•
So
AI as a field of study
What Is AI?
– 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
What is Intelligence?
• Is there a “holistic” definition for intelligence?
• Here are some definitions:
– the ability to comprehend; to understand and profit from experience
– a general mental capability that involves the ability to reason, plan, solve
problems, think abstractly, comprehend ideas and language, and learn
– is effectively perceiving, interpreting and responding to the environment
• None of these tells us what intelligence is, so instead, maybe we
can enumerate a list of elements that an intelligence must be
able to perform:
– perceive, reason and infer, solve problems, learn and adapt, apply
common sense, apply analogy, recall, apply intuition, reach emotional
states, achieve self-awareness
• Which of these are necessary for intelligence? Which are
sufficient?
• Artificial Intelligence – should we define this in terms of human
intelligence?
– does AI have to really be intelligent?
– what is the difference between being intelligent and demonstrating
intelligent behavior?
Table-Lookup vs. Reasoning
• Consider two approaches to programming a Tic-Tac-Toe player
– Solution 1: a pre-enumerated list of best moves given the board
configuration
– Solution 2: rules (or a heuristic function) that evaluate a board
configuration, and using these to select the next best move
• Solution 1 is similar to how Eliza works
– This is not practical for most types of problems
– Consider solving the game of chess in this way, or trying to come up with
all of the responses that a Turing Test program might face
• Solution 2 will reason out the best move
– Given the board configuration, it will analyze each available move and
determine which is the best
– Such a player might even be able to “explain” why it chose the move it did
• We can (potentially) build a program that can pass the Turing Test
using table-lookup even though it would be a large undertaking
• Could we build a program that can pass the Turing Test using
reasoning?
– Even if we can, does this necessarily mean that the system is intelligent?
But Computers Solve Problems
• We can clearly see that computers solve problems in a
seemingly intelligent way
– Where is the intelligence coming from?
• There are numerous responses to Searle’s argument
– The System’s Response:
• the hardware by itself is not intelligent, but a combination of the
hardware, software and storage is intelligent
• in a similar vein, we might say that a human brain that has had no
opportunity to learn anything cannot be intelligent, it is just the
hardware
– The Robot Response:
• a computer is void of senses and therefore symbols are meaningless to
it, but a robot with sensors can tie its symbols to its senses and thus
understand symbols
– The Brain Simulator Response:
• if we program a computer to mimic the brain (e.g., with a neural
network) then the computer will have the same ability to understand
as a human brain
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
The computer performs tasks
without understanding what
its doing
Does the brain understand
what its doing when it solves
problems?
Two AI Assumptions
• We can understand and model cognition without
understanding the underlying mechanism
– That is, it is the model of cognition that is important not the
physical mechanism that implements it
– If this is true, then we should be able to create cognition (mind)
out of a computer or a brain or even other entities that can
compute such as a mechanical device
• This is the assumption made by symbolic AI researchers
• Cognition will emerge from the proper mechanism
– That is, the right device, fed with the right inputs, can learn and
perform the problem solving that we, as observers, call
intelligence
– Cognition will arise as the result (or side effect) of the hardware
• This is the assumption made by connectionist AI researchers
• Notice that while the two assumptions differ, neither is
necessarily mutually exclusive and both support the idea
that cognition is computational
Problems with Symbolic AI Approaches
• Scalability
– It can take dozens or more man-years to create a useful
systems
– It is often the case that systems perform well up to a certain
threshold of knowledge (approx. 10,000 rules), after which
performance (accuracy and efficiency) degrade
• Brittleness
– Most symbolic AI systems are programmed to solve a
specific problem, move away from that domain area and the
system’s accuracy drops rapidly rather than achieving a
graceful degradation
• this is often attributed to lack of common sense, but in truth, it is a lack
of any knowledge outside of the domain area
– No or little capacity to learn, so performance (accuracy) is
static
• Lack of real-time performance
So What Does AI Do?
• Most AI research has fallen into one of two categories
– Select a specific problem to solve
• study the problem (perhaps how humans solve it)
• come up with the proper representation for any knowledge needed to
solve the problem
• acquire and codify that knowledge
• build a problem solving system
– Select a category of problem or cognitive activity (e.g.,
learning, natural language understanding)
• theorize a way to solve the given problem
• build systems based on the model behind your theory as experiments
• modify as needed
• Both approaches require
– one or more representational forms for the knowledge
– some way to select proper knowledge, that is, search
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
