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Artificial Intelligence (AI)

Artificial Intelligence (AI). Can Machines Think?. Advantage computer:. Calculate Communicate Process information Storage and recall of facts Make decisions using established rules of logic Consistency/Rationality e.g. rejection of anecdotal evidence. Advantage human:. Perceive Reason

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Artificial Intelligence (AI)

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  1. Artificial Intelligence (AI) Can Machines Think?

  2. Advantage computer: • Calculate • Communicate • Process information • Storage and recall of facts • Make decisions using established rules of logic • Consistency/Rationality • e.g. rejection of anecdotal evidence

  3. Advantage human: • Perceive • Reason • Not all possibilities can be anticipated, and therefore programmed • Recognize patterns • Unless a specific pattern has been anticipated and ‘programmed’, a computer can’t act on it • Ambiguity • Application of knowledge (child describing his toys)

  4. So, can they think?? • The “Turing Test” • Developed by Alan Turing (1950) • A person sits at a computer and types questions into a terminal. • If a computer were truly “intelligent”, the questioner would not be able to determine whether the responder was a human or a computer • To date, no computer has even come close • Some still consider the Turing Test to be the best determinant of AI. Other researchers favor a more lenient definition.

  5. Defining AI • Hard to define • Many disagree • “…ability to perceive, reason, and act” • “…do things which, at the moment, people are better” • etc

  6. Was Deep Blue “intelligent”? • Deep Blue was a computer developed by IBM that defeated Kasparov in chess. • Rules were clearly defined • Objectives were unmistakable • Searching: Winning typically goes to the player who can sift through the greatest number of possibilities and outcomes • Recall: Pattern recognition of board patterns and best strategies to employ given a specific pattern • Humans may have the edge here… • $25 chess programs can defeat the greatest players in the world

  7. Language Translation • Still work to be done… • Shakespeare: “The spirit is willing, but the flesh is weak” • Computer: “The wine is agreeable, but the meat is rotten” • “Out of sight, out of mind” • Computer: “Invisible idiot”

  8. Syntax vs Semantics • Language rarely limits itself to a consistent set of rules and structure • There are always “exceptions” • Sometimes, understanding the true, underlying meaning of a single word can require a great deal of knowledge • Syntax: the ‘rules’ of a language, definitions of words • Semantics: the underlying meanings • Expressions • Idioms • Slang • Visual cues • Ambiguity: e.g. All that glitters is not gold. • Etc

  9. Practical applications of AI • Knowledge bases • Expert systems

  10. AI techniques • Heuristics • Pattern recognition • Machine learning

  11. Knowledge vs Facts • Facts are details that are typically quantifiable and reproducible • Knowledge is the ability to form relationships by using facts • Humans are considerably better at inferring things • Computer require tremendous input of data to accomplish this same task, and even then, will inevitably fall short at some point

  12. Knowledge Base • A computer KB will • Incorporate a database of facts • Incorporate a series of programmed rules • Attempt to derive new facts by applying steps 1 and 2

  13. Expert Systems • “A software program designed to replicate the decision making process of a human expert” • A collection of specialized knowledge where facts and appropriate actions are obtained from expert sources and programmed into a database • Usually involves a series of “IfThen” question and answers.

  14. Algorithms • An expert system will frequently use a series of algorithms to provide solutions to a given question • Here are a couple of examples of well-established medical algorithms:

  15. Difficult Airway Algorithm

  16. ACLS Algorithm – Cardiac Arrest

  17. Pulmonary HTN Algorithm:

  18. Fuzzy Logic • Uncertainty is an inevitable part of the human experience • Computers do not handle ambiguity well • Computers use likelihood (e.g. percentages) – derived from as much factual data as possible – to come up with the “best” solution

  19. Expert Systems - examples • Training • Teaching “difficult airway” procedure to anesthesiology residents • “What do you do next?” • Routine / repetitive task work • Monitoring millions of credit card accounts for unusual activity • Expertise when human help is not available • PDAs with medical databases • Error reduction • Checking for drug interactions in an EMR

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