Artificial_intelligence.pptx

Areas of AI and Some
Dependencies
Search
Vision
Planning
Machine
Learning
Knowledge
Representation
Logic
Expert
Systems
Robotics
NLP

What is Artificial Intelligence ?
• making computers that think?
• the automation of activities we associate with human thinking,
like decision making, learning ... ?
• the art of creating machines that perform functions that
require intelligence when performed by people ?
• the study of mental faculties through the use of computational
models ?

• the study of computations that make it possible to
perceive, reason and act ?
• a field of study that seeks to explain and emulate
intelligent behaviour in terms of computational processes
?
• a branch of computer science that is concerned with the
automation of intelligent behaviour ?
• anything in Computing Science that we don't yet know
how to do properly ? (!)

AI Definitions
• The study of how to make programs/computers do things that
people do better
• The study of how to make computers solve problems which
require knowledge and intelligence
• The exciting new effort to make computers think … machines
with minds
• The automation of activities that we associate with human
thinking (e.g., decision-making, learning…)
• The art of creating machines that perform functions that
require intelligence when performed by people
• The study of mental faculties through the use of computational
models
• A field of study that seeks to explain and emulate intelligent
behavior in terms of computational processes
• The branch of computer science that is concerned with the
automation of intelligent behavior
Thinking
machines or
machine
intelligence
Studying
cognitive
faculties
Problem
Solving and
CS

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

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?

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?

Systems that act
rationally
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
HUMAN RATIONAL

Systems that act like humans:
Turing Test
• “The art of creating machines that perform
functions that require intelligence when
performed by people.” (Kurzweil)
• “The study of how to make computers do
things at which, at the moment, people are
better.” (Rich and Knight)

Systems that act like humans
• You enter a room which has a computer
terminal. You have a fixed period of time to type
what you want into the terminal, and study the
replies. At the other end of the line is either a
human being or a computer system.
• If it is a computer system, and at the end of the
period you cannot reliably determine whether it
is a system or a human, then the system is
deemed to be intelligent.
?

• The Turing test was developed by
Alan Turing in the mid-1900s, it is a
test that is able to quantify
machines ability for cognitive
thinking and directly correlate this
with the thinking of a human mind.
If the machine is able to interact
with a human by online instant
messaging, the receiving human
would not be able to determine
whether the sender is human or
machine.

• The Turing Test is for determining whether a
computer can actually think. Many papers
regarding the test summarize the imitation
game, while discussing variations of the tests,
evaluating the standards for passing and
considering the rules a computer must follow
to be able to pass the test.

• Engineers have used the Turing test to help aid
online security. If a ‘bot’ is unable to
understand the GUI it is unable to gain access
to the site. Example: when validating an email
address for an online site you will usually have
to look at a small image and type in the
alphanumerical value to validate the
transaction.

• From 1950 to 2003, many controversial topics
regarding the Turing test were raised, one of
these topics was “Can machines think?” at the
beginning of Computing and Machinery and
Intelligence, inside the section The Imitation
Game. This has and will be a controversial
topic for the considerable future.

“Even though Turing made it clear that his
new question merely replaces how can
machines think?", some people claim that
Turing intended an equivalent or even a
stronger definition of this question. A few of
these people further believe that Turing stated
that any machine could be considered
intelligent if it passed the Turing test.

• There have been reports of the Turing Test,
and whether or not it is suitable for the
purpose of depicting a machines thinking
process. It concludes with the ironic fact that
if a machine were to pass the Turing Test that
it will have most probably used the methods
of the test in order to beat it

• Many have followed and studied the course of
computer intelligence following Alan Turing's
development of the Turing Test in
1950. Papers related discuss test as a
benchmark for the validation of a computer
system becoming intelligent and explores the
multiple interpretations of Turing's concepts
of organized and unorganized machinery.

• Researchers have explored the philosophical
debates, practical developments and
repercussions of the Turing Test over the 50
since Turing's conception of the imitation
game. It discusses Turing's ideas in detail,
theorizing over the decades long
interpretations of the test and important
comments relating to its sociological and
psychological influences.

• The Turing Test approach
– a human questioner cannot tell if
• there is a computer or a human answering his
question, via teletype (remote communication)
– The computer must behave intelligently
• Intelligent behavior
– to achieve human-level performance in all
cognitive tasks

• These cognitive tasks include:
– Natural language processing
• for communication with human
– Knowledge representation
• to store information effectively & efficiently
– Automated reasoning
• to retrieve & answer questions using the stored
information
– Machine learning
• to adapt to new circumstances

The total Turing Test
• Includes two more issues:
– Computer vision
• to perceive objects (seeing)
– Robotics
• to move objects (acting)

Systems that think like humans:
cognitive modeling
• Humans as observed from ‘inside’
• How do we know how humans think?
– Introspection vs. psychological experiments
• Cognitive Science
• “The exciting new effort to make computers think
… machines with minds in the full and literal
sense” (Haugeland)
• “[The automation of] activities that we associate
with human thinking, activities such as decision-
making, problem solving, learning …” (Bellman)

Systems that think ‘rationally’
"laws of thought"
• Humans are not always ‘rational’
• Rational - defined in terms of logic?
• Logic can’t express everything (e.g. uncertainty)
• Logical approach is often not feasible in terms of
computation time (needs ‘guidance’)
• “The study of mental facilities through the use of
computational models” (Charniak and
McDermott)
• “The study of the computations that make it
possible to perceive, reason, and act” (Winston)

Systems that act rationally:
“Rational agent”
• Rational behavior: doing the right thing
• The right thing: that which is expected to
maximize goal achievement, given the
available information
• Giving answers to questions is ‘acting’.
• I don't care whether a system:
–replicates human thought processes
–makes the same decisions as humans
–uses purely logical reasoning

Systems that act rationally
• Logic  only part of a rational agent, not all of
rationality
– Sometimes logic cannot reason a correct
conclusion
– At that time, some specific (in domain) human
knowledge or information is used
• Thus, it covers more generally different situations of
problems
– Compensate the incorrectly reasoned conclusion

Systems that act rationally
• Study AI as rational agent –
2 advantages:
– It is more general than using logic only
• Because: LOGIC + Domain knowledge
– It allows extension of the approach with more
scientific methodologies

Rational agents
 An agent is an entity that perceives and acts
 This course is about designing rational agents
 Abstractly, an agent is a function from percept histories to
actions:

[f: P*  A]
 For any given class of environments and tasks, we seek the
agent (or class of agents) with the best performance
 Caveat: computational limitations make perfect rationality
unachievable
  design best program for given machine resources


• Artificial
– Produced by human art or effort, rather than
originating naturally.
• Intelligence
• is the ability to acquire knowledge and use it" [Pigford
and Baur]
• So AI was defined as:
– AI is the study of ideas that enable computers to
be intelligent.
– AI is the part of computer science concerned with
design of computer systems that exhibit human
intelligence(From the Concise Oxford Dictionary)

From the above two definitions, we can see that
AI has two major roles:
– Study the intelligent part concerned with humans.
– Represent those actions using computers.

Goals of AI
• To make computers more useful by letting
them take over dangerous or tedious tasks
from human
• Understand principles of human intelligence

The Foundation of AI
• Philosophy
– At that time, the study of human intelligence
began with no formal expression
– Initiate the idea of mind as a machine and its
internal operations

Mathematics formalizes the three main area of
AI: computation, logic, and probability
 Computation leads to analysis of the problems that
can be computed
 complexity theory
 Probability contributes the “degree of belief” to
handle uncertainty in AI
 Decision theory combines probability theory and
utility theory (bias)

• Psychology
– How do humans think and act?
– The study of human reasoning and acting
– Provides reasoning models for AI
– Strengthen the ideas
• humans and other animals can be considered as
information processing machines

• Computer Engineering
– How to build an efficient computer?
– Provides the artifact that makes AI application
possible
– The power of computer makes computation of
large and difficult problems more easily
– AI has also contributed its own work to computer
science, including: time-sharing, the linked list
data type, OOP, etc.

• Control theory and Cybernetics
– How can artifacts operate under their own control?
– The artifacts adjust their actions
• To do better for the environment over time
• Based on an objective function and feedback from the
environment
– Not limited only to linear systems but also other
problems
• as language, vision, and planning, etc.

• Linguistics
– For understanding natural languages
• different approaches has been adopted from the
linguistic work
– Formal languages
– Syntactic and semantic analysis
– Knowledge representation

– more powerful and more useful computers
– new and improved interfaces
– solving new problems
– better handling of information
– relieves information overload
– conversion of information into knowledge
Some Advantages of Artificial
Intelligence

The Disadvantages
– increased costs
– difficulty with software development - slow and
expensive
– few experienced programmers
– few practical products have reached the market
as yet.

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

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

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 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?

1990s: ALife
• The dumbest smart thing you can do is staying alive
– We start over – lets not create intelligence, lets just create
“life” and slowly build towards intelligence
• Alife is the lower bound of AI
– Alife includes
• evolutionary learning techniques (genetic algorithms)
• artificial neural networks for additional forms of learning
• perception and motor control
• adaptive systems
• modeling the environment
• Let’s disguise AI as something new, maybe we’ll get
some funding that way!
– Problems: genetic algorithms are useful in solving some
optimization problems and some search-based problems, but
not very useful for expert problems
– perceptual problems are among the most difficult being
solved, very slow progress

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

The main topics in AI
Artificial intelligence can be considered under a number of
headings:
– Search (includes Game Playing).
– Representing Knowledge and Reasoning with it.
– Planning.
– Learning.
– Natural language processing.
– Expert Systems.
– Interacting with the Environment
(e.g. Vision, Speech recognition, Robotics)

Search
• Search is the fundamental technique of AI.
– Possible answers, decisions or courses of action are structured into an
abstract space, which we then search.
• Search is either "blind" or “uninformed":
– blind
• we move through the space without worrying about
what is coming next, but recognising the answer if we
see it
– informed
• we guess what is ahead, and use that information to
decide where to look next.
• We may want to search for the first answer that satisfies our goal, or we
may want to keep searching until we find the best answer.

Knowledge Representation & Reasoning
• The second most important concept in AI
• If we are going to act rationally in our environment, then we must have
some way of describing that environment and drawing inferences from
that representation.
– how do we describe what we know about the world ?
– how do we describe it concisely ?
– how do we describe it so that we can get hold of the right piece of
knowledge when we need it ?
– how do we generate new pieces of knowledge ?
– how do we deal with uncertain knowledge ?

Knowledge
Declarative Procedural
• Declarative knowledge deals with factoid questions
(what is the capital of India? Etc.)
• Procedural knowledge deals with “How”
• Procedural knowledge can be embedded in
declarative knowledge

Planning
Given a set of goals, construct a sequence of actions that achieves
those goals:
– often very large search space
– but most parts of the world are independent of most other parts
– often start with goals and connect them to actions
– no necessary connection between order of planning and order of
execution
– what happens if the world changes as we execute the plan and/or
our actions don’t produce the expected results?

Learning
• If a system is going to act truly appropriately,
then it must be able to change its actions in
the light of experience:
–how do we generate new facts from old ?
–how do we generate new concepts ?
–how do we learn to distinguish different
situations in new environments ?

Symbolic and Sub-symbolic AI
• Symbolic AI is concerned with describing and
manipulating our knowledge of the world as explicit
symbols, where these symbols have clear relationships to
entities in the real world.
• Sub-symbolic AI (e.g. neural-nets) is more concerned with
obtaining the correct response to an input stimulus
without ‘looking inside the box’ to see if parts of the
mechanism can be associated with discrete real world
objects.
• This course is concerned with symbolic AI.

AI Applications
• Autonomous Planning
& Scheduling:
– Autonomous rovers.

AI Applications
• Autonomous Planning & Scheduling:
– Telescope scheduling

AI Applications
• Autonomous Planning & Scheduling:
– Analysis of data:

AI Applications
• Medicine:
– Image guided surgery

AI Applications
• Medicine:
– Image analysis and enhancement

AI Applications
• Transportation:
– Autonomous
vehicle control:

AI Applications
• Transportation:
– Pedestrian detection:

AI Applications
• Robotic toys:

AI Applications
Other application areas:
• Bioinformatics:
– Gene expression data analysis
– Prediction of protein structure
• Text classification, document sorting:
– Web pages, e-mails
– Articles in the news
• Video, image classification
• Music composition, picture drawing
• Natural Language Processing .
• Perception.

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