Artificial Intelligence: The Turing Test

Introduction

Artificial Intelligence has been a fascinating topic in science fiction for decades, whether it came in the form of the obsessively logical supercomputer HAL (2001: a Space Odyssey) or as the genocidal Skynet (Terminator); unfortunately, the term AI has garnered a very negative reputation from the many examples of “rogue AIs” in fiction. This idea of a thinking machine that is both like us and yet not like us derives from the man many think of as one of the fathers of modern computers, Alan Turing [4]. The Turing Test, proposed in 1950, was designed by Turing to see if a computer could convince a person it was a human being under controlled conditions [4]. This is the basis for the main sub-theme underpinning most fictional Artificial

How does it solve problems? There are three types of feedback that determine the three main types of learning: “In unsupervised learning, the agent (computer program) learns from patterns in the input, even though no explicit feedback is supplied. The most common unsupervised learning task is clustering: detecting potentially useful clusters of input examples. For example, a taxi agent might gradually develop a concept of 'good traffic days' and 'bad traffic days' without ever being given labeled examples of each by a teacher. In reinforcement learning, the agent learns from a series of reinforcements-- rewards or punishments. For example, the lack of a tip at the end of the journey gives the taxi agent an indication it did something wrong... it is up to the agent to decide which of the actions prior to the reinforcement were most responsible for it. In supervised learning, the agent observes some example input-output pairs and learns a function that maps from input to output... the output value is available directly from the agent's perception; the environment is the teacher” [4]. Utilizing this feedback can be done with a “Decision Tree induction algorithm” [4], which is a function that relies on a sequence of tests to find a Boolean classification (True or False) [4]. This sort of pattern matching decision making is excellent for many fields, including speech recognition, flight

Remarks made during this conference concluded that the major achievements of AI are going to be reached soon [7]. Artificial Intelligence is a major component in many solutions to areas in medicine such as logistics, data mining, image processing, genetics and molecular medicine [7]. The power and flexibility these solutions can provide better healthcare options for patients as well as a lower chance of a misdiagnosis [6]. Mario Stefanelli, a panelist at the AIME conference in 2007, had this to say about one important aspect of AI: “Knowledge management (KM) is one of the most interesting AI fields. The goal of KM is to improve organizational performance by enabling individuals to capture, share and apply their collective knowledge to make optimal 'decisions in real time'... the new main goals of health care organizations are safety, efficiency and effectiveness, centrality of the patient, continuity of care, care quality and access equity. As a consequence, medical KM and health care process management are crucial to achieve the desired quality” [7]. Peter Szolovits, one of Stefanelli's co-panelists, says “AI in medicine is viewed today much less as a separate field and more as an essential component of biomedical informatics and one of the methodologies that can help solve problems in health care” [7]. Data is the lifeblood of