https://www.youtube.com/watch?v=UdE-W30oOXo

Brain vs. Computer

• In AI, we compare the brain (or the mind) and the computer
• Our hope: the brain is a form of computer
• Our goal: we can create computer intelligence through programming just as people become intelligent by learning

But we see that the computer is not like the brain

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The computer performs tasks without understanding what its doing

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Does the brain understand what its doing when it solves problems?

So What Is AI?

• AI as a field of study
• Computer Science
• Cognitive Science
• Psychology
• Philosophy
• Linguistics
• Neuroscience
• AI is part science, part engineering
• AI often must study other domains in order to implement systems
• e.g., medicine and medical practices for a medical diagnostic system, engineering and chemistry to monitor a chemical processing plant
• AI is a belief that the brain is a form of biological computer and that the mind is computational
• AI has had a concrete impact on society but unlike other areas of CS, the impact is often
• felt only tangentially (that is, people are not aware that system X has AI)
• felt years after the initial investment in the technology

Importance of AI

• Self Driving car
• Playing chess with computers

Artificial Intelligence

History of AI

https://www.youtube.com/watch?v=yaL5ZMvRRqE

Foundations of Artificial Intelligence:�

• Philosophy
• e.g., foundational issues (can a machine think?), issues of knowledge and believe, mutual knowledge
• Psychology and Cognitive Science
• e.g., problem solving skills
• Neuro-Science
• e.g., brain architecture

Foundations of Artificial Intelligence:

• Computer Science And Engineering
• e.g., complexity theory, algorithms, logic and inference, programming languages, and system building.
• Mathematics and Physics
• e.g., statistical modeling, continuous mathematics,
• Statistical Physics, and Complex Systems.

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A Brief History of AI: 1950s

• Computers were thought of as an electronic brains
• Term “Artificial Intelligence” coined by John McCarthy
• John McCarthy also created Lisp in the late 1950s
• Alan Turing defines intelligence as passing the Imitation Game (Turing Test)
• AI research largely revolves around toy domains
• Computers of the era didn’t have enough power or memory to solve useful problems
• Problems being researched include
• games (e.g., checkers)
• primitive machine translation
• blocks world (planning and natural language understanding within the toy domain)
• early neural networks researched: the perceptron
• automated theorem proving and mathematics problem solving

The 1960s

• AI attempts to move beyond toy domains
• Syntactic knowledge alone does not work, domain knowledge required
• Early machine translation could translate English to Russian (“the spirit is willing but the flesh is weak” becomes “the vodka is good but the meat is spoiled”)
• Earliest expert system created: Dendral
• Perceptron research comes to a grinding halt when it is proved that a perceptron cannot learn the XOR operator
• US sponsored research into AI targets specific areas – not including machine translation
• Weizenbaum creates Eliza to demonstrate the futility of AI

1970s

• AI researchers address real-world problems and solutions through expert (knowledge-based) systems
• Medical diagnosis
• Speech recognition
• Planning
• Design
• Uncertainty handling implemented
• Fuzzy logic
• Certainty factors
• Bayesian probabilities
• AI begins to get noticed due to these successes
• AI research increased
• AI labs sprouting up everywhere
• AI shells (tools) created
• AI machines available for Lisp programming
• Criticism: AI systems are too brittle, AI systems take too much time and effort to create, AI systems do not learn

1980s: AI Winter

• Funding dries up leading to the AI Winter
• Too many expectations were not met
• Expert systems took too long to develop, too much money to invest, the results did not pay off
• Neural Networks to the rescue!
• Expert systems took programming, and took dozens of man-years of efforts to develop, but if we could get the computer to learn how to solve the problem…
• Multi-layered back-propagation networks got around the problems of perceptrons
• Neural network research heavily funded because it promised to solve the problems that symbolic AI could not
• By 1990, funding for neural network research was slowly disappearing as well
• Neural networks had their own problems and largely could not solve a majority of the AI problems being investigated
• Panic! How can AI continue without funding?

Sub Areas of AI

What is Search?

• We define the state of the problem being solved as the values of the active variables
• this will include any partial solutions, previous conclusions, user answers to questions, etc

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• while humans are often able to make intuitive leaps, or recall solutions with little thought, the computer must search through various combinations to find a solution
• To the right is a search space for a tic-tac-toe game

Search Spaces and Types of Search

• The search space consists of all possible states of the problem as it is being solved
• A search space is often viewed as a tree and can very well consist of an exponential number of nodes making the search process intractable
• Search spaces might be pre-enumerated or generated during the search process
• Some search algorithms may search the entire space until a solution is found, others will only search parts of the space, possibly selecting where to search through a heuristic
• Search spaces include
• Game trees like the tic-tac-toe game
• Decision trees (see next slides)
• Combinations of rules to select in a production system
• Networks of various forms (see next slides)
• Other types of spaces

Search Algorithms and Representations

• Breadth-first
• Depth-first
• Best-first (Heuristic Search)
• A*
• Hill Climbing
• Limiting the number of Plies
• Minimax
• Alpha-Beta Pruning
• Adding Constraints
• Genetic Algorithms
• Forward vs Backward Chaining

• We will study various forms of representation and uncertainty handling in the next class period
• Knowledge needs to be represented
• Production systems of some form are very common
• If-then rules
• Predicate calculus rules
• Operators
• Other general forms include semantic networks, frames, scripts
• Knowledge groups
• Models, cases
• Agents
• Ontologies

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Today: The New (Old) AI

• Look around, who is doing AI research?
• By their own admission, AI researchers are not doing “AI”, they are doing
• Intelligent agents, multi-agent systems/collaboration
• Ontologies
• Machine learning and data mining
• Adaptive and perceptual systems
• Robotics, path planning
• Search engines, filtering, recommendation systems
• Areas of current research interest:
• NLU/Information Retrieval, Speech Recognition
• Planning/Design, Diagnosis/Interpretation
• Sensor Interpretation, Perception, Visual Understanding
• Robotics
• Approaches
• Knowledge-based
• Ontologies
• Probabilistic (HMM, Bayesian Nets)
• Neural Networks, Fuzzy Logic, Genetic Algorithms

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