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    My Paper on Computer Intelligence
    This is intended to be read after Identifying the Differences Between Human vs. Computer Intelligence by those who are interested further

    Jorgen Hookham
    December 7, 2010

    Why Computers Do Not

    When Gary Kasparov was defeated by the chess-playing computer Deep Blue in 1997 a great milestone in computer science was achieved, but it was no triumph of artificial intelligence. The IBM team behind Deep Blue did not create intelligence at all; they simply assembled a combination of database, algorithm and computing power capable of computing it's way through a game of world class chess. By this I mean to say that intelligence is not vast processing power, an impressive database or a potent methodology: all of these things were assembled by the intelligences of the Deep Blue team, and Deep Blue the computer had no intelligence to speak of. In fact I take the stance that no 'Artificial Intelligence' (AI) to date has ever been intelligent, because they have all lacked two fundamental aspects of intelligence: creativity, intuition. “‘Artificial Intelligence’ is not a technology. It is a problem domain that is delineated by the criterion that arriving at solutions would require intelligence. We will recognize Intelligence when we see it; a Turing Test will not be required.” (Anderson, Artificial Intuition)

    No definition of intelligence to date commands universal assent, so first let’s looks at the history of the definition of intelligence, then I will define my own best conceptualization of intelligence.

    If we look back in to pre-history, we can imagine a cave man seeing another cave man use a tool for the first time: his brow rises, and he grunts in affirmation of the other caveman's smartness. There is no definition of intelligence here, but the essence of intelligence is in this moment.

    Looking in to the 20th century, we can see a different definition of intelligence as necessity has dictated. In this time period, intelligence is seen as a degree of capability to produce, and to apply and recite catalogued knowledge. It is defined by a score on a standardized test, and described on a scale relative to the cultural or social habitat of a person as a number value: an intelligence quotient. The intelligence quotient is used in social studies to discover relationships between intelligence distribution and other variables, and to predict as well as rate the educational achievements of children. (Wikipedia: Intelligence Quotient)

    Later in the 20th century Howard Gardner proposes the theory of multiple intelligences as a counter to the standing definition of intelligence, defined in psychometrics (IQ tests,) which he saw as an insufficient description of the wide variety of cognitive ability that humans display. An example of the important differentiation that this theory posits would be thus:

    "A child who learns to multiply easily is not necessarily more intelligent than a child who has stronger skills in another kind of intelligence. The child who takes more time to master simple multiplication 1) may best learn to multiply through a different approach, 2) may excel in a field outside of mathematics, or 3) may even be looking at and understanding the multiplication process at a fundamentally deeper level. Such a fundamentally deeper understanding can result in what looks like slowness and can hide a mathematical intelligence potentially higher than that of a child who quickly memorizes the multiplication table despite a less detailed understanding of the process of multiplication." (Wikipedia: Theory of multiple intelligences, para. 1)

    Another definition of intelligence from the same time period sees intelligence in somewhat of a different light: “(intelligence is a) mental activity directed toward purposive adaptation to, selection and shaping of, real-world environments relevant to one’s life.” (Sternberg, 1985, p. 45)

    It is important to note the degree in which each of these definitions of intelligence relates itself to situational context. If we go back through the examples above with this in mind, we can see a chronological dissociation between the two. As far as an intelligence quotient is concerned, intelligence is inseparably linked to superficial context (a test score.) Later theories such as the ones posited by Gardner and Sternberg suggest a differential between face value and actual core intelligence. Psychometrics actually tries to describe such an underlying phenomenon of core intelligence with the 'general intelligence factor,' or g-factor. (Wikipedia: g Factor)

    Human intelligence has a complex nature that is difficult to attribute to a single source. Perceptions and thoughts are reflected in the mind and the intellect acts as a sort of curator. It performs lower level operations such as categorizing and recalling thoughts, but it has higher order functions as well. They are the functions of creativity and intuition, which are imperative to the adaptive nature of human intelligence.

    A theory for helping to describe the creativity of intelligence is "neural darwinism," which suggests a sort of natural selection within the thought process. (Wikipedia: Neural Darwinism) As the mind receives new information, the intellect 'mates' the new data with other ideas and relationships to create offspring ideas. The offspring ideas that are 'strongest' are committed, and the weaker are discarded. New relationships and thought patterns are formed, causing whole parts of the neural network to evolve through this creative process. This is a fundamental aspect of intelligence that AIs do not possess.

    Most of human thought processes are not logical, they are intuitive. This is why computers have such a hard time with so many things that are so simple for our human minds. Computers have a very hard time in what AI researcher Monica Anderson calls "bizarre domains." (A New Direction in AI Research) In these domains logical certainty is an impossibility and the only course of action is to guess, and intuition is the art of guessing wisely based on patterns and past experience. A good example of such a bizarre domain is language. We can make computer programs capable of reading text and recognizing voice commands, but we cannot so far create an intelligence capable of understanding the meaning behind any real world form of human linguistic communication. The meaning of single words is one thing (type 'yellow duck' in to a google image search, for example,) but AIs can't interpret the meaning that emerges from larger strings of words. This illustrates the irreductability of language, because the meaning of a sentence is greater than the sum of it's words. Language is also deeply complex. The meanings of words are not organized in to a neat hierarchy, they are richly connected, just like neurons in the brain. Inside these deeply complex systems is where the meaning of a piece of language originates. Communication relies on a word having similar synapses in the receiving system to those of the sending system to convey meaning. This means that to understand language a computer will likely need a world model of it's own. An intelligence is necessary to navigate the waters of these systems. More than that, it is intelligence combined with experience that has brought us as humans to this common ground, and even if a machine were to become able to comprehend our language it would not be intelligent until it could keep up with and contribute to the evolution of language under said machine's own volition.

    Closing Thoughts
    I consider humans to be a form of technology, and the progress of modern biotechnology seems to reinforce this idea. We have created the first synthetic life (in the wired article Rachel Swaby actually says, “Man-made DNA has ‘booted up’ a cell for the first time.”) (Swaby, para.1), and we have sequenced the human genome. It seems likely that, given enough time, we will one day be able to create a race much like our own to take on the tasks that we often envision artificially intelligent robots taking on. At that point it would be hard to refute the authenticity of an AI’s intelligence, or their consciousness. Perhaps it’s better that AIs are not intelligent and self-aware in a human way, so that we can keep them doing our dirty work without feeling too bad about it.

    Works Cited
    A New Direction in AI Research. Vimeo, 2009. Web. 7 Dec. 2010.

    Wikipedia: Intelligence. Wikipedia. Web. 7 Dec. 2010.

    Wikipedia: Theory of Multiple Intelligences. Wikipedia. Web. 7 Dec. 2010.

    Wikipedia: Intelligence Quotient. Wikipedia. Web. 7 Dec. 2010.

    Wikipedia: g Factor (psychometrics). Wikipedia. Web. 7 Dec. 2010.

    Wikipedia: Neural Darwinism. Wikipedia. Web. 7 Dec. 2010.

    Neisser et al. “Intelligence: Knows and Unknows.” Wikipedia. University of Illinois, 1996. Web. Dec. 7 2010

    Anderson, Monica. Artificial Intuition. Syntience. Web. Dec. 7 2010.

    Sternberg. Beyond IQ: A Triarchic Theory of Intelligence. Cambridge University Press, 1985.

    Swaby, Rachel. Scientists Create First Self-Replicating Synthetic Life. Wired, 2010. Web.
    Dec. 7 2010.

    Sat, Mar 26, 2011  Permanent link

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    BenRayfield     Sun, Mar 27, 2011  Permanent link
    You're right that "neural darwinism" is part of intelligence that computers lack. Part of that is the ability to think by analogy in many ways. While some programs think by analogy, they can only compare strictly defined parts of their data using analogies. They can't think of an analogy between the way they do analogies and the way they do something else.

    Most people can sometimes think using "neural darwinism", but usually they think in the same strict ways a computer does. Example: In this thread  I explained a common example where people see something that behaves in a PLUS way and something else that behaves in a MULTIPLY way, but they do not recognize it when asked. Most people understand plus and multiply on paper but not in most other forms.

    There are a lot of ideas that people can't apply to anything other than the subject they learned it about.

    In general, peoples' inability to combine ideas (the same problem software has) causes the complexity of governments, businesses, language, and lots of other things, to increase exponentially instead of linearly, as it makes exceptions and specialized rules for each possibility instead of finding the patterns in it so they can be generalized and compressed. Example: To sign up for a service, there is usually a legal contract so long and complex that the person signing it is incapable of understanding it even if they had enough time to read it. If people were smart enough to use ideas together, such complexity would be compressed down to the relevant ideas of the contract, like "we give you cell phone service" and "you pay us this much money" and a few possible scenarios. But instead, the complexity of that Human interaction increases the same way computers make special cases out of almost everything.

    we cannot so far create an intelligence capable of understanding the meaning behind any real world form of human linguistic communication

    Thats because Human communication is a form of lossy-compression where the compression algorithm includes a lot of shared knowledge.

    Compare Human language to the 1s and 0s in computer memory and you can say the same thing about Humans not understanding computer communications...

    So far no Human is capable of understanding the meaning behind any real world form of 1s and 0s in computer memory except when that Human knows exactly which program and which part of the program and what sequence of actions the 1s and 0s were generated from, except when its very simple things being communicated. Humans can understand 1s and 0s if computers put them into semantic networks connected to well defined actions, but if I choose a random location in memory to read 100 1s and 0s from, usually no Human is capable of understanding it, even though computers have no problem understanding it.

    So lets have a reverse Turning Test: When a Human can type 1s and 0s to interact with a random location in computer memory, and convince the computer that the Human is a software, without creating any errors, then that Human will be considered to have computer level intelligence. Until then, we can not consider Humans to have real intelligence.

    Language is also deeply complex.

    Complexity is a cost, a bad thing, something to be avoided unless it pays for something more valuable. People like to say they're smart because they can make things very complex, but intelligence is the opposite... doing the same work better with a simpler thought pattern.

    Therefore, here is a measure of intelligence that can measure it up to infinite IQ and is the simplest possible game that can measure intelligence: 
    Phyllotaxis     Mon, Mar 28, 2011  Permanent link
    So lets have a reverse Turning Test: When a Human can type 1s and 0s to interact with a random location in computer memory, and convince the computer that the Human is a software, without creating any errors, then that Human will be considered to have computer level intelligence. Until then, we can not consider Humans to have real intelligence.

    I like the simple symmetry of that concept.
    BenRayfield     Sun, Apr 3, 2011  Permanent link
    Phyllotaxis, symmetry is important because it doesn't allow definitions of intelligence like "made of carbon atoms" or "thinks the same way I do".

    Here's a good example of the similarity of how Humans and artificial intelligence think: I wrote "Turning Test" but I meant "Turing Test". Turning is a more common word than Turing. 1 part of my mind chooses what words to say/write. An other part of my mind chooses how to spell them. That's how its done in artificial intelligence too. Its a little more combined in my mind, blurred between those 2 systems. I think the problem was "Turing" has an "n" and it matched the "n" in "Turn". I make that kind of mistake much more often than I type a random letter so it doesn't form a word. I'll sometimes make the same kind of mistake with words, like writing the same word 2 times consecutively, spelled perfectly each time, or writing extra words that I wasn't trying to type on my keyboard. Language is a very low level system in Human minds, and I don't think it works much differently than in the best artificial intelligence. Whats missing in AI is the ideas of what to write.