Artificial Intelligence(Introduction)

What is ‘intelligence’ ?
Intelligence is defined by some general characteristics:
 Perception: Manipulation & interpretation of data.
 Action: Control and use of effectors to accomplish a variety of tasks.
 Reasoning: Deductive (logical) inference, inductive inference.
 Learning: Adapting behavior to better cope with changing environments, discovery of
patterns, learning to reason, plan, and act.
 Communication with other intelligent agents including humans using signals, signs,
icons, etc.
 Planning: Formulation of plans - sequences or agenda of actions to accomplish
 externally or internally determined goals.
What AI Does?
 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.
 What is Artificial Intelligence ?

THOUGHT Systems that think Systems that think

like humans rationally

BEHAVIOUR Systems that act Systems that act

like humans rationally

HUMAN RATIONAL

Cognition, or thought processes should, at some level, be modeled by computation.

● 4 Approaches:
Elaborations
Approach I : Act like a Human
Turing Test

?
 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 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
 Systems that act like humans
 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
Approach II :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)
Approach III: 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)
Approach IV : Acting Rationally
(Rational Agent)
AI has two major roles:
 Study the intelligent part concerned with humans.
 Represent those actions using computers.
AI Task Domain
 Mundane Tasks
Perception : Vision , Speech
• Natural Language : Understanding, Generation, Translation
• Commonsense Reasoning
• Robot Control
 Formal Tasks
Games : Chess
Mathematics: Geometry, Logic, Proving
 Expert Task
Design, Fault Finding, Manufacturing Planning
 Scientific Analysis
 Medical Analysis
 Financial Analysis
Some AI Problem Characteristics
 Is problem decomposable?
 Can Solution steps ignored, undo (Recoverable) , Irrecoverable
 Is solution absolute or relative
 Is solution a state or path
 Classification Problem
 Role of knowledge
 Interaction with person/computer
Goals of AI
 To make computers more useful by letting them take over dangerous or tedious tasks
from human
 Understand principles of human intelligence
Foundations of AI
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
The Foundation of AI
 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
The Foundation of AI
 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.
 Linguistics
 For understanding natural languages
 different approaches has been adopted from the linguistic work
 Formal languages
 Syntactic and semantic analysis
 Knowledge representation
AI System Building Steps
 Define problem precisely
 Analyze the problem
 Isolate and represent task knowledge
 Choose best problem solving technique
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 ?
History of AI
 1943 - McCulloch & Pitts: Model of Artificial Neurons
 ● 1950 - Turing’s “Computing Machinery and Intelligence”
 ● 1956 - McCarthy, Minsky, Newell, Simon, Shannon, Nash et al. Dartmouth
 workshop: Birth of “Artificial Intelligence”
 ● 1952-69 - Early enthusiasm, great expectations, optimism fueled by early
 success on some problems thought to be hard
 ● 1966-73 - Collapse in AI research: Progress was slower than expected -
 Unrealistic predictions, Herbert Simon (1957)
 ○ AI discovers computational complexity.
 ● 1969-86 - Expert systems
 1980 - AI becomes an industry: expert systems boom, then
busts (1988-93):“AI Winter”
 1986 - Neural networks regain popularity
 1987 - Probabilistic reasoning and machine learning
 1995 - Emergence of intelligent agents
○ AI technologies continue to find applications in :
 ■ Information retrieval
 ■ Data mining and knowledge discovery
 ■ Customizable software systems
 ■ Bioinformatics
 Autonomous vehicles
 Agile manufacturing systems
 Smart devices (e.g., home, automobiles)
 Internet tools: search engines, recommender systems
 Steady progress on fundamental AI research problems
continues.
 2001 - Big data
 ○ Successful large-scale real-world applications in :
 ■ Image recognition
 ■ Natural Language Processing
 Speech Recognition
 Machine Translation
 2011 - Deep learning
 ○ Convolutional neural networks (CNNs)
 Periods in AI
 Early period - 1950’s & 60’s
 Game playing
 brute force (calculate your way out)
 Theorem proving
 symbol manipulation
 Biological models
 neural nets
 Symbolic application period - 70’s
 Early expert systems, use of knowledge
 Commercial period - 80’s
 boom in knowledge/ rule bases
 Periods in AI cont’d
 ? period - 90’s and New Millenium
 Real-world applications, modelling, better evidence, use of
theory, ......?
 Topics: data mining, formal models, GA’s, fuzzy logic,
agents, neural nets, autonomous systems
 Applications
 visual recognition of traffic
 medical diagnosis
 directory enquiries
 power plant control
 automatic cars
 Fashions in AI
Progress goes in stages, following funding booms and crises: Some examples:
1. Machine translation of languages
1950’s to 1966 - Syntactic translators
1966 - all US funding cancelled
1980 - commercial translators available

2. Neural Networks
1943 - first AI work by McCulloch & Pitts
1950’s & 60’s - Minsky’s book on “Perceptrons” stops nearly all work on nets
1986 - rediscovery of solutions leads to massive growth in neural nets research

The UK had its own funding freeze in 1973 when the Lighthill report reduced AI work severely -
Lesson: Don’t claim too much for your discipline!!!!
Look for similar stop/go effects in fields like genetic algorithms and evolutionary computing. This is a
very active modern area dating back to the work of Friedberg in 1958.
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 mainly symbolic AI and partially
with other one.
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
Games:
AI Applications
 Games:
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.