI but how?
These questions and many others are the concern of the general public brought about by the lack of education concerning rapidly advancing computer technology. Artificial intelligence is defined as the ability of a machine to think for itself. Scientists and theorists continue to debate if computers will actually be able to think for themselves at one point (Patterson 7). The generally accepted theory is that computers do and will think more in the future.
AI has grown rapidly in the last ten years chiefly because of the advances in computer architecture. The term artificial intelligence was actually coined in 1956 by a group of scientists having their first meeting on the topic (Patterson 6). Early attempts at AI were neural networks modeled after the ones in the human brain. Success was minimal at best because of the lack of computer technology needed to calculate such large equations. AI is achieved using a number of different methods.
The more popular implementations comprise neural networks, chaos engineering, fuzzy logic, knowledge-based systems, and expert systems. Using any one of the aforementioned design structures requires a specialized computer system. For example, Anderson Consulting applies a knowledge-based system to commercial loan officers using multimedia (Hedburg 121). Their system requires a fast IBM desktop computer. Other systems may require even more horsepower using exotic computers or workstations.
Even more exotic is the software that is used. Since there are very few applications that are pre-written using AI, each company has to write its own software for the solution to the problem. An easier way around this obstacle is to design an add-on. The company FuziWare makes several applications that act as an addition to a larger application. FuziCalc, FuziQuote, FuziCell, FuziChoice, and FuziCost are all products that are used as management decision support systems for other off-the-shelf applications (Barron 111). In order to tell that AI is present, we must be able to measure the intelligence being used.
For a relative scale of reference, large supercomputers can only create a brain the size of a fly (Butler and Caudill 5). It is surprising what a computer can do with that intelligence once it has been put to work. Almost any scientific, business or financial profession can benefit greatly from AI. The ability of the computer to analyze variables provides a great advantage to these fields.
There are many ways that AI can be used to solve a problem. Virtually all of these methods require special hardware and software to use them. Unfortunately, that makes AI systems expensive. Consulting firms, companies that design computing solutions for their clients, have offset that cost with the quality of the system. Many new AI systems now give a special edge that is needed to beat the competition. Neural networks have entered the spotlight with surprisingly successful results.
A neural network is a type of information processing system whose architecture is similar to the structure of biological neural systems (Butler and Caudill 5). The neural network tries to mimic the way a brain and nervous system work by analyzing sensory inputs and calculating an outcome. A neural network is usually composed of simple decision making elements that are connected with variable weights and strengths. Each one these elements is called a neurode. The term neurode is similar to the biological neuron. The term was modified slightly to indicate an artificial nature. Memory is stored by a certain pattern of the connection weights between the neurodes. Processing information is performed by changing and spreading the connection’s weights among the network. Before it can be used a neural network it must be trained.
Some can learn by themselves, some require training by doing, and others learn by trial and error. A computer learns by naturally associating items the computer is taught and grouping them together physically. Additionally, a computer can retrieve stored information from incomplete or partially incorrect clues. Neural networks are able to generalize categories based on specifics of the contents.
Lastly, it is highly fault-tolerant. This means that the network can sustain a large amount of damage and still function. Its performance fades proportionally as the neurodes disappear (Butler and Caudill 8). This type of system is inherently an excellent design for any application that requires little human intervention and that must learn on the go. Created by Lotfi Zadeh almost thirty years ago, fuzzy logic is a mathematical system that deals with imprecise descriptions, such as “new”, “nice”, or “large” (Schmuller 14). This concept was also inspired by biological roots. The inherent vagueness in everyday life motivates fuzzy logic systems (Schmuller 8).
In contrast to the usual yes and no answers, this type of system can distinguish the shades in-between. In Los Angeles, a fuzzy logic system is used to analyze input from several cameras located at different intersections (Barron 114). This system provides a “smart light” that can decide whether a traffic light should be changed more often or remain green longer. In order for these “smart lights” to work the system assigns a value to input and analyzes all the inputs at once.
Those inputs that have the highest value get the highest amount of attention. For example, here is how a fuzzy logic system might evaluate water temperature. If the water is cold, it assigns a value of zero. If it is hot the system will assign the value of one. But if the next sample is lukewarm it has the capability to decide upon a value of 0.6 (Schmuller 14). The varying degrees of warmness or coldness is shown through the values assigned to it. Fuzzy logic’s structure allows it to easily rate any input and decide upon the importance. Moreover, fuzzy logic lends itself to multiple operations at once. Fuzzy logic’s ability to do multiple operations allows it to be integrated into neural networks.
Two very powerful intelligent structures make for an extremely useful product. This integration takes the pros of fuzzy logic and neural networks and eliminates the cons of both systems (Liebowitz 113). This new system is now a neural network with the ability to learn using fuzzy logic instead of hard concrete facts. Allowing a more fuzzy input to be used in the neural network instead of being passed up will greatly decrease the learning time of such a network.
Another promising arena for AI is chaos engineering. Chaos theory is the cutting-edge mathematical discipline aimed at making sense of the ineffable and finding order among seemingly random events (Weiss 138). Chaologists are experimenting with Wall Street where they are hardly receiving a warm welcome. Nevertheless, chaos engineering has already proven itself and will be present for the foreseeable future. The theory came to life in 1963 at the Massachusetts Institute of Technology.
Edward Lorenz, who was frustrated with weather predictions, noted that they were inaccurate because of the tiny variations in the data. Over time he noticed that these variations were magnified as time continued. His work went unnoticed until 1975 when James Yorke detailed the findings to American Mathematical Monthly. Yorke’s work was the foundation of the modern chaos theory (Weiss 139). The theory is put into practice by using mathematics to model complex natural phenomena.
The chaos theory is used to construct portfolios of long and short positions in the stock market on Wall Street. This is used to assess market risk accurately, not to predict the future (Weiss 139). Unfortunately, the hard part is putting the theory into practice. It has yet to impress the people that really count: financial officers, corporate treasurers, etc.
It is quite understandable though, who is willing to sink money into a system that they cannot understand? Until a track record is set for chaos most will be unwilling to try, but to get the track record someone has to try it, it’s what is known as the “catch-22.” The chaos theory can be useful in other places as well. Kazuyuki Aihara, an engineering professor at Tokyo’s Denki University, claims that chaos engineering can be applied to analyzing heart patients.
The pattern of beating hearth changes slightly and each person pattern is different (Ono 41). Considering this discovery a data processing company in Japan has marketed a physical checkup system that uses chaos engineering. This system measures health and psychological conditions by monitoring changes in circulation at the fingertip (Ono 41). Aihara admits that chaos-engineering has tremendous potential but does have limitations. He states, “It can predict the future more accurately than any other system but that doesn’t mean it can predict the future all the time.”
Along these lines, Rabi Satter, a computer consultant with a BS in Computer Science, believes that the current sentiment that the world is rational and can be reduced to mathematical equations is wrong. “In order to make great strides in this arena [AI], we need new approaches informed by the past but not guided by it. A fresh voice if you would. As one person said we are using brute force to solve the problem” states Satter.
A few more implementations of artificial intelligence include knowledge-based systems, expert systems, and case-based reasoning. All of these are relatively similar because they all use a fixed set of rules. Knowledge-based systems (KBS) are systems that depend on a large base of knowledge to perform difficult tasks (Patterson 13). KBS get their information from expert knowledge that has been programmed into facts, rules, heuristics, and procedures.
However, the power of a knowledge-based system is only as good as the knowledge given to it. Therefore, the knowledge section is usually separate from the control system and can be updated independently. This enables system updates and additional information to be added in a more efficient manner then making a whole new system from scratch (O’Shea 162). Expert systems have proven effective in a number of problem domains that usually require human intelligence (Patterson 326).
They were developed in the research labs of universities in the 1960s and 1970s. Expert systems are primarily used as specialized problem solvers. The areas that this can cover are almost endless. This can include law, chemistry, biology, engineering, manufacturing, aerospace, military operations, finance, banking, meteorology, geology, and more. Expert systems use knowledge instead of data to control the solution process. “In knowledge lies the power” is a theme repeated when building such systems.
These systems are capable of explaining the answer to the problem and why any requested knowledge was necessary. Expert systems use symbolic representations for knowledge and perform computations through manipulations of the different symbols (Patterson 329). But perhaps the greatest advantage to expert systems is their ability to realize their limits and capabilities. Case-based reasoning (CBR) is similar to an expert system because theoretically, they could use the same set of data. CBR has been proposed as a more psychologically plausible model of the reasoning used by an expert while expert systems use more fashionable rule-based reasoning systems (Riesbeck 9).
This type of system uses a different computational element that decides the outcome of a given input. Instead of rules in an expert system, CBR uses cases to evaluate each input uniquely. Each case would be matched to what a human expert would do in a specific case. Additionally, this system knows no right answers, just those that were used in former cases to match. A case library is set up and each decision is stored. The input question is characterized by appropriate features that are recognizable and are matched to a similar past problem and its solution is then applied.
Now that each type of implementation of AI has been discussed, how do we use all this technology? Foremost, neural networks are used mainly for internal corporate applications in various types of problems. For example, Troy Nolen was hired by a major defense contractor to design programs for guiding flight and battle patterns of the YF-22 fighter. His software runs on five on-board computers and makes split-second decisions based on data from ground stations, radar, and other sources. Additionally, it predicts what the enemy planes would do, guiding the jet’s actions consequently (Schwartz 136).
Now he and many others design financial software based on their experience with neural networks. Nolen works for Merrill Lynch & Co. to develop software that will predict the prices of many stocks and bonds. Murry Ruggiero also designs software, but he forecasts the future of the Standard & Poors index. Ruggiero’s program, called BrainCel, is capable of giving an annual return of 292%. Another major application of neural networks is detecting credit card fraud.
Mellon Bank, First Bank, and Colonial National Bank all use neural networks that can determine the difference between fraud and regular transactions (Bylinsky 98). Mellon Bank states the new neural network allows them to eliminate 90% of the false alarms that occur under traditional detection systems (Bylinsky 99). Secondly, fuzzy logic has many applications that hit close to home.
Home appliances win most of the ground with AI-enhanced washing machines, vacuum cleaners, and air-conditioners. Hitachi and Matsu*censored*a manufacture washing machines that automatically adjust for load size and how dirty the articles are (Shine 57). This machine washes until clean, not just for ten minutes. Matsu*censored*a also manufactures vacuum cleaners that adjust the suction power according to the volume of dust and the nature of the floor. Lastly, Mitsubishi uses fuzzy logic to slow air-conditioners gradually to the desired temperature.
The power consumption is reduced by 20% using this system (Schmuller 27). The chaos theory is limited in scope at this time mainly because of a lack of interest and resources to experiment with. However, Wall Street will be hearing more about it for a long time to come. Also, the medical field has an interest because of its ability to distinguish between natural and non-natural patterns.
The chaos theory has a foot in the door, but a breakthrough in design will have to come around first before any major moves toward the chaos theory will happen. Expert systems are prevalent all over the world. This proven technology has made its way into almost everywhere that human experts live. Expert systems even can show an employee how to be an expert in a particular occupation.
A Massachusetts company specializes in teaching good judgment to new employees or trainees. Called Wisdom Simulators, this company sells software that simulates nasty job situations in the business world. The ability to learn before the need arises attracts many customers to this type of software (Nadis 8).
Expert systems have also been applied in medical facilities, diagnosis of mechanical devices, planning scientific experiments, military operations, and teaching students specialized tasks. Knowledge-based systems and case-based reasoning will be on the rise for a long time to come. These systems are souped-up expert systems that provide more powerful searching and decision-making strategies. KBS is finding its home at help desks by working with telephone operators to direct calls. CBR will have close ties to law with its ability to use past precedents to determine a sentence and prison term. KBS is already being used by the Tennessee Department of Corrections for determining which inmates are eligible for parole (Peterson 37).
Making recommendations on which AI systems work the best almost requires AI itself. However, I believe that some are definitely better than others. Neural networks, unfortunately, have performance spectrums that continue to dwell at both extremes. While there are some very good networks that perform their designed task beautifully, there are others that perform miserably. Furthermore, these networks require massive amounts of computing resources that restrict their use to those who can afford it.
On the other hand, fuzzy logic is practically a win-win situation. Although some are rather simple, these systems perform their duties quickly and accurately without expensive equipment. They can easily replace many mundane tasks that other computer systems would have trouble with. Fuzzy logic has enabled computers to calculate such terms as “large” or “several” that would not be possible without it (Schmuller 14). On the other hand, chaos theory has the potential for handling an infinite amount of variables. This gives it the ability to be a huge success in the financial world. It’s a high learning curve and its primitive nature, however, limits it to testing purposes only for the time being.
It will be a rocky road for chaos theory and chaos engineering for several years. Finally, expert systems, knowledge-based systems, and case-based reasoning systems are here to stay for a long time. They provide an efficient, easy to use program that yields results that no one can argue with. Designed correctly, they are can be easily updated and modernized.
While the massive surge into the information age has ushered some old practices out of style, the better ones have taken over with great success. The rate of advancement may seem fast to the average person, but the technology is being put to good use and is not out of control.
A little time to experiment with the forefront technologies and society will be rewarded with big pay-offs. Soon there will be no place uncharted and no stone unturned. Computers are the future in the world and we should learn to welcome their benefits and improve their shortcomings to enrich the lives of the world.
Over the years people have been wanting robots to become more Intelligent. In the past 50 years since computers have been around, the computer world has grown like you wouldn’t believe it. Robots have now been given jobs that were 15 years ago no considered to be a robot’s job. Robots are now part of the huge American government Agency the FBI.
They are used to disarm bombs and remove dangerous products from a site without putting human life in danger. You probably don’t think that when you are in a carwash that a robotic machine is cleaning your car.
The truth is that they are. The robot is used senses to tell the main computer what temperature the water should be and what style of wash the car is getting e.g. Supreme or Normal wash. Computer robots are being made, that learn from their mistakes. Computers are now creating their own programs. In the past there used to be some problems, now they are pretty much full proof.
The Television and Film business has to keep up with the demands from the critics sitting back at home, they try and think of new ideas and ways in which to entertain the audiences. They have found that robotics interests people. With that have made many movies about robotics (e.g. Terminator, Star Wars, Jurassic Park).
Movie characters like the terminator would walk, talk, and do actions by its self mimicking a human through the use of Artificial Intelligence. Movies and Television robots don’t have Artificial Intelligence ( AI ) but are made to look like they do. This gives us the viewers a reality of robotics with AI. Understanding Of The IT Background Of The IssueArtificial Intelligence means ” Behavior performed by a machine that would require some degree of intelligence if carried out by a human “.
The Carwash machine has some intelligence which enables it to tell the precise temperature of the water it is spraying onto your car. If the water is too hot it could damage the paintwork or even make the rubber seals on the car looser. The definition above shows that AI is present in everyday life surrounding humans where ever they go.
Alan Turing Invented a way in which to test AI. This test is called the Turing Test. A computer asks human various questions. Those conducting the test have to decide whether the human or the computer is asking the questions. Analysis Of The Impact Of The issue with the increasing amount of robots with AI in the workplace and in everyday life, it is making human jobs insecure for now and in the future. If we take a look at all the major car factories 70 years ago they were all handcrafted and machinery was used very little. Today we see companies like TOYOTA who produce mass amounts of cars with robots as workers.
This shows that human workmanship is required less and less needed. This is bad for the workers because they will then have no jobs and will be on the unemployment benefit or trying to find a new job. The advantage of robots is that they don’t need a coffee break or need to have the time off work.
The company owns the machinery and therefore they have control over the robot. Solutions To Problems Arising From The IssueSome problems arising from the issue would include job loss, due to robots taking the place of humans in the workplace. This could be resolved by educating the workers to do other necessary jobs in the production line.
Many of the workers will still keep their other jobs that machines can’t do. If robots became intelligent this could be a huge disaster for humankind. We might end up being second best to robots. They would have the power to do anything and could eliminate humans from the planet especially if they are able to program themselves without human help. I think the chance of this happening is slim but it is a possibility.
With advances in technology, many researchers have become captivated with the pursuit of Artificial Intelligence. Numerous fields of study have tried to contribute their knowledge in order to create intelligence. However, years of research have thus far been unable to create human intelligence. The endeavor seems doomed to fail, for a century of thought which has tried to simply define intelligence has yet to succeed. This lack of a concrete tangible definition does not preclude its existence but merely points to its complex nature. Human intelligence could be viewed as being as diverse as its population however this type of analysis leads us to the individual and becomes useless.
There is no doubt that there are universal patterns of what could be considered intelligence and it is these patterns that may give us insight. Because these patterns of intelligence could be linked to humanity s evolution, much time is devoted to finding what forces or factors are responsible for them. There are few who would still adhere to a model of Nature vs. Nurture rather than substituting the vs. for via. Both environmental and genetic factors contribute to human intelligence, however which of these, if any, is more important in shaping intelligence is a source of fierce disagreement. It seems apparent that those who posses higher levels of intelligence are accorded a certain amount of privilege.
Therefore where intelligence comes from is essential in determining the validity of endowing privilege on those who possess it. Is it the case that the very definition of intelligence is socially constructed in order to maintain the existing social inequalities? Is it the case that social inequalities are merely a reflection of the variance in intelligence? Do social inequalities reduce the oppressed ability to develop intelligence? Is intelligence merely a small factor contributing to the uneven distribution of resources within our world? Is intelligence a product of hard work or just luck? Evolution of Intelligence Before we can begin to examine the modern-day conceptions of intelligence, it is necessary to look at how human intelligence has evolved.
For the purpose of simplicity, I am making the assumption that the general theory of evolution is accurate. That is that humans did not spontaneously appear on Earth and are a product of millions of years of evolution. Therefore it is conceivable that the very way in which we think was once quite different from today s mode of logic and reason. Amaury de Reincourt looks at a turning point in the evolution of human intelligence in her article Sex and Power in History. In this article, she examines the rise of patriarchy out of matriarchy. She iterates how this shift was a result of man s gradual development of his role in procreation. This marked a mental threshold from magico-symbolic thought processes to rational thinking. The creation of life was now understood in terms of causality rather than mysticism. From this point forth, all the female-oriented myths were reinterpreted patriarchal.
The cyclical nature of female-oriented thought was replaced by the linear thought patterns of male-oriented thinking. This further led to the notion of progress and later reflective thought. The mythology that prevailed under the matriarchal rule was replaced by the masculine thought process of rationalism and logic. The overall effect is that tension replaced repetitive rhythm.
This led the way to the concept of time as being unidirectional instead of the lunar-vegetal cycle which previously set-up the notion of time. History could now be viewed with a beginning, middle, and an end. This had great significance in releasing man from the endless repetitive cycles of time, which could now be seen as a linear development with unique moral significance for each step of the way. It is apparent from this article that logic and reason are not value-neutral, they are concepts that are steeped in a particular ideology. What it also inadvertently points to the idea of interpreting intelligence from completely opposite perspectives.
Thinking in terms of cycles instead of our linear modes of thoughts produces completely different types or patterns of intelligence. It serves as a caution in trying to determine and define the very slippery notion of intelligence. Intelligence defined The inherent difficulty in studying intelligence is reflected in one of the psychology’s maxims; the human mind s greatest challenge is to understand itself. This has nonetheless not deterred psychologists in attempting to measure this ambiguous concept. The first to propose and design an intelligence test was Alfred Binet. He was summoned by the French government to design a test that would be able to alert educators of children who might benefit from remedial instruction.
The test was so successful in determining school performance that it was accepted throughout the western world. In 1916 Lewis Terman from Standford University adapted it for use with American children. It thus became the Stanford-Binet Intelligence Test and is the test most commonly referred to when speaking of an IQ test. This test and others like it take a holistic approach to intelligence. They point to the idea of intelligence as a unified trait. This idea was expanded in 1927 by Spearman who noticed that all the items on the Stanford-Binet test were correlated and thus proposed a general factor, which he termed g, of intelligence. He viewed the different items as measuring specific factors he termed.
The concept that intelligence can be viewed as a singular trait is one that has lost its appeal over the years. In an article published by the Progressive Labor Party Racism, Intelligence, and the Working Class, the authors bring some of the common criticisms that have been directed at such tests. The political agenda they wish to push clouds some of its points but the overall criticism of IQ tests is that they are designed to measure a particular type of ability defined by the ruling class. In essence, this argument points to the fact that these tests are culturally biased. Hence, the scores are not only indicative of only one potential pattern of intelligence but furthermore, they do not reflect an objective universal pattern of intelligence but rather one that is socially constructed.
The first of this criticism was addressed in the mid-1960 s by J.P.Guilford. He devised a 180-factor model of intelligence, which classified each intellectual task according to three dimensions: content, mental operation, and product. This theory is the predecessor to Gardner s theory of multiple intelligence, which was developed, in the last 15 years.
This theory identifies seven independent bits of intelligence on the basis of distinct sets of processing operations applied in culturally meaningful activities (linguistic, logico-mathematical, musical, spatial, bodily/kinesthetic, interpersonal, intrapersonal). This theory addressing both of the major flaws, which were present in some of the earlier, tests. Nonetheless, Gardner s theory is just that a theory, it is not rooted in strong empirical data. However, I believe that this is, to date, the best theory of intelligence that has been developed. Nature or Nurture? Gardner proposes that there are seven distinct types of human intelligence patterns, which manifest themselves to varying degrees in each of us. This might begin to account for the infinite variations in human abilities.
There could possibly be more than just seven but even five types, for example, of intelligence could be mixed in varying degrees to produce the diversity of human existences throughout the ages. However, this does not speak to the origins of intelligence. Is the type and degree of intelligence that we possess a product of our genes or does our environment determine it? A more sensible question might be to ask which, nature or nurture, is in the driver s seat? Researchers have been trying to design experiments to investigate this very question for at least a century. The most common type of study has been one, which investigates the intelligence in identical twins reared apart. This should allow the researcher to differentiate between the effects of nature and nurture on intelligence.
The results have given us estimates as high as 70% for the attribution of genetic influence on intelligence variance(Ken Richardson, Understanding Intelligence). Although these results seem to be conclusive evidence for the view that intelligence is primarily genetic it is not without its critics. It is fairly rare for identical twins to be reared apart, therefore producing a small sample size and thus results that can not easily be generalized. The environments that separated twins are brought up in are likely to be similar, making it difficult to accurately attribute variations in intelligence to their genetic makeup. The most common criticism of all such studies is the very measurement of intelligence. Without an accepted definition, intelligence can not be accurately measured and thus trying to understand its development is somewhat futile.
There are so many factors that contribute to human intelligence and development that trying to find causal links is an exercise in fantasy rather than a scientific endeavor. For us to understand exactly how intelligence develops would be to unravel one of the most elusive mysteries facing humankind. I don t however, believe that this puzzle is likely to be solved in the near future. Social Deconstruction Whichever determinant one believes to have a greater influence on intelligence, it is undeniable that the other still plays a part. Therefore, the environment in which we are raised has a direct effect on the type and the degree of intelligence we may develop, the only ambiguity is how large or small this effect might be. Even if only 30% of the variation in intelligence is attributable to environmental factors, this effect should still be detectable.
Victoria J. Molfese, Lisabeth F. DiLalla, and Debra Bunce from Southern Illinois University at Carbondale conducted a study, which attempted to measure the effects of socioeconomic status, home environment, and biomedical risk factors on intelligence test scores of 3 to 8-year-olds. Home environment quality was evaluated according to maternal intelligence, characteristics of the home, and parenting practices.
Although the researchers found that the home environment was the best predictor of scores on intelligence test scores, the definition of a superior home environment seems fraught with biases. The conclusion that the researchers draw is a valid one based on their data but may not easily be generalized. A good home environment is undoubtedly essential to the intellectual development of young children but what constitutes such an environment is certainly open to debate. Differences in values may lead to an incorrect assessment of the home environment and thus skew the results.
The second measure that they employed, as a predictor of intelligence test scores, was that of Socio-Economic Status. This is, in my opinion, is a more objective factor. The results from the study showed that SES had a greater effect on predicting intelligence test scores of 5 to 8-year-olds. Many studies have also shown that early adolescent test scores are positively correlated with SES. These results would seem to suggest that as children become older and gain an awareness of their SES their intellectual development suffers. This could be the result of stigmatization, once the individual realized that they do not possess the things that others in their environment do, they may feel inferior.
Conversely, their peers might treat them in a negative manner leading to what Goffman termed a spoiled identity. Because money is often equated with morality, those children who are monetarily disadvantaged might feel and be made to feel that they are inferior which might affect their self-conception and lead to decreased motivation. It is extremely difficult to draw any conclusion because of the dynamic relationship of the multitude of environmental factors that work together to shape intelligence.
No links were found between biomedical risk factors, such as preterm and low birthweight babies, and intelligence test scores. This study shows a link between SES and the development of a pattern of intelligence, which is tested through conventional IQ tests. It is hard to generalize these results to other types of intellectual patterns such as those in Gardner s model. However, if SES affects IQ through the process of stigmatization than there seems to be no reason why it should not affect the development of other types of intellectual patterns such as interpersonal and intrapersonal intelligence.
We have all experienced the negative effects that stigmatization can have so it isn’t hard to imagine that being stigmatize due to a factor that is enduring and beyond one’s control could potentially have profound effects. A study conducted by Steve Henry, Ph.D. Gen. Dir. Planning, Eval. & Grant Procurement, looked at the correlation between ethnicity and SES and achievement on a standardized test for 4000 students in grades 3, 5, 8, and 11. The findings showed that ethnicity accounted for 6.5% of the variability in scores while SES accounted for 15.9%. There was also a strong link between SES and ethnicity. White students were twice as likely to come from a higher SES bracket than were minorities.
This study again suggests that a low SES is a strong predictor of success on standardized achievement tests. It also points to the fact that ethnicity by itself is not a good predictor of achievement. This is in line with mountains of evidence gathered over the last two decades which debunk the myth that race is correlated with intelligence.
The horrors of World War II awoke the world to the reality that associating inferiority with a particular race can have dire consequences. More recent sociological investigations have questioned the entire notion of race. Race can be seen as a way of classifying a group of people based on physical traits that have been given social meaning. It is a social construction used to maintain a hierarchy, which favors the dominant race.
Having darker skin does not correlate with lower intellectual abilities but does correlate with lower socio-economic status. As we have seen low SES also correlates with ethnicity. It would suggest that the mere belief the visible minorities are intellectually inferior puts them at a socio-economic disadvantage, which leads to poorer intellectual development. In order to properly assess the variance in intelligence across humanity, we must first strip away some of the widely held myths about social groupings.
It is extremely important to examine and deconstruct the social meaning behind existing groupings such as race, gender, and sexuality before we can properly assess what environmental factors contribute to the formation of human intelligence. Intelligence as a privilege There is no doubt that the people who demonstrate greater intellectual ability are accorded more freedoms and privileges in our World.
As I have iterated intelligence is not a value-neutral concept and is subject to social construction. The first of these constructions is that particular types of intelligence are more valuable to human evolution. A scientist who possesses a high level of, what Gardner termed, logico-mathematical intelligence are accorded greater prestige while social workers who demonstrate high levels of interpersonal intelligence are given less. The social construction of intelligence can warp our perception of the usefulness of certain types of knowledge. It is my opinion that various patterns of intelligence should be accorded equal importance to ensure that human intelligence is allowed to evolve in all of its diverse manifestations.
However, this type of social construction of intelligence transcends all other social constructs. That is that all races, for example, should demonstrate an equal distribution of all the patterns of intelligence. The current conceptions of what types of intelligence are most desirable can not be attributed to socially constructed groups such as race or gender. Therefore we are all subject to equal discrimination based on the type of intelligence in which we demonstrate the greatest aptitude.
However, as I have already mentioned, the mere belief that a group is endowed with particular patterns of intelligence can influence development. The second dimension of intelligence that endows privileges is that of degree. Without relying on a conventional IQ test to show that humans differ in levels of intelligence, it seems that life experience has taught us that we are not all as intelligent regardless of what type of intelligence we are referring to.
Some of the variances can be attributed to micro and macro environmental factors but for evolution to work, intelligence must be passed down from generation to generation. Therefore it seems reasonable to suggest that if all environmental factors were controlled we would still end up with varying degrees of intelligence across all populations.
Viewed in these terms, intelligence seems to give a great deal of privilege quite arbitrarily. However, intelligence does not develop in a completely passive manner. We are not slaves to our environment or our genes. The development of intelligence is also dependent on effort. Learning is not easy regardless of much intellectual privilege you might have.
The dimension of merit further complicates the assessment of intelligence as a privilege. If effort was the only factor that determined intelligence than it could not be considered a privilege. It would seem that there are limits to effort, one who does not have a certain level of intellectual capacity may never be able to attain high levels of intelligence regardless of effort. But conversely, there are those who may be underachievers as a result of lack of effort.
We can however assume that all socially constructed groups are apt to show similar patterns of effort although being oppressed might make one complacent about their chances to advance and reduce their willingness to make the required effort The ultimate privilege Are there different levels of advantages? We know that certain groups are given a host of privileges solely based on their membership to a socially constructed group.
Peggy McIntosh lists some of these privileges that are endowed to whites in her essay White Privilege and Male Privilege: A Personal Account of Coming to See Correspondences Through Work in Women s Studies (1988). She illustrates quite poignantly how we take for granted many little and big privileges that the color of our skin gives or takes away from us.
She also cautions us about finding parallels between the privileges given to different groups. Since racism, sexism, heterosexism is not the same, the advantages associated with them should not be seen as the same (p.104). I think this also points to the suggestion that various privileges give us varying degrees of unearned advantages. Therefore the advantages given to a white male might be mitigated severely if that male is homosexual or the privileges given to a heterosexual male might be entirely negated because of the color of his skin. Where does the privilege of intelligence fit in?
We have seen that SES correlates with intellectual development and that if you belong to an ethnic minority you are more likely to be part of a low SES. We have also seen that intelligence manifests itself in a variety of forms and to varying degrees across all socially constructed groups. Therefore if everything else was equal, we should see even distributions of all groups in our educational institutions.
We know, however, that things are not equal. Ethnic minorities are put a decisive disadvantage when it comes to factors such as SES, which have been shown to correlate with intellectual development. They lack all of the privileges that white skin endows. Therefore it would seem that the disadvantages that ethnic minorities endure should translate to an under-representation in our educational institutions. A group of Canadian researchers looked at the representation rates of visible minorities among 1990 university graduates and the average 1992 earnings of these graduates.
The results indicate that visible minorities comprised just over 10% of the graduates compared to their nine-and-a-half percent share of the 1991 population. Their representation increased from 10% at the undergraduate level to 19% at the Ph.D. level. Their earnings for 1992 were on average 101.9% of non-minorities. These results go in the opposite direction of what could be predicted based theories of white privilege. I believe these results indicate is that intelligence is the ultimate privilege.
Someone who is given the privilege of above-average intelligence is able to overcome all other disadvantages they experience as a result of socially constructed stereotypes. In the knapsack of privilege, intelligence occupies the main compartment while other privileges fill the smaller pockets.
This does not mean that those ethnic minorities that are endowed with above-average intelligence do not suffer from the lack of privileges that whites might enjoy but that the advantages gained from intelligence overcome these socially constructed inequalities.
The effects of white privilege become negligible in terms of allocation of resources but are undoubtedly still present in terms of the more taken for granted privileges that Peggy McIntosh illustrates. Conclusion It is apparent that inequalities permeate our social structure. The sources of many of these inequalities are often subjective social constructs, which are kept in place for the benefit of those who reap their rewards.
The deconstruction of these social groupings is essential if we wish to create a society in which everyone is given the opportunity to fulfill their potential. However, our potentials may not be equal in the strict sense of the word. Intelligence is a dimension of human existence, which may not be evenly distributed for the good of the collective. If everyone were really intelligent no one would have the patience to do more repetitive tasks that are essential to our survival. Viewed in this light, rewarding higher levels of intelligence with privilege seems to be a social construction in itself.
However, as mentioned earlier the acquisition of intelligence requires hard work and should thereby be rewarded. If it were possible to devise a magical equation that could take into account privilege, potential, and effort we might be able to endow the advantages of intelligence in an equitable manner. As researchers in many fields have come to realize, intelligence is an extremely difficult concept for us to wrap our concrete definitions around. However, acknowledging that it manifests itself in various forms at varying degrees may help us to see some of the diversity that life has to offer in a more favorable light.
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