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 – 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)

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.

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

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

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”

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

Practical applications of AI Knowledge bases Expert systems

AI techniques Heuristics Pattern recognition Machine learning

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

Knowledge Base A computer KB will 1.Incorporate a database of facts 2.Incorporate a series of programmed rules 3.Attempt to derive new facts by applying steps 1 and 2

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.

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:

Difficult Airway Algorithm

ACLS Algorithm – Cardiac Arrest

Pulmonary HTN Algorithm:

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

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