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2022 1/31
CS321: Principles of Artificial Intelligence
Introduction
Principles of Artificial
Intelligence
Course Code CS321
Faculty of Computing and Information Technology
Computer Science Department
Jan, 2022
These slides are based on lecture notes of the book’s author(Artificial
Intelligence: A Modern Approach,)
&
King Saud University course materials
&
Grokking Artificial Intelligence Algorithms
Lecturer: Wedad Al-Sorori

2022 2/31
Introduction
Introduction

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Introduction 3/31
Chapter Objectives
• At the end of this chapter, the student should be able to:
• Understand Artificial Intelligence (AI).
• Identify and describe AI foundations.
• Evaluate the various definitions of AI.
• Summarize the history of AI.
• Mention AI applications with examples.

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What is Intelligence
?
• What is intelligence?
• What is artificial intelligence?

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• Intelligence may be defined as:
1. The capacity to acquire and apply knowledge.
2. The faculty of thought and reason.
3. In general, things that are autonomous yet adaptive are considered to
be intelligent.
What is Intelligence?

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• Salvador Dali’ believed that ambition is an attribute of intelligence; he
said, “Intelligence without ambition is a bird without wings.”
• Albert Einstein believed that imagination is a big factor in intelligence;
he said, “The true sign of intelligence is not knowledge, but
imagination.”
• And Stephen Hawking said, “Intelligence is the ability to adapt,”
What is Intelligence?

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• “The art of creating machines that perform functions that require
intelligence when performed by people” (Kurzweil, 1990).
• “The branch of computer science that is concerned with the automation of
intelligent behavior.” (Luger and Stublefield, 1993)
• AI is concerned with real-world problems (difficult tasks), which require
complex and sophisticated reasoning processes and knowledge.
• Artificial intelligence concerned with not just understanding but also
building intelligent entities—machines that can compute how to act
effectively and safely in a wide variety of novel situations.
What is AI?

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• Grroking textbook defines AI as a synthetic system that exhibits
“intelligent” behavior.
• Douglas Hofstadter said, “AI is whatever hasn’t been done yet.”
• Russell textbook define AI as the study of agents that receive percepts from
the environment and perform actions. Each such agent implements a
function that maps percept sequences to actions, and we cover different
ways to represent these functions, such as reactive agents, real-time
planners, decision-theoretic systems, and deep learning systems.
What is AI?

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What is AI
• Some have defined intelligence in terms of fidelity to human
performance, while others prefer an abstract, formal definition
of intelligence called rationality—loosely speaking, doing the
“right thing.”
• Views of AI fall into four categories:
• Systems that think like humans
• Systems that act like humans
• Systems that think rationally
• Systems that act rationally

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In 1950 Turing proposed an operational definition of intelligence by using a Test composed of:
• An interrogator (a person who will ask questions)
• a computer (intelligent machine !!)
• A person who will answer to questions
• A curtain (separator)
• If the response of a computer to an unrestricted textual natural-
language conversation cannot be distinguished from that of a human
being then it can be said to be intelligent.
Acting humanly: The Turing Test

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• To give an answer, the computer would need to possess some capabilities:
• Natural language processing: To communicate successfully.
• Knowledge representation: To store what it knows or hears.
• Automated reasoning: to answer questions and draw conclusions using stored information.
• Machine learning: To adapt to new circumstances and to detect and extrapolate patterns.
• Computer vision: To perceive objects.
• Robotics to manipulate objects and move.
Acting humanly: The Turing Test

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• To say that a program thinks like a human, we must know how
humans think.
• Cognitive Science: Interdisciplinary field (AI, psychology, linguistics,
philosophy, anthropology) that tries to form computational theories
of human cognition.
• We can learn about human thought in three ways:
• introspection—trying to catch our own thoughts as they go by;
• psychological experiments—observing a person in action;
• brain imaging—observing the brain in action.
Thinking Humanly: Cognitive Modeling

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• Formalize “correct” reasoning using a mathematical model (e.g. of
deductive reasoning).
• We can learn about it through:
• Syllogisms
• Logicist
• Probability
• Logicist Program: Encode knowledge in formal logical statements and use
mathematical deduction to perform reasoning:
• Problems:
• Formalizing common sense knowledge is difficult.
• General deductive inference is computationally intractable.
Thinking Rationally: Laws of Thought

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• An agent is an entity that perceives its environment and is able to execute
actions to change it.
• Agents have inherent goals that they want to achieve (e.g. survive,
reproduce).
• A rational agent is one that acts so as to achieve the
• best outcome or, when there is uncertainty, the best expected outcome.
• True maximization of goals requires omniscience and unlimited
computational abilities.
• Limited rationality involves maximizing goals within the computational and
other resources available.
Acting Rationally: Rational Agents

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The Foundations of AI
• Philosophy: Philosophers (going back to 400 B.c.) made A1 conceivable by
considering the ideas that the mind is in some ways like a machine, that it operates
on knowledge encoded in some internal language, and that thought can be used to
choose what actions to take.
• Mathematics: Mathematician provided the tools to manipulate statements of
logical certainty as well as uncertain, probabilistic statements. They also set the
groundwork for understanding computation and reasoning about algorithms.
• Economics: Economists formalized the problem of making decisions that maximize
the expected outcome to the decision-maker.
• Neuroscience: physical substrate for mental activity
• Psychology: Psychologists adopted the idea that humans and animals can be
considered information processing machines. Linguists showed that language use
fits into this model.
• Computer engineering: Computer engineers provided the artifacts that make A1
applications possible. AI programs tend to be large, and they could not work
without the great advances in speed and memory that the computer industry has
provided.
• Control theory: It deals with designing devices that act optimally on the basis of
feedback from the environment. Initially, the mathematical tools of control theory
were quite different from AI, but the fields are coming closer together.

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Early AI: (The gestation of Artificial Intelligence)
1943 McCulloch & Pitts: Boolean circuit model of brain
1950 Turing's ``Computing Machinery and Intelligence''
1950s Early AI programs, including Samuel's checkers program,
Newell & Simon's Logic Theorist, Gelernter's Geometry Engine
The birth of Artificial Intelligence (1956)
1956 McCarthy organizes Dartmouth meeting and includes
Minsky, Shannon, Newell, Samuel, Simon
Name ``Artificial Intelligence'' adopted
AI History

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Early enthusiasm, great expectations (1952-1969):
1957 General Problem Solver [Newell, Simon, Shaw @ CMU]
1958 Creation of the MIT AI Lab by Minsky and McCarthy
1958 LISP, [McCarthy], second high level language (MIT AI Memo 1)
1963 Creation of the Stanford AI Lab by McCarthy
1965 Robinson's complete algorithm for logical reasoning
A dose of reality (1966-1973):
1966-74 AI discovers computational complexity …
1966-72 Shakey, SRI’s Mobile Robot [Fikes, Nilson]
AI History

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Knowledge-based systems (1969-1979)
1969 Publication of “Perceptrons” [Minsky & Papert],
Neural network research almost disappears
1969-79 Early development of knowledge-based systems
• First expert system DENDRAL for interpreting mass spectrogram data to determine molecular structure by
Buchanan, Feigenbaum, and Lederberg (1969).
1970 SHRDLU, Winograd’s natural language system
1971 MACSYMA, an symbolic algebraic manipulation system
1975 MYCIN: diagnosis of bacterial infection
AI History

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AI becomes an Industry (1980 – present)
1980-88 Expert systems industry booms
1981 Japan: Fifth generation project to build intelligent
computers based on Prolog logic programming.
US: Microelectronics and Computer Technology Corp.
UK: Alvey (Natural Language Tools)
AI History

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The return of neural networks (1986 - present)
• New algorithms discovered for training more complex neural networks (1986).
• Cognitive modeling of many psychological processes using neural networks, e.g.
learning language.
1988-93 Expert systems industry busts: ``AI Winter''
1985-95 Neural networks return to popularity
AI History

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AI becomes a science (1987 – present)
1988 Resurgence of probabilistic and decision-theoretic methods
• General focus on learning and training methods to address knowledge-acquisition
bottleneck.
• Shift of focus from rule-based and logical methods to probabilistic and statistical
methods (e.g. Bayes nets, Hidden Markov Models).
• Increased interest in particular tasks and applications
• Data mining
• Intelligent agents and Internet applications(softbots, believable agents, intelligent information
access)
• Scheduling/configuration applications (Successful companies: I2, Red Pepper, Trilogy)
AI History

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Big data (2001–present)
• These data sets include trillions of words of text, billions of images, and billions of
hours of speech and video, as well as vast amounts of genomic data, vehicle
tracking data, clickstream data, social network data, and so on.
• the development of learning algorithms specially designed to take advantage of
very large data sets.
• The availability of big data and the shift towards machine learning helped AI recover
commercial attractiveness (Havenstein, 2005; Halevy et al., 2009)
AI History

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Deep learning (2011–present)
• deep learning refers to machine learning using multiple layers of simple,
adjustable computing elements.
• A remarkable successes have led to a resurgence of interest in AI among
students, companies, investors, governments, the media, and the general
public.
• Deep learning relies heavily on powerful hardware.
AI History

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AI History

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• ROBOTIC VEHICLES:
• cars
• radio-controlled cars of the 1920s.
• 1980s without control
• driving on dirt roads in the 132-mile DARPA Grand Challenge in
2005
• driving on on streets with traffic in the 2007 Urban
Challenge
• In 2018, Waymo test vehicles passed the landmark of 10
million miles.
• commercial robotic taxi service.
• autonomous fixed-wing drones and Quadcopters.
• Legged locomotion BigDog, a quadruped robot by
Raibert et al. (2008).
• Atlas, a humanoid robot.
AI Applications

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• AUTONOMOUS PLANNING AND SCHEDULING
– NASA’s Remote Agent program
– The EUROPA planning toolkit and the SEXTANT system
• MACHINE TRANSLATION
• SPEECH RECOGNITION
– Alexa, Siri, Cortana, and Google offer assistants that can
answer questions and carry out tasks for the user
AI Applications

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• RECOMMENDATIONS:
– Companies such as Amazon, Facebook, Netflix, Spotify, YouTube,
Walmart, and others use ML to recommend what u like.
• IMAGE UNDERSTANDING
• MEDICINE
• Search Engines
AI Applications

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• Game Playing
●
Deep Blue defeated world chess
champion Garry Kasparov in 1997.
●
ALPHAGO surpassed all human Players on
Go.
●
ALPHAZERO, used no input from
humans (except for the rules of the
game), and was able to learn through
self-play alone to defeat all opponents,
human and machine, at Go, chess, and
shogi.
AI Applications

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• Expert Systems
 Geology
• prospector expert system carries evaluation of mineral potential of geological site or region.
 Diagnostic Systems
• Pathfinder, a medical diagnosis system (suggests tests and makes diagnosis) developed by Heckerman and other Microsoft
research.
• MYCIN system for diagnosing bacterial infections of the blood and suggesting treatments.
 Financial Decision Making
• Credit card providers, banks, mortgage companies use AI systems to detect fraud and expedite financial transactions.
 Configuring Hardware and Software
• AI systems configure custom computer, communications, and manufacturing systems, guaranteeing the purchaser
maximum efficiency and minimum setup time.
AI Applications

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Knowledge-based system
• Expert system (or knowledge-based system): a program which encapsulates knowledge from
some domain, normally obtained from a human expert in that domain
• components:
• Knowledge base (KB): repository of rules, facts (productions)
• working memory: (if forward chaining used)
• inference engine: the deduction system used to infer results from user input and KB
• user interface: interfaces with user
• external control + monitoring: access external databases, control,...
AI Applications

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• Why use expert systems:
• commercial viability: whereas there may be only a few experts whose time is expensive and rare, you can have many
expert systems
• expert systems can be used anywhere, anytime
• expert systems can explain their line of reasoning
• commercially beneficial: the first commercial product of AI
• Weaknesses:
• expert systems are as sound as their KB; errors in rules mean errors in diagnoses
• automatic error correction, learning is difficult (although machine learning research may change this)
• the extraction of knowledge from an expert, and encoding it into machine-inferrable form is the most difficult part of
expert system implementation.
AI Applications

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Risks and Benefits of AI
 Our entire civilization is the product of our human intelligence. If we have
access to substantially greater machine intelligence, the ceiling on our
ambitions is raised substantially.
 As Demis Hassabis, CEO of Google DeepMind, has suggested: “First solve AI,
then use AI to solve everything else.”
 As AI systems find application in the real world, it has become necessary to
consider a wide range of risks and ethical consequences.
 In the longer term, we face the difficult problem of controlling super intelligent
AI systems that may evolve in unpredictable ways.

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Risks and Benefits of AI
 Risks
 LETHAL AUTONOMOUS WEAPONS
 SURVEILLANCE AND PERSUASION
 BIASED DECISION MAKING
 IMPACT ON EMPLOYMENT
 SAFETY-CRITICAL APPLICATIONS

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Problem types and problem-solving paradigms
 AI aims to solve:
 Search problems: Find a path to a solution
 Optimization problems: Find a good solution
 Prediction and classification problems: Learn from patterns in data
 Clustering problems: Identify patterns in data
 Deterministic models: Same result each time it’s calculated
 Stochastic/probabilistic models: Potentially different result each time it’s
calculated

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AI Concepts/fields

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• To conclude:
• AI is a very fascinating field. It can help us solve difficult, real-world
problems, creating new opportunities in business, engineering, and many
other application areas.
• AI has matured considerably compared to its early decades, both
theoretically and methodologically. As the problems that AI deals with
became more complex, the field moved from Boolean logic to probabilistic
reasoning, and from hand-crafted knowledge to machine learning from
data. This has led to improvements in the capabilities of real systems and
greater integration with other disciplines.
Summary

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THANKS

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