BAYESIAN LEARNING Abstract Uncertainty has presented a difficult obstacle in artificial intelligence. Bayesian learning outlines a mathematically solid method for dealing with uncertainty based upon Bayes' Theorem. The theory establishes a means for calculating the probability an event will occur in the future given some evidence based upon prior occurrences of the event and the posterior probability that the evidence will predict the event. Its use in artificial intelligence has been met with success in a number of research areas and applications including the development of cognitive models and neural networks. At the same time, the theory has been criticized for being philosophically unrealistic and logistically inefficient. Bayesian Learning The aim of artificial intelligence is to provide a computational model of intelligent behavior (Pearl, 1988). Expert systems are designed to embody the knowledge of an expert in a given field. But how do people become experts themselves? While artificial intelligence can produce Ph.D. quality experts, a more difficult challenge lies in creating a naive observer. The common sense people use in everyday reasoning provides one of the most difficult challenges in building intelligent systems. Common sense reasoning is often based on incomplete knowledge and is powerfully broad in its use. Intelligent systems have historically been successful in specific domains with well defined structures. To make them succeed in a broad arena, they would need either a greater base of knowledge or be able to deal with uncertainty and learn. In light of the fact that the former option is more demanding in resources and assumes that all the appropriate knowledge is obtainable, the latter is an attr... ... middle of paper ... ...cess. References Anderson, J. R. (1993). Rules of the Mind. New Jersey: Lawrence Erlbaum Associates. Anderson, J. R. (1991). Is human cognition adaptive? Behavior and Brain Sciences, 14, 471-517. Krueger, L. E. (1984). Perceived numerosity: A comparison of magnitude production, magnitude estimation, and discrimination judgements. Perception and Psychophysics, 35(6), 536-542. McCarthy, J. & Hayes, P. (1969). Some philosophical problems from the stand point of artificial intelligence. In B. Metltzer & D. Michie (Eds.), Machine Intelligence Vol. 4, (pp. 463-502). Edinburgh, U.K.: Edinburgh University Press. Neal, R. M. (1996). Bayesian Learning for Neural Networks. New York: Springer-Verlag. Pearl, J. P. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, California, USA: Morgan Kaufmann Publishers, Inc..
Andy Clark strongly argues for the theory that computers have the potential for being intelligent beings in his work “Mindware: Meat Machines.” The support Clark uses to defend his claims states the similar comparison of humans and machines using an array of symbols to perform functions. The main argument of his work can be interpreted as follows:
In this paper I will evaluate and present A.M. Turing’s test for machine intelligence and describe how the test works. I will explain how the Turing test is a good way to answer if machines can think. I will also discuss Objection (4) the argument from Consciousness and Objection (6) Lady Lovelace’s Objection and how Turing responded to both of the objections. And lastly, I will give my opinion on about the Turing test and if the test is a good way to answer if a machine can think.
Due to the unique nature of the intelligence field, error of judgments can (and has) had catastrophic consequences. These errors are a result of complex decision making processes involved in the generation of intelligence products, affected by not only training and expertise, but by cognitive factors, particularly bias. The aim of this paper is to identify two different models of decision making (bounded rationality and intuitive decision making), the biases found in both models that affect the final intelligence product, and how these biases can be mitigated in order to avoid intelligence failures or minimise their impact.
The Chinese room argument certainly shows a distinction between a human mind and strong AI. However, it seems that the depths of human understanding can also be a weakness to how it compares to strong AI and the way that knowledge and understanding is derived.
Artificial Intelligence (AI) is one of the newest fields in Science and Engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956 by John McCarthy. Artificial Intelligence is an art of creating machines that perform functions that require intelligence when performed by people [Kurzweil, 1990]. It encompasses a huge variety of subfields, ranging from general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on the crowded street, and diagnosing diseases. Artificial Intelligence is relevant to any intellectual task; it is truly a Universal field. In future, intelligent machines will replace or enhance human’s capabilities in
One of the hottest topics that modern science has been focusing on for a long time is the field of artificial intelligence, the study of intelligence in machines or, according to Minsky, “the science of making machines do things that would require intelligence if done by men”.(qtd in Copeland 1). Artificial Intelligence has a lot of applications and is used in many areas. “We often don’t notice it but AI is all around us. It is present in computer games, in the cruise control in our cars and the servers that route our email.” (BBC 1). Different goals have been set for the science of Artificial Intelligence, but according to Whitby the most mentioned idea about the goal of AI is provided by the Turing Test. This test is also called the imitation game, since it is basically a game in which a computer imitates a conversating human. In an analysis of the Turing Test I will focus on its features, its historical background and the evaluation of its validity and importance.
Many theories of logic use mathematical terms to show how premises lead to conclusions. The Bayesian confirmation theory relates directly to probability. When applying this theory, a logician must know the probability of a given situation, have a conditional rule, and then he or she must apply the probability when the conditional rule is applied. This theory is used to determine an outcome based on a given condition. The probability of a given situation is x, when y occurs, or the probability is z if it does not occur. If y occurs, then the outcome of the given would be x. For example, if there is a high probability that a storm will occur if a given temperature drops and there is no temperature change, then it will most likely not rain because the temperature did not change (Strevens, 2012). By using observational data such as weather patterns, a person can arrive at a logical prediction or conclusion that will most likely come true based...
Since antiquity the human mind has been intrigued by artificial intelligence hence, such rapid growth of computer science has raised many issues concerning the isolation of the human mind.
"Artificial Intelligence: Cannibal Or Missionary?." AI & Society 21.4 (2007): 651-657.Academic Search Complete. Web. 16 Nov. 2014.
... T. (1979, November). Why I am not an Objective Bayesian: Some Reflections Prompted by Rosenkranz. Theory and Decision pp.413-440.
The traditional notion that seeks to compare human minds, with all its intricacies and biochemical functions, to that of artificially programmed digital computers, is self-defeating and it should be discredited in dialogs regarding the theory of artificial intelligence. This traditional notion is akin to comparing, in crude terms, cars and aeroplanes or ice cream and cream cheese. Human mental states are caused by various behaviours of elements in the brain, and these behaviours in are adjudged by the biochemical composition of our brains, which are responsible for our thoughts and functions. When we discuss mental states of systems it is important to distinguish between human brains and that of any natural or artificial organisms which is said to have central processing systems (i.e. brains of chimpanzees, microchips etc.). Although various similarities may exist between those systems in terms of functions and behaviourism, the intrinsic intentionality within those systems differ extensively. Although it may not be possible to prove that whether or not mental states exist at all in systems other than our own, in this paper I will strive to present arguments that a machine that computes and responds to inputs does indeed have a state of mind, but one that does not necessarily result in a form of mentality. This paper will discuss how the states and intentionality of digital computers are different from the states of human brains and yet they are indeed states of a mind resulting from various functions in their central processing systems.
In order to see how artificial intelligence plays a role on today’s society, I believe it is important to dispel any misconceptions about what artificial intelligence is. Artificial intelligence has been defined many different ways, but the commonality between all of them is that artificial intelligence theory and development of computer systems that are able to perform tasks that would normally require a human intelligence such as decision making, visual recognition, or speech recognition. However, human intelligence is a very ambiguous term. I believe there are three main attributes an artificial intelligence system has that makes it representative of human intelligence (Source 1). The first is problem solving, the ability to look ahead several steps in the decision making process and being able to choose the best solution (Source 1). The second is the representation of knowledge (Source 1). While knowledge is usually gained through experience or education, intelligent agents could very well possibly have a different form of knowledge. Access to the internet, the la...
The conditions of the present scenario are as follows: a machine, Siri*, capable of passing the Turing test, is being insulted by a 10 year old boy, whose mother is questioning the appropriateness of punishing him for his behavior. We cannot answer the mother's question without speculating as to what A.M. Turing and John Searle, two 20th century philosophers whose views on artificial intelligence are starkly contrasting, would say about this predicament. Furthermore, we must provide fair and balanced consideration for both theorists’ viewpoints because, ultimately, neither side can be “correct” in this scenario. But before we compare hypothetical opinions, we must establish operant definitions for all parties involved. The characters in this scenario are the mother, referred to as Amy; the 10 year old boy, referred to as the Son; Turing and Searle; and Siri*, a machine that will be referred to as an “it,” to avoid an unintentional bias in favor of or against personhood. Now, to formulate plausible opinions that could emerge from Turing and Searle, we simply need to remember what tenants found their respective schools of thought and apply them logically to the given conditions of this scenario.
As the human brain has shown to only be able to comprehend a limited amount of information, this endless horizon for AI intelligence gives AI the likelihood of therefore exceeding human intelligence. This is rather one that has been educated. Being educated and having a high degree of intelligence are two distinctive definitions. The difference between intelligence and intelligence is one’s ability to apply what one has learned, instead of just storing information and being able to recall it.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?