In this presentation, I have presented an introduction to AI, foundation of AI, and History of AI.
The content is a summary of each topic of Chapter-1 of a very famous book on AI, "Artificial Intelligence, A Modern Approach by Stuart Russell and Peter Norvig ".
This document discusses artificial intelligence (AI) and its foundations. It defines AI as simulating human intelligence through machine processes like thinking and problem-solving. It explores key approaches to AI like acting humanly through tests like the Turing Test or thinking rationally through logic. The foundations of AI draw from fields like philosophy, mathematics, economics, neuroscience, psychology and computer engineering by applying concepts like logic, learning, decision-making, brain processes, cognition and building intelligent computer systems. The overall goal of AI is to create technology that allows computers and machines to work intelligently by breaking the problem into sub-problems.
Artificial intelligence - Approach and MethodRuchi Jain
Human natural intelligence is ubiquitous with human activities, such as solving problems, playing chess, guessing puzzles. AI is new mean to solve such complex problems. We NuAIg is a AI consulting firm, who will help you to create a AI road-map for your business and process automation.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
This document provides an overview of artificial intelligence (AI) including definitions, approaches, foundations, capabilities, and comparisons to human intelligence. It defines AI as the study of intelligent behavior in machines, discusses the four main approaches of acting humanly, thinking humanly, thinking rationally, and acting rationally. The foundations of AI are explained including contributions from fields like philosophy, mathematics, psychology, neuroscience, and more. Both strong AI which aims to truly replicate human reasoning and weak AI which focuses on narrow domains are described. Current capabilities of AI systems in areas such as games, robotics, diagnosis, and planning are summarized. Finally, differences between human and machine intelligence are outlined.
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
1. The document provides an introduction to the philosophy of artificial intelligence, discussing definitions of AI, the nature of human vs artificial intelligence, and different approaches to AI such as systems that think or act like humans and systems that think or act rationally.
2. Key aspects of AI discussed include machine learning, natural language processing, computer vision, robotics, and the differences between strong and weak AI.
3. The document also examines how AI aims to build intelligent machines that can perform tasks requiring human intelligence through techniques like problem solving, perception, reasoning, and learning.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
This document discusses different definitions and approaches to artificial intelligence (AI). It begins by defining AI as helping machines solve complex problems like humans by applying human-like algorithms. It then discusses AI's links to other fields and its history. The rest of the document explores definitions of AI and different goals or approaches in AI research, including systems that think or act like humans and systems that think or act rationally. It focuses on the Turing Test approach of acting humanly and the cognitive modeling approach of thinking humanly by modeling human cognition.
This document discusses artificial intelligence (AI) and its foundations. It defines AI as simulating human intelligence through machine processes like thinking and problem-solving. It explores key approaches to AI like acting humanly through tests like the Turing Test or thinking rationally through logic. The foundations of AI draw from fields like philosophy, mathematics, economics, neuroscience, psychology and computer engineering by applying concepts like logic, learning, decision-making, brain processes, cognition and building intelligent computer systems. The overall goal of AI is to create technology that allows computers and machines to work intelligently by breaking the problem into sub-problems.
Artificial intelligence - Approach and MethodRuchi Jain
Human natural intelligence is ubiquitous with human activities, such as solving problems, playing chess, guessing puzzles. AI is new mean to solve such complex problems. We NuAIg is a AI consulting firm, who will help you to create a AI road-map for your business and process automation.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
This document provides an overview of artificial intelligence (AI) including definitions, approaches, foundations, capabilities, and comparisons to human intelligence. It defines AI as the study of intelligent behavior in machines, discusses the four main approaches of acting humanly, thinking humanly, thinking rationally, and acting rationally. The foundations of AI are explained including contributions from fields like philosophy, mathematics, psychology, neuroscience, and more. Both strong AI which aims to truly replicate human reasoning and weak AI which focuses on narrow domains are described. Current capabilities of AI systems in areas such as games, robotics, diagnosis, and planning are summarized. Finally, differences between human and machine intelligence are outlined.
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
1. The document provides an introduction to the philosophy of artificial intelligence, discussing definitions of AI, the nature of human vs artificial intelligence, and different approaches to AI such as systems that think or act like humans and systems that think or act rationally.
2. Key aspects of AI discussed include machine learning, natural language processing, computer vision, robotics, and the differences between strong and weak AI.
3. The document also examines how AI aims to build intelligent machines that can perform tasks requiring human intelligence through techniques like problem solving, perception, reasoning, and learning.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
This document discusses different definitions and approaches to artificial intelligence (AI). It begins by defining AI as helping machines solve complex problems like humans by applying human-like algorithms. It then discusses AI's links to other fields and its history. The rest of the document explores definitions of AI and different goals or approaches in AI research, including systems that think or act like humans and systems that think or act rationally. It focuses on the Turing Test approach of acting humanly and the cognitive modeling approach of thinking humanly by modeling human cognition.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
This document provides an overview of an introductory course on Artificial Intelligence. It discusses the learning outcomes, which include gaining knowledge of core AI concepts like search, game playing, knowledge representation, planning and machine learning. It emphasizes using Python to construct simple AI systems and developing transferable problem solving skills. The document outlines that students are expected to attend lectures, supplement with textbook reading, and use references. It also gives a high-level overview of the different perspectives and definitions of what constitutes Artificial Intelligence.
This document provides an introduction to artificial intelligence and cognitive science. It defines intelligence and AI, discusses different approaches to AI like thinking like humans, thinking rationally, acting like humans and acting rationally. It also summarizes the history of AI from early neural networks to modern applications. Key concepts covered include the Turing test, knowledge representation, rational agents, intelligent environments and knowledge-based systems.
1. Artificial Intelligence aims to understand and build intelligent systems by studying human intelligence and behavior.
2. There are different approaches to defining AI such as thinking rationally, acting rationally, thinking humanly, and acting humanly.
3. The foundations of AI draw from various fields including philosophy, mathematics, economics, neuroscience, psychology, and computer engineering.
This document provides an overview of artificial intelligence (AI). It defines AI as the science of developing methods to solve problems usually associated with human intelligence. The document discusses different definitions and visions of AI, including thinking and acting like humans, thinking and acting rationally, and modeling human thinking through computational models. It also covers the history of AI from its origins in the 1940s to recent successes, as well as related fields and main areas of AI research like machine learning, robotics, and natural language processing.
The document provides an introduction to artificial intelligence (AI), including its key concepts, scope, components, types, and applications. It defines AI as the science and engineering of creating intelligent machines, especially computer programs. The main types of AI discussed are narrow/weak AI, which can perform specific tasks, and general AI, which aims to create human-level intelligence. The document also outlines the core components of AI in areas like logic, cognition, and computation, and how these combine to form knowledge-based systems. Common applications of AI mentioned include gaming, natural language processing, and robotics.
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.
The document discusses different definitions and approaches to artificial intelligence (AI). It describes AI as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally based on logic, and systems that act rationally by being goal-oriented agents. The foundations of AI include philosophy, mathematics, psychology, computer engineering, and linguistics. Key topics in AI are search, knowledge representation and reasoning, planning, learning, and interacting with the environment through perception and action. The history and development of AI over time is also reviewed.
This document provides an overview of an introduction to artificial intelligence course, including:
- Course details such as the textbook, grading breakdown, and schedule
- Definitions and types of artificial intelligence including rational agents, the Turing test, and different branches of AI
- A brief history of ideas influencing AI such as philosophy, mathematics, psychology, and agents
- Examples of AI applications and challenges including ethics
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
This document provides an introduction to artificial intelligence. It defines intelligence and discusses approaches to AI such as systems that act like humans through techniques like the Turing test. It also discusses systems that think rationally using logic and reasoning. The document outlines key concepts in AI like the Chinese room argument and differences between weak and strong AI. It examines using computational models to understand human thinking and formalizing rational thinking through logic.
Module-1.1.pdf of aiml engineering mod 1fariyaPatel
This document provides an overview of the history and foundations of artificial intelligence (AI). It discusses early definitions and approaches to AI, including the Turing Test. The document also outlines some of the key developments in the early years of AI research, including the work of McCulloch and Pitts on artificial neurons in 1943, the first neural network computer built by Minsky and Edmonds in 1950, and the pivotal 1956 Dartmouth workshop organized by McCarthy that is considered the official birth of the field of AI.
The document discusses artificial intelligence and how it works. It defines artificial intelligence as making computers do intelligent tasks like humans. It discusses neural networks which are composed of artificial neurons that mimic biological neurons. The document also discusses machine learning approaches like failure driven learning, learning by being told, and learning by exploration. Examples of applications of AI are given, like expert systems used in geology and medicine. The key differences between human and artificial intelligence are noted.
Artificial intelligence (AI) is defined as making computers intelligent like humans. It works using artificial neurons that mimic biological neurons and scientific theorems. Neural networks are composed of interconnected artificial neurons that accept inputs, process them, and output results. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to solve problems. While AI can process large amounts of data quickly, humans have abilities like intuition and creativity that AI currently lacks. The relationship between AI, psychology, and society is an important area of research.
This document provides an overview of artificial intelligence (AI). It defines intelligence and AI, explaining that AI aims to make computers intelligent like humans. It describes how AI works using artificial neurons and logic. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The document also discusses applications of expert systems and machine learning. It compares human and artificial intelligence, noting strengths of each. In the end, it argues that AI is humanity's attempt to build models of ourselves and should not be feared.
The document discusses the history and development of artificial intelligence. It defines intelligence as the ability to plan, solve problems, and reason. Early AI research focused on creating programs that could play games like chess at a human level. Major milestones included the development of LISP programming language in the 1950s and the first AI summer conference at Dartmouth College in 1956. While early work aimed to build human-like intelligence, later approaches focused on narrow applications and bottom-up modeling of neural networks and learning.
This document provides an overview of an artificial intelligence course, including assessment methods, topics that will be covered, and the foundations of AI. The key topics to be discussed include what intelligence is, what AI is capable of today such as game playing and machine translation, approaches to AI like the Turing Test and rational agent theory, the foundations of AI from various fields, the historic concepts that influenced AI, and a brief introduction to the instructor. Assessment will include assignments, presentations, programming, and a final exam.
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The Turing test tests a machine's ability to demonstrate intelligence through conversation. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to problem solving. While AI can process large data quickly, it lacks common sense, intuition, and critical thinking that humans have. Overall, AI is an attempt to build models of human intelligence.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
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This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
This document provides an overview of an introductory course on Artificial Intelligence. It discusses the learning outcomes, which include gaining knowledge of core AI concepts like search, game playing, knowledge representation, planning and machine learning. It emphasizes using Python to construct simple AI systems and developing transferable problem solving skills. The document outlines that students are expected to attend lectures, supplement with textbook reading, and use references. It also gives a high-level overview of the different perspectives and definitions of what constitutes Artificial Intelligence.
This document provides an introduction to artificial intelligence and cognitive science. It defines intelligence and AI, discusses different approaches to AI like thinking like humans, thinking rationally, acting like humans and acting rationally. It also summarizes the history of AI from early neural networks to modern applications. Key concepts covered include the Turing test, knowledge representation, rational agents, intelligent environments and knowledge-based systems.
1. Artificial Intelligence aims to understand and build intelligent systems by studying human intelligence and behavior.
2. There are different approaches to defining AI such as thinking rationally, acting rationally, thinking humanly, and acting humanly.
3. The foundations of AI draw from various fields including philosophy, mathematics, economics, neuroscience, psychology, and computer engineering.
This document provides an overview of artificial intelligence (AI). It defines AI as the science of developing methods to solve problems usually associated with human intelligence. The document discusses different definitions and visions of AI, including thinking and acting like humans, thinking and acting rationally, and modeling human thinking through computational models. It also covers the history of AI from its origins in the 1940s to recent successes, as well as related fields and main areas of AI research like machine learning, robotics, and natural language processing.
The document provides an introduction to artificial intelligence (AI), including its key concepts, scope, components, types, and applications. It defines AI as the science and engineering of creating intelligent machines, especially computer programs. The main types of AI discussed are narrow/weak AI, which can perform specific tasks, and general AI, which aims to create human-level intelligence. The document also outlines the core components of AI in areas like logic, cognition, and computation, and how these combine to form knowledge-based systems. Common applications of AI mentioned include gaming, natural language processing, and robotics.
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.
The document discusses different definitions and approaches to artificial intelligence (AI). It describes AI as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally based on logic, and systems that act rationally by being goal-oriented agents. The foundations of AI include philosophy, mathematics, psychology, computer engineering, and linguistics. Key topics in AI are search, knowledge representation and reasoning, planning, learning, and interacting with the environment through perception and action. The history and development of AI over time is also reviewed.
This document provides an overview of an introduction to artificial intelligence course, including:
- Course details such as the textbook, grading breakdown, and schedule
- Definitions and types of artificial intelligence including rational agents, the Turing test, and different branches of AI
- A brief history of ideas influencing AI such as philosophy, mathematics, psychology, and agents
- Examples of AI applications and challenges including ethics
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
This document provides an introduction to artificial intelligence. It defines intelligence and discusses approaches to AI such as systems that act like humans through techniques like the Turing test. It also discusses systems that think rationally using logic and reasoning. The document outlines key concepts in AI like the Chinese room argument and differences between weak and strong AI. It examines using computational models to understand human thinking and formalizing rational thinking through logic.
Module-1.1.pdf of aiml engineering mod 1fariyaPatel
This document provides an overview of the history and foundations of artificial intelligence (AI). It discusses early definitions and approaches to AI, including the Turing Test. The document also outlines some of the key developments in the early years of AI research, including the work of McCulloch and Pitts on artificial neurons in 1943, the first neural network computer built by Minsky and Edmonds in 1950, and the pivotal 1956 Dartmouth workshop organized by McCarthy that is considered the official birth of the field of AI.
The document discusses artificial intelligence and how it works. It defines artificial intelligence as making computers do intelligent tasks like humans. It discusses neural networks which are composed of artificial neurons that mimic biological neurons. The document also discusses machine learning approaches like failure driven learning, learning by being told, and learning by exploration. Examples of applications of AI are given, like expert systems used in geology and medicine. The key differences between human and artificial intelligence are noted.
Artificial intelligence (AI) is defined as making computers intelligent like humans. It works using artificial neurons that mimic biological neurons and scientific theorems. Neural networks are composed of interconnected artificial neurons that accept inputs, process them, and output results. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to solve problems. While AI can process large amounts of data quickly, humans have abilities like intuition and creativity that AI currently lacks. The relationship between AI, psychology, and society is an important area of research.
This document provides an overview of artificial intelligence (AI). It defines intelligence and AI, explaining that AI aims to make computers intelligent like humans. It describes how AI works using artificial neurons and logic. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The document also discusses applications of expert systems and machine learning. It compares human and artificial intelligence, noting strengths of each. In the end, it argues that AI is humanity's attempt to build models of ourselves and should not be feared.
The document discusses the history and development of artificial intelligence. It defines intelligence as the ability to plan, solve problems, and reason. Early AI research focused on creating programs that could play games like chess at a human level. Major milestones included the development of LISP programming language in the 1950s and the first AI summer conference at Dartmouth College in 1956. While early work aimed to build human-like intelligence, later approaches focused on narrow applications and bottom-up modeling of neural networks and learning.
This document provides an overview of an artificial intelligence course, including assessment methods, topics that will be covered, and the foundations of AI. The key topics to be discussed include what intelligence is, what AI is capable of today such as game playing and machine translation, approaches to AI like the Turing Test and rational agent theory, the foundations of AI from various fields, the historic concepts that influenced AI, and a brief introduction to the instructor. Assessment will include assignments, presentations, programming, and a final exam.
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The Turing test tests a machine's ability to demonstrate intelligence through conversation. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to problem solving. While AI can process large data quickly, it lacks common sense, intuition, and critical thinking that humans have. Overall, AI is an attempt to build models of human intelligence.
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Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
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#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
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- Validate access.
- Exploiting IAM PassRole Misconfiguration
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- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
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- Create role with administrative privileges.
- Allow user to assume the role.
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Agile Methodology: Before Agile – Waterfall, Agile Development.
2. Before We Begin
Studying AI is Valuable but before delving into the Study of AI,
defining is important.
For years, we human thought how we humans think and why
only a few living organisms can perceive, understand,
manipulate, and predict.But AI goes beyond it, it doesn’t just
understand but also builds intelligent entities.
“Artificial Intelligence is a technology that can perform tasks
which require human cognition”
3. 1.1 What is AI ?
Scientists have approached artificial intelligence in
various ways, looking at it from different angles.
Some focus on making machines smart like humans,
while others aim to create systems that can perform
tasks intelligently. These approaches are as follows:
Approaches to AI
Thought Process and
Reasoning
Human Behavior
Acting Rationally
(The Rational
Agent Approach)
Acting Humanly
(Turing Test)
Thinking
Rationally
(The Laws of
Thought)
Thinking
Humanly
(Cognitive
Modeling)
4. 1.1.1 Thinking Humanly: Cognitive Modeling
● This approach first requires to understand how human brain works on
problems.
● We can understand how human brain works through introspection,
psychological experiments, and brain imaging
● If we grasp how the human brain works, we can write a computer program
on the same principles.
● Now, we need to ensure that the program input-output are correspond
those of human brain.
Experimental
tools of
Psychology
Precise
Theory of
Human Brain
AI Modeling
5. 1.1.2 Thinking Rationally : The laws of thought approach
● Aristotle was the first person to codify “Right Thinking Process” or “Irrefutable
Reasoning Process”.
● He gave idea of Syllogism
● This study of laws of thought led to the birth to the term “Logic”.
● Logist tradition of AI hopes to build program to create intelligent system
● Challenges encountered in this approach are:
(i) Notation of the informal knowledge in formal term using logical notation.
(ii) Big difference between solving problems in principle and solving problem in
practical
6. 1.1.3 Acting Humanly : Turing Test
● The Turing Test, devised by British mathematician Alan Turing, suggests that
if a person cannot reliably tell whether they are communicating with a
computer or a human through terminals, then the machine demonstrates
human-like behavior.
● Following are the process under turing tests that a machine need to pass:
- Natural Language Processing: Understanding of Language
- Knowledge Representation: Storing information
- Automatic Reasoning: using stored data to draw conclusion
- Machine Learning: Adopting new situation and drawing patterns
- Computer Vision: To perceive object
- Robotics: To manipulate object physically
7. 1.1.4 Acting Rationally: The Rational Agent Approach
● The word agent is a latin word which means “To do” and computer agents are expected
to do more: Operate Automatically, Perceive Environment, Persist over a prolonged
period, adopt changes, create and pursue goals.
● The word “Rational” means “Act to achieve best or best expected outcome”.
● Combining above two points, the rational agent focuses on course of action.
● All the skills required in turing test also allow an agent to act rationally.
● Knowledge representation and reasoning will enable good decision making and then
we require to generate natural general sentences to cope with complex society.
● Rational Agent approach has two advantages over other approaches to AI: The
solution will be more general and this approach is acceptable to scientific
development. This is because the standard of acting rationally is well defined
mathematically and in more general way.
8. 1.2
The Foundation
Of Artificial
Intelligence
This section provide a brief history of
the disciplines that contributed ideas,
viewpoints, and techniques to AI :
1. Philosophy
2. Mathematics
3. Economics
4. Neuroscience
5. Psychology
6. Computer engineering
7. Control theory and cybernetics
8. Linguistics
9. 1.2.1 Philosophy
● In ancient times (around 4th century B.C.), Aristotle created basic rules for
thinking and made a system for logical reasoning called syllogisms.
● Aristotle's system allowed people to draw conclusions in a mechanical way from
starting ideas.
● Ramon Lull later thought that machines could do logical thinking in a mechanical
way.
● In the 17th century, Thomas Hobbes compared thinking to doing math, suggesting
a link between mental processes and calculations.
● Around the year 1500, Leonardo da Vinci designed a working mechanical
calculator, showing early progress in automation.
● Wilhelm Schickard (1623) and Blaise Pascal (1642) made machines that could do
calculations, with Pascal saying they act a lot like thought.
● Gottfried Wilhelm Leibniz (1646–1716) made a machine that could do operations
on ideas, going beyond basic math.
10. ● Descartes (1596–1650) proposed rationalism, dualism, and materialism.
● The empiricism movement, led by Bacon and Locke, said knowledge comes from experiences
with our senses.
● Logical positivism, created by the Vienna Circle, mixed ideas from thinking and experiences,
connecting knowledge to what we observe.
Rationalism
Dualism Materialism
The power of
reasoning in
understanding
world
There is a part
of human
brain which is
out of nature
Brain operation,
according to
the physics law,
constitute brain
● Hence, the philosophical picture of the mind is constituted by connection between knowledge and actions
11. 1.2.2 Mathematics
Philosopher gave fundamental idea of AI but formal science required mathematical
formalization. There are three areas of focus under this discipline:
Logic
George Boole (1815–1864)
initiated the development of
propositional, or Boolean, logic,
which serves as the basis for
logical reasoning. In 1879,
Gottlob Frege (1848–1925)
expanded Boole's work,
introducing first-order logic that
is widely used today. Alfred
Tarski (1902–1983) contributed
by presenting a theory of
reference, facilitating the
connection between logical
entities and real-world objects.
Computation
In AI, computation is fundamental,
relying on logical operations and
algorithms. By manipulating
symbols and data, computers mimic
intelligent behavior, facilitating
problem-solving, learning, and
decision-making across different
fields. The essence of AI
computation lies in mathematical
models, algorithms, and data
processing, replicating cognitive
functions and advancing the
creation of intelligent systems and
machines.
Probability
Probability, the third big math idea
in AI, started with Gerolamo
Cardano and was developed by
Blaise Pascal and others. It began
with gambling but became crucial
for dealing with uncertainty in
sciences. People like James
Bernoulli, Pierre Laplace, and
Thomas Bayes improved the
theory and introduced ways to use
statistics. Thomas Bayes
suggested a rule for updating
probabilities with new evidence,
which is a key part of how AI
systems handle uncertainty today.
12. 1.2.3 Economics
● Adam Smith gave birth to economics by launching his book An Inquiry into the nature
and the cause of wealth of nations . Smith was the first person to treat economic as
science. Economists always thought that economy is always about money, but it was
about how people make choices that lead to "preferred outcome". The mathematical
treatment or Utility of "Preferred outcome" was formalised by Leon Walras, and was
further improved by Frank Ramsey, John von Neumann and Oskar Morgenstern in the
book, "The Theory of Games and Economic Behavior".
13. ● Decision Theory combines probability and Utility Theory,
applicable to large economies where individual decisions have
no bearing on others.
● In small organizations, individual decisions significantly
impact others, leading to the development of Game Theory by
Von Neumann and Morgenstern.
● Unlike Decision Theory, Game Theory doesn't prescribe clear
actions.
Probability Utility Theory Decision Theory
14. ● Economists haven't addressed the question of making rational decisions when payoffs
result from a sequence of actions. This was pursued in the field of operations research,
which emerged during WWII.
● Richard Bellman introduced a sequential process, Markov Decision Process, to
formalize a class of sequential problems in the field of operations research.
Pursued in
15. 1.2.4 Neuroscience
● Neuroscience is the study of brain. Though, exact working of human brain
remained mysterious but later it was called a seat of consciousness
● Paul Broca’s study of speech deficit in a damaged-brain patient showed
the existence of localized areas in brain responsible for different function.
The left hemisphere of the brain is responsible for speech production
● The brain is made up of small nerves called neurons. Camillo Golgi
developed a technique to study individual neurons, which Santiago used to
understand the brain's neuron structure. Nicolas was the first to apply a
mathematical model to comprehend how the brain's neurons work.
● Brain cannot use all of its neurons simultaneously but a computer can. But
brain has advantage of storing unlimited information
● A simple collection of cells lead to thought, action, and consciousness or
we can say brain causes mind.
16. ● The most information
processing goes in
cerebral cortex, the outer
layer of the brain.
● When several neurons
sends signal to each other
and communicate at a
junction, this happens
because of
electrochemical reaction.
The signals control brain
activity and this
mechanism is thought to
be the process of learning
in the brain.
17. 1.2.5 Psychology
● Scientific psychology traces its origins to Hermann von Helmholtz
(1821–1894) and his student Wilhelm Wundt (1832–1920).
● Helmholtz applied the scientific method to human vision, creating a
fundamental treatise on vision.
● In 1879, Wundt established the first laboratory of experimental
psychology at the University of Leipzig, emphasizing controlled
experiments and introspection.
● Behaviorism, led by John Watson (1878–1958), rejected mental
processes, focusing on objective measures of stimulus and response.
● Cognitive psychology, viewing the brain as an information-processing
device, can be traced back to William James (1842–1910).
18. ● Helmholtz believed perception involved unconscious logical inference, a
viewpoint later revived in cognitive psychology.
● Frederic Bartlett's Applied Psychology Unit at Cambridge fostered cognitive
modeling, challenging behaviorism.
● Kenneth Craik (1943) outlined three key steps for a knowledge-based agent:
translating stimulus, cognitive manipulation, and retranslation into action.
● Donald Broadbent continued Craik's work, modeling psychological
phenomena as information processing.
● Cognitive science emerged in the U.S., influenced by computer modeling and
key presentations in a 1956 MIT workshop by Miller, Chomsky, and Newell-
Simon.
______________
19. 1.2.6 Computer Engineering
● Essentials for AI Success: AI success relies on combining intelligence with a computing
artifact, with the computer being the primary tool.
● World War II Contributions: The first operational computers emerged during World War
II, including Heath Robinson and Colossus by Alan Turing's team, Z-3 by Konrad Zuse,
and ENIAC by John Mauchly and John Eckert.
20. ● Evolution of Computer Performance: Computer performance has evolved,
emphasizing parallelism since 2005, after which the focus shifted from
increasing clock speed to multiplying CPU cores.
● Calculating Devices Before Computers: Automated machines from the
17th century preceded electronic computers, with Joseph Marie
Jacquard's programmable loom in 1805 and Charles Babbage's
ambitious Analytical Engine in the mid-19th century.
21. ● Babbage's Unfinished Machines: In 1991, Charles
Babbage designed the Difference Engine for
mathematical computations. He also designed the
Analytical Engine, the first artifact capable of
universal computation.
● Ada Lovelace's Contribution: Ada Lovelace,
Babbage's colleague, is considered the world's first
programmer. She had written programs for the
unfinished Analytical Engine.
● Debt to Computer Science: AI owes a debt to
computer science for operating systems,
programming languages, and tools. However, AI
has also contributed significantly to mainstream
computer science with ideas like time sharing,
interactive interpreters, and more.
22. ● AI Pioneering Ideas: AI has pioneered concepts adopted in mainstream computer
science, including personal computers with windows and mice, rapid development
environments, linked list data type, automatic storage management, and key
concepts of symbolic, functional, declarative, and object-oriented programming.
● Software Side Contribution: The software side of computer science has played a
vital role in providing tools and languages for writing modern programs and papers
about them.
● Reciprocal Impact: The relationship between AI and computer science is reciprocal,
with both fields influencing and benefiting from each other's advancements.
23. 1.2.7 Control theory and Cybernetics
● Control theory and cybernetics contributed to AI by providing frameworks for
understanding and regulating the behavior of systems.
● Cybernetics is the interdisciplinary study of the structure, function, and dynamics of
systems, particularly those that involve communication and control. It explores the
principles of feedback, information, and regulation in various types of systems,
including biological, mechanical, and social systems.Cybernetic principles, such as
goal-oriented feedback, played a crucial role in the development of learning
algorithms in AI.
24. ● Control theory offered insights into system stability, helping AI engineers
design robust and reliable autonomous systems.
● The integration of cybernetic ideas into AI allowed for the creation of self-
regulating systems capable of adapting to changing environments.
● The study of control mechanisms in biological systems inspired the design
of adaptive algorithms in AI, mirroring natural learning processes.
● Cybernetics influenced the development of intelligent agents, enabling them
to perceive, reason, and act in a manner analogous to how living organisms
interact with their environment.
● Control theory and cybernetics continue to shape AI research, providing
theoretical foundations and practical tools for designing efficient and
responsive artificial systems.
__________________
25. 1.2.8 Linguistics
● B.F. Skinner's "Verbal Behavior" (1957) presented behaviorism in language learning.
● Noam Chomsky's critique questioned behaviorism's inability to explain language
creativity.
● Chomsky's own theory, based on syntactic models, offered a programming
potential.
● Modern linguistics and AI emerged simultaneously, forming computational
linguistics.
● Language understanding complexity extends beyond sentence structure to context
and subject matter.
● Early knowledge representation work in AI was closely linked to language and
linguistics.
● The intersection of philosophy and language influenced linguistic research and AI
development.
_________________
27. 1943-55
The gestation
of Artificial
Intelligence
1956
The birth of
artificial
intelligence
1952-69
Early
enthusiasm,
great
expectations
1966-73
A dose of
reality
1969-79
Knowledge
Based
System
1980-
Present
1986–
present
1987–
present
1995–
present
2001–
present
AI
Becomes
an Industry
The return of
neural
network
AI Adopts
Scientific
Method
The
emergence of
intelligent
agent
The
availability
of large data
sets
28. 1.3.1 The Gestation of AI
In 1943, Warren McCulloch and Walter
Pitts laid the foundation for artificial
intelligence (AI) by creating a model of
artificial neurons inspired by brain
physiology, propositional logic, and
Turing's theory of computation. They
demonstrated that networks of these
neurons could compute any function
and implement logical operations.
Donald Hebb (1949) introduced
Hebbian learning to modify connection
strengths between neurons, a concept
still influential today.
29. Alan Turing's 1950 article introduced key AI concepts, including the Turing Test,
machine learning, genetic algorithms, and reinforcement learning. Turing also
proposed the Child Programme idea, simulating a child's mind instead of an
adult's.
In 1950, Harvard
students Marvin
Minsky and Dean
Edmonds built the first
neural network
computer, SNARC.
Minsky later explored
universal computation
in neural networks at
Princeton.
30. 1.3.2 The Birth of AI
For the next 20 years, AI was shaped by these people and their connections at
MIT, CMU, Stanford, and IBM. The Dartmouth proposal highlighted that AI
focuses on imitating human abilities, using computer science as its method. AI
became its own field because it had unique goals and methods, unlike control
theory, operations research, or decision theory.
In 1951, John McCarthy, an important person in
AI, finished his PhD at Princeton. Later, in 1956,
he organized a workshop at Dartmouth, which is
considered the starting point of AI. The goal was
to figure out how to make machines simulate
human intelligence. Attendees included famous
researchers like Allen Newell and Herbert Simon.
The workshop didn't bring big breakthroughs, but
it united key people.
31. 1.3.3 Early Enthusiasm, great expectations
● In the early days of AI, with basic
computers, pioneers like John
McCarthy and others amazed people
by making computers do clever things.
● Allen Newell and Herbert Simon made
the General Problem Solver, a program
that solved problems like humans. It
sparked the idea that intelligence
involves manipulating symbols.
Outline of General Problem Solver
32. ● At IBM, Herbert Gelernter and Arthur Samuel created AI programs.
● in 1958, McCarthy made Lisp, a key programming language for AI.
● McCarthy later started the AI lab at Stanford to emphasize logic.
● They explored "microworlds" like the blocks world to solve limited but
smart tasks.
● Early work on neural networks, inspired by McCulloch and Pitts, also
advanced.
● All these achievements set the stage for the future of AI.
_________
33. 1.3.4 A dose of reality
● In 1957, Herbert Simon said machines
would think and learn fast. But early AI
had problems.
● Translating languages failed because
computers lacked knowledge.
● Thinking faster with better hardware
didn't work for complex AI challenges.
● In 1973, the Lighthill report criticized AI,
reducing support.
● In 1969, Minsky showed that basic
structures for smart behavior had limit.
● New learning methods came later, but
early AI struggled with big expectations
and real-world difficulties.
"AI Winter"
symbolizes a period
marked by reduced
enthusiasm and
backing for
advancements in
artificial intelligence.
34. 1.3.5 Knowledge Based System
● In early day of AI, AI researchers used weak methods or general searches for
solutions.
● DENDRAL ,an expert system, broke ground using specific knowledge for
molecular structure. It replaced exhaustive searches with chemists' pattern
recognition, making it more efficient.
● DENDRAL was knowledge-intensive and used specialized rules.
● MYCIN, another expert system, was a backward chaining expert system that
used AI to identify microorganisms causing severe diseases like bacteremia
and meningitis and propose antibiotics based on patient weight.
● Since then, domain knowledge became crucial in natural language
understanding. While early systems like SHRDLU had limitations, Roger
Schank's work at Yale emphasized knowledge representation and reasoning
for language understanding.
● Real-world applications led to different languages, from logic-based Prolog to
Minsky's frame-based approach.
35. 1.3.6 AI Becomes An Industry
In the early 1980s, the first successful
commercial expert system, R1, operated
at Digital Equipment Corporation, saving
millions of dollars. By 1988, major
corporations like DEC and DuPont had
deployed numerous expert systems,
resulting in significant cost savings. The
AI industry grew rapidly, reaching billions
of dollars with companies developing
expert systems, vision systems, robots,
and specialized software and hardware.
However, the period known as the "AI
Winter" followed, marked by companies
failing to fulfill grand promises, leading to
a downturn in the AI industry.
36. 1.3.7 The Return of Neural Network
In the 1980s, researchers rediscovered a learning algorithm called back-
propagation, first found in 1969. They applied it to solve learning problems
in computer science and psychology. Some thought that connectionist
models, which emphasize neural networks, could challenge symbolic and
logic-based approaches in AI. There was a debate about whether
manipulating symbols played a crucial role in human thinking. Nowadays,
we see both connectionist and symbolic approaches as working together,
not competing. Current neural network research has two branches: one
focuses on designing effective systems, and the other studies the
properties of real neurons.
37. 1.3.8 AI Adopts Scientific Method
● In recent years, there has been a significant shift in artificial intelligence (AI)
towards building on existing theories, rigorous experimentation, and real-world
applications.
● AI, once isolated, is now integrating with fields like control theory and
statistics.
● The scientific method is firmly applied and, now, AI requires hypotheses to
undergo empirical experiments and statistical analysis.
● Recent dominance by hidden Markov models (HMMs) is due to their rigorous
theory and training on real speech data.
● Similar trends are seen in machine translation and neural networks, which now
benefit from improved methodology and theoretical frameworks.
38. ● Judea Pearl's work in probabilistic reasoning led to a new acceptance of
probability and decision theory, with Bayesian networks dominating
uncertain reasoning in AI.
● Normative expert systems, acting rationally based on decision theory,
have become prominent.
● Similar revolutions have occurred in robotics, computer vision, and
knowledge representation, as increased formalization and integration
with machine learning prove effective in solving complex problems.
______________
39. 1.3.9 The emergence of Intelligent Agent
Researchers are looking again at the "whole agent" challenge in AI, like the SOAR
architecture. The Internet is a big deal for smart agents, used in things like search
engines.
Creating complete agents shows the need to shake up AI fields and handle
uncertainties in sensory systems. AI now works closely with areas like control theory
and economics, especially in things like controlling robotic cars.
40. ● Despite successes, some AI leaders like McCarthy, Minsky,
Nilsson, and Winston weren't happy.
● They wanted AI to go back to its original goal of making human-
like AI (HLAI), focusing on machines that think, learn, and create.
● Another idea was Artificial General Intelligence (AGI), aiming for a
universal way of learning and acting in any situation rightly and
making sure AI is friendly and not a worry in this journey.
__________________
41. 1.3.10 The availability of large data sets.
In the past 60 years of computer science, people mostly focused on creating
algorithms. But now, in AI, we're realizing that for many problems, it's more useful to
focus on the data instead
of getting too caught up
in which algorithm to use.
This change is because
we have a lot of data
available, like trillions of
English words or billions
of web images.
42. An important study by Yarowsky showed that, for tasks like figuring out
the meaning of a word in a sentence, you can do it really well without
human-labeled examples. Another study by Banko and Brill found that
having more data is often more helpful than choosing a specific
algorithm.
For instance, Hays and Efros improved a photo-filling tool by using a
bigger collection of photos. This shift in thinking suggests that in AI,
where we need a lot of knowledge, we might rely more on learning from
data instead of manually coding everything.
With the rise of new AI applications, some say we're moving from "AI
Winter" to a new era, “AI Summer”, as AI becomes a fundamental part of
many industries, as noted by Kurzweil.
43. 1.4 The State of The Art
AI today does various tasks:
1. Robotic Vehicles: Driverless cars like
STANLEY navigate terrains using
cameras and sensors.
2. Speech Recognition: Systems guide
conversations, like booking flights with
an automated phone system.
3. Autonomous Planning: NASA's
Remote Agent autonomously plans
spacecraft operations.
4. Game Playing: IBM's DEEP BLUE
beat the world chess champion, Garry
Kasparov.
44. 5. Spam Fighting: Learning algorithms classify
over a billion messages daily to identify and filter
spam.
6. Logistics Planning: During the Persian Gulf
crisis, AI tools like DART automated complex
logistics planning for the U.S. forces.
7. Robotics: iRobot's Roomba vacuum and
PackBot handle various tasks, from cleaning
homes to hazardous materials disposal.
8. Machine Translation: Programs translate
languages, like Arabic to English, using
statistical models trained on vast text examples.
These are real applications of AI, showing its