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Intelligent Technology of the Future - Cognitive Science Luleå University of Technology Siri Johansson 2010 Natural and Artificial Intelligence When talking about human behaviour and ethics, the discussion of what is natural and what isn’t can be an intricate one. Nature is often referred to as an unquestioned authority, eluding further explanations of the concept. When naturalness is defined, it is often done with an excluding purpose. In this essay I’ll discuss whether is critical or not to make this distinction on the topic of intelligence. The Turing test will be used as a reference for this objective. We’ll begin by taking a look at the definition of artificial intelligence (AI). AI is dedicated to developing programs that enable computers to display intelligent behaviour. Since the 1950s, AI has had its up and downs. The real rise happened when research was reoriented from using general-purpose weak methods, to knowledge-intense expert systems that use gathered human knowledge within a restricted domain (Negnevitsky, 2002). Since this happened in the 1970s, AI research has started to combine expert systems with artificial neural networks, which can learn from experience, and fuzzy logic, that uses a more human way of reasoning (with words, even using imprecise terms). It is important to point out that AI researchers aren’t restricted to studying and imitating biological intelligence. The field is free to explore methods dealing with much more computing than people can do. Although most contemporary research is concerned with narrow applications, there is still an interest in the long term goal of building generally intelligent agents (Thomason, Stanford Encyclopedia of Philosophy). When attempting to define a boundary between natural and artificial intelligence, the Turing test is often brought up. The test, or “imitation game” as it was first referred to by Turing(1950), defines intelligence as the ability to perform cognitive tasks on the level of a human. The test consists of a human judge interrogating one human and one machine through a text-only channel. They both try to appear convincingly human. The machine is said to have passed the test if the judge can’t tell it from the real human. Turing believed the question “Can machines think?” to be irrelevant and instead proposed that his test answered the question "Can machines do what we (as thinking entities) can do?” (Turing, 1950). The definition of intelligence should consequently not be concerned with the method by which output is produced. A system very different from what goes on in a human brain but capable of producing satisfying answers should therefore be considered more human-like than a poorly performing system where great efforts have been put into simulating the workings of organic neurons. Turing himself was not particularly interested in defining intelligence in any other terms than behavioural. We commonly describe intelligent behaviour as the ability to think, understand and learn. Natural thinking has a causal drive, it is goal-directed. Information is interpreted contextually and by taking external as well as our own internal states into account, we are able to reason, solve problems and come up with ideas. Learning is propelled forward by the collecting of knowledge, by acting, by analyzing the consequences of taken actions and adjusting future behaviour accordingly to better direct oneself towards one’s goals. In order for an AI to be able to learn, it too has to possess a plastic mind, capable of detecting mistakes and calculating consequences. The artificial neural networks mentioned earlier are able to simulate a natural learning process. One argument against strong AI1 is however that the ability to process symbols according to rules doesn’t qualify as actual thinking. This is classically illustrated by John Searle’s Chinese room thought experiment2 (Hauser, 2005). According to Searle, it doesn’t matter if intelligent behaviour is produced - as long as the symbols being manipulated lack semantic content, no actual thought process can be said to have occurred. The Chinese room has been accused of dualism and meets its opponents in connectionists among others, who argue that a more brain like system with many agents working in parallel could understand, even if each single processor component can’t. The Turing test does evaluate linguistic aspects, namely syntax. But even if an AI is capable of producing expressions with perfect syntax, is the level of sophistication of one’s language controlling the sophistication of the thoughts expressed by an individual, as claimed by the Strong AI research intends to produce machines with an intelligence that matches or exceeds that of human beings, whereas weak AI only claim that machines can act intelligently (without possessing real understanding). (Wikipedia) 1 “The human in the Chinese Room follows English instructions for manipulating Chinese symbols, where a computer “follows” a program written in a computing language. The human produces the appearance of understanding Chinese by following the symbol manipulating instructions, but does not thereby come to understand Chinese. Since a computer just does what the human does—manipulate symbols on the basis of their syntax alone—no computer, merely by following a program, comes to genuinely understand Chinese.” (Cole, Stanford Encyclopedia of Philosophy) 2 linguistic relativity hypothesis3? Does language equal thought, equal intelligence? The strong version of linguistic determinism supposes that thought is not possible without language. While this notion has been heavily criticized and today is more or less disregarded, there are contemporary thinkers who contend that it is the imprecision and flexibility of language that allows for the existence of our creativity-based natural intelligence. Jacob and Shapira (2008) convincingly argues that the rules of the Turing test are set from a machine’s perspective, making it inherently inconsistent. It is suggested that the rules of the game must be modified to let the special features of natural intelligence be expressed. For example, the Turing test doesn’t have causality built-in, as there are no rewards or punishments. For a “game”, this is a quite unusual premise. Designing the test to better evaluate learning processes would make it harder to pass for an AI, but perhaps bring it closer to detecting what the test is really after. I believe that we since the days when Turing made his definition have expanded on the notion of human intelligence and begun to appreciate the different forms it can take. One example is autism, where there often lies great intelligence behind a façade practically non-penetrable due to lack of higher cognitive abilities. It has to some extent been the increased use of computers and machine intelligence (as a bridge for communication) that has opened up to reveal some people’s true level of intellect. Many persons lacking higher level cognitive abilities would probably fail the Turing Test. This means that the test can’t really be said to detect humanness. It also leads to the conclusion that a machine could still be intelligent, even though it fails the test. Only a positive result in the test gives a certain answer – despite failing the test the machine can still be intelligent, but without the capability of imitating common human behaviour, similarly to many people. In the same way that our everyday definition of intelligence has to be revised to include some deviating cases of human intellect, forcing the definition of machine intelligence into reaching a human level in cognitive tasks seems hard to justify. As pointed out by Negnevitsky (2002), trying to reach this elusive goal might be pointless. Hence, a relevant question is whether the Turing Test really sets appropriate goals for AI research. The same problems are encountered as with a traditional, human intelligence test – are we really testing for desirable or relevant abilities? This of course depends on the context in which the test is executed and what the results will be used for. Perhaps a model similar to Howard Gardner’s theory of multiple “Many thinkers have urged that large differences in language lead to large differences in experience and thought. They hold that each language embodies a worldview, with quite different languages embodying quite different views, so that speakers of different languages think about the world in quite different ways. This view is sometimes called the Whorf-hypothesis or the Whorf-Sapir hypothesis, after the linguists who made it famous.” (Swoyer, Stanford Encyclopedia of Philosophy) 3 intelligences could be applicable to AI. For each unique case, different desired profiles or personalities could be developed for the AI. What is often implicitly meant by natural intelligence is the cognitive abilities of individual primates. In order not to loose sight of other systems of intelligence, collective intelligence should be mentioned. Out of the different discussions on intelligence that I’ve so far come across, the large topic of collective intelligence has appeared most intriguing and relevant to the developments of our time. The phenomena occurs in colonies of bacteria, insects and humans alike. In fact, distributed intelligence is a large area of study with a close relationship to AI. It researches how people and computers collectively can act more intelligently than any individual. (Handbook of Collective Intelligence). My view is that computers should be designed to complement human intelligence in the best possible way. This could mean a computer capable of dealing with human input and fully able to understand us, but without necessarily simulating our, sometimes inefficient, ways of reasoning and communicating. AI should be used to help solve complex problems, using the collective capabilities of humans and machine. NASA’s recent discovery of microbes able to live off arsenic means we are currently redefining the conditions necessary for the existence of life. What about our minds? Is the progress made in cognitive science and related disciplines leading to any redefinitions of intelligence? The distinction of natural and artificial seems obvious only as long as you care about the process, the “machinery” directing behaviour. The newly discovered arsenic microbes proves that organisms can use biochemistry in ways we’ve never dreamed of, and it provides us with more places to look for life. In the search for thought, a more open definition of the word can likewise enable us to see things that we wouldn’t have seen otherwise. If we are too narrow-minded in what we are looking for, we might be missing out on valuable intelligence, be it natural or artificial. I believe it is more interesting to discuss what things in this world can be and try to imagine new ways of being, rather than spending too much time and intelligence defining the boundaries. Works Cited Ben-Jacob, Eshel and Shapira, Yoash. 2008 “Meaning-Based Natural Intelligence Vs. Information-Based Artificial Intelligence” http://en.scientificcommons.org/42513974 Cole, David. “The Chinese Room Argument” Stanford Encyclopedia of Philosophy http://plato.stanford.edu/entries/chinese-room/#3 Last updated: Sep 22, 2009 Date of access: 09 Dec, 2010 Handbook of Collective Intelligence, MIT Center for Colletive Intelligence. http:// scripts.mit.edu/~cci/HCI/index.php?title=Main_Page Last updated Sep 19, 2010. Date of access: Dec 5, 2010 Hauser, Larry. “Chinese Room Argument” Internet Encyclopedia of Philosophy http://www.iep.utm.edu/chineser/ Last updated: July 27, 2005 Date of access: 09 Dec, 2010 Negnevitsky, Michael. Artificial Intelligence – A Guide to Intelligent Systems Harlow, England: Addison-Wesley, 2002 “Strong AI vs. weak AI” Wikipedia http://en.wikipedia.org/wiki/Strong_AI_vs._weak_AI Last modified: May 17, 2008 Date of access: 09 Dec, 2010 Swoyer, Chris. “The Linguistic Relativity Hypothesis” Stanford Encyclopedia of Philosophy http://plato.stanford.edu/entries/relativism/supplement2.html Year of publication: Feb 2, 2003 Date of access: 06 Dec, 2010 Thomason, Richmond. “Logic and Artificial Intelligence” Stanford Encyclopedia of Philosophy http://plato.stanford.edu/entries/logic-ai/ Last updated: May 9, 2008 Date of access: 07 Dec, 2010 Turing, Alan. 1950 "Computing Machinery and Intelligence", Mind http://mind.oxfordjournals.org/content/LIX/236/433