20-03-2012, 11:16 AM
What is AI: Some Quick Answers
intro-search.ppt (Size: 140 KB / Downloads: 58)
What socially-inept superhackers do
…The opposite of natural stupidity
…Building useful idiot-savant programs
…Deep Blue (IBM’s chess program)
…Robots with feelings (Spielberg)
Applied Cognitive Science
Computational models of human reasoning
Problem solving
Scientific thinking
Models of non-introspective mental processes
Language comprehension, language learning
Human memory organization (STM, LTM)
Knowledge Engineering
Codify human knowledge for specific tasks
E.g.: Medical diagnosis, Machine Translation
Central in 1970s & 80s just one lecture here
Problem-Solving Methods
Machine Learning
Learning as the hallmark of intelligence…but it is already practical in multiple applications
E.g.: D-trees, rule-induction, reinforcement, NNets
Discredited in 1960s Vibrant core 90’s & 00’s
Applications: data & text mining, speech, robotics
Most active research area in AI many lectures
How to encode and use knowledge to find answer
E.g. HS, MEA, A*, Logic resolution
Always at the very core of AI many lectures
intro-search.ppt (Size: 140 KB / Downloads: 58)
What socially-inept superhackers do
…The opposite of natural stupidity
…Building useful idiot-savant programs
…Deep Blue (IBM’s chess program)
…Robots with feelings (Spielberg)
Applied Cognitive Science
Computational models of human reasoning
Problem solving
Scientific thinking
Models of non-introspective mental processes
Language comprehension, language learning
Human memory organization (STM, LTM)
Knowledge Engineering
Codify human knowledge for specific tasks
E.g.: Medical diagnosis, Machine Translation
Central in 1970s & 80s just one lecture here
Problem-Solving Methods
Machine Learning
Learning as the hallmark of intelligence…but it is already practical in multiple applications
E.g.: D-trees, rule-induction, reinforcement, NNets
Discredited in 1960s Vibrant core 90’s & 00’s
Applications: data & text mining, speech, robotics
Most active research area in AI many lectures
How to encode and use knowledge to find answer
E.g. HS, MEA, A*, Logic resolution
Always at the very core of AI many lectures