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CSCI-100Introduction to Computing
Artificial Intelligence
What is AI?
• Artificial intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans
What is Intelligence?
• The ability to think and act rationally• The capacity to learn
– Consider the behavior of the digger wasp. When the wasp brings food to her nest, she puts it on the entrance, goes inside to check for intruders and, if the coast is clear, carries in the food. If you move the food a few inches while the wasp is inside checking, on emerging, the wasp repeats the whole procedure (i.e., it carries the food to the entrance, goes in to look around, and emerges again)
Dumb insect
What’s involved in Intelligence?
• Ability to interact with the real world– to perceive, understand, and act– speech recognition, understanding– Image understanding (computer vision)
• Reasoning and planning– Modeling the external world– Problem solving, planning, and decision making– Ability to deal with unexpected problems,
uncertainties
• Learning and Adaptation
What’s involved in Intelligence?
• Research in AI has focused mainly on the following components of intelligence– Learning– Reasoning– Problem Solving– Perception– Language Understanding
Strong AI
• Strong AI aims to build machines whose overall intellectual ability is indistinguishable from that of a human being– The ultimate goal of [strong] AI is nothing
less than to build a machine on the model of a man, a robot that is to have its childhood, to learn a language as a child does, to gain its knowledge of the world by sensing the world through its own organs, and ultimately to contemplate the whole domain of human thought [ Joseph Weizenbaum, MIT AI Laboratory ]
Applied AI
• Applied AI aims to produce commercially viable “smart” systems (e.g., a security system that is able to recognize the faces of people who are permitted to enter a particular building)
• Applied AI has enjoyed considerable success
Different Approaches
Systems that think like humans
Systems that think rationally
Systems that act like humans
Systems that act rationally
Thinking
Behavior
Against human performance
Against ideal concept of intelligence (rationality)
Thinking Humanly: Cognitive Science
• Claim: A given program thinks like a human– Must have some way of determining how humans think– Need to get inside the actual workings of human minds– After we have a theory of the mind, can express it as a
computer program– If program’s I/O and timing matches corresponding
human behavior, evidence that our theory is correct
Systems that think
like humans
Systems that think
rationally
Systems that act like
humans
Systems that act rationally
Thinking Humanly: Cognitive Science
• The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind
Thinking Rationally: Laws of Thought
• Aristotle– Attempted to codify “right thinking” (i.e., correct arguments)– His syllogisms provided patterns for argument structures that always
yielded correct conclusions given correct premisesSocrates is a man
All men are mortal
Socrates is mortal
• By 1965, programs existed that could, in principle, solve any solvable problem described in logical notation
• The logicist tradition within AI hopes to build on such programs to create intelligent systems
Systems that think
like humans
Systems that think
rationally
Systems that act like
humans
Systems that act rationally
Acting Humanly: The Turing Test
ELIZA, computer therapist
Systems that think
like humans
Systems that think
rationally
Systems that act like
humans
Systems that act rationally
Acting Humanly: The Turing Test
• Proposed by Alan Turing (1950) (the father of AI)• Based on indistinguishability from undeniably
intelligent entities (i.e., we)• Is a computer that passes the test really intelligent?• Programming a computer to pass the test provides
plenty to work on– Natural Language Processing
– Knowledge Representation
– Automated Reasoning
– Machine Learning
Acting Humanly: The Turing Test
• Physical simulation of a person unnecessary for intelligence thus no need for physical interaction between interrogator and computer
• Total Turing Test– Interrogator can test the subject’s perceptual abilities– Interrogator can pass physical objects “through the
hatch”– Computer would need computer vision, robotics
Acting Humanly: The Turing Test
• Today, AI researchers devote little effort to passing the test
• More important to study underlying principles of intelligence
• Turing Test suggested major components of AI– Natural Language Processing– Knowledge Representation– Automated Reasoning– Machine Learning– Computer Vision– Robotics
Acting Rationally: Rational Agents
• Rational behavior: doing the right thing
• The right thing: that which is expected to maximize goal achievement, given the available information
• Doesn’t necessarily involve thinking (e.g., blinking reflex) but thinking should be in the service of rational action Systems
that think like humans
Systems that think
rationally
Systems that act like
humans
Systems that act rationally
Acting Rationally: Rational Agents
• An agent is an entity that perceives and acts
• Examples of Agents?• Sensors and Actuators in…
– A Human Agent?– A Robotic Agent?
Acting Rationally: Rational Agents (Advantages)• In the “Laws of Thought” approach to AI, emphasis
is on correct inferences• Making correct inferences is sometimes part of
being a rational agent but not all of rationality. Why?– Sometimes there is no provably correct thing to do, yet
something must still be done– There are ways of acting rationally that cannot be said to
involve inference (e.g., recoiling from a hot stove)
• Thus, more general than the “Laws of Thought” approach
Acting Rationally: Rational Agents (Advantages)
• More suitable for scientific development that approaches based on human behavior or human thought because standard of rationality is clearly defined and completely general
AI History
• History of AI is commonly supposed to begin with Turing’s 1950 discussions of machine intelligence and to have been defined as a field at the 1956 Dartmouth Summer Research Project on Artificial Intelligence– But ideas on which AI is based, symbolic AI in
particular, have a very long history in the Western intellectual tradition, dating back to ancient Greece
Symbolic AI
• Approach to AI that has dominated the field throughout most of its history
• Based on the Physical Symbol System Hypothesis, enunciated by Newell and Simon (1976)– “A physical symbol system has the necessary and
sufficient means for general intelligent action”– Knowledge represented in brain by language-like
structures or formulas– Thinking is a computational process that rearranges
such structures according to formal rules
The Roots of Formal Logic
• In ancient Greece, pebbles were used for calculation in a similar way to beads on an abacus– Latin word for “pebble” is calculus– In logic and math, we use the word “calculus” for any
system of notation in which we can accomplish some purpose by manipulation of tokens according to formal, mechanical rules (e.g., propositional, predicate calculus)
– To the extent that such rules are purely mechanical, they can, in principle, be carried out by a machine
• If a process can be reduced to a calculus, it can be calculated by a machine
Neuroscience
• Neuroscience (1861 – present)– How do brains process information?
• Brain consists of brain nerve cells or neurons• A neuron makes connections with other neurons at junctions called synapses• Signals are propagated from neuron to neuron • The signals enable long-term changes in connectivity of neurons• Thought to form the basis for learning in the brain• A collection of simple cells can lead to thought, action, and consciousness or,
in other words, that brains cause minds (Searle, 1992)• Alternative is mysticism: there is a mystical realm in which minds operate that
is beyond physical science
Connectionism
• Has only recently become a serious contender to symbolic AI
• The level of the symbol is too high to lead to a good model of the mind– Have to go lower: instead of designing programs that
perform computations on such symbols, design programs that perform computations at a lower level (the neuron)
– When viewed at the semantic levels such systems often do not appear to be engaged in rule-following behavior (rules lie at a deeper level)