Lecture 1-Introduction to AI and its application.pdf

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Artificial Intelligence
Lecture # 1
Fall 2024
Introduction to AI and its application
By. Dr. Shahzad Ashraf
Associate Professor
Introduction to Artificial Intelligence
 A branch of computer science that focuses on creating systems capable of
performing tasks that typically require human intelligence.
 Study of how to make programs/computers do things that people do better.
 The exciting new effort to make computers think … machines with minds.
 AI systems are designed to simulate human cognitive functions and can range
from simple algorithms to complex machine learning models.
 It signifies computers’ general ability to mimic human thought while carrying out
tasks in real-world environments.
Brain vs. 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

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AI History
 Machines are developed rapidly to understand and work with human intelligence.
you may have seen that YouTube is showing videos according to your recent
searches, Amazon is displaying brands of your choices, google assistant, Alexa
and many more devices are result of these revolutions.
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?
AI History
Early Foundations (1940-1950)
o The early foundation of AI is based on Mathematics.
o During this era, many scientists were encouraged to think of intelligent machines
specifically about those machines capable to perform the task which needs human
intelligence.
o The roots of AI can be traced back to the work of early pioneers like Alan Turing,
who proposed the idea of a "universal machine" (the Turing Machine) in 1936.
o In 1950, he introduced the "Turing Test," a method to determine if a machine can
exhibit intelligent behavior indistinguishable from that of a human.
o Similarly, Norbert Wiener developed the field of cybernetics, which explored
control and communication in animals and machines.
Birth of AI (1956-1970)
o The AI was officially born at a summer workshop at Dartmouth College in 1956, organized
by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon.
o This conference is often regarded as the formal founding of AI as a field of study.

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AI History
Birth of AI (1956-1970)
o During this period, early AI programs were developed such as
--Logic Theorist (1956): It mimic human problem-solving skills, proving
mathematical theorems.
--General Problem Solver (GPS, 1957): It simulate human thought
processes in problem-solving.
--ELIZA (1966): LIZA It was an early natural language processing program
that mimicked a psychotherapist, engaging users in conversation.
--Symbolic AI: The symbolic AI is known as "good old-fashioned AI"
(GOFAI). Researchers believed that human intelligence could be replicated
by manipulating symbols, leading to the development of rule-based systems
and expert systems.
AI History
Challenges and AI Winters (1970-1980)
--First AI Winter (1974-1980): Despite early successes, AI research faced significant
challenges. The limitations of early AI programs, particularly their inability to handle
complex real-world problems, led to reduced funding and interest. This period, known
as the "AI Winter," was marked by skepticism and declining government and
commercial support.
--Expert Systems (1980): The expert system was designed to emulate the decision-
making abilities of human experts in specific domains. Systems such as MYCIN, used
for diagnosing bacterial infections, became widely known.
--Second AI Winter (Late 1980-1990): As a result of the rigidity and high development
costs of expert systems, the second AI Winter was marked by disillusionment and
reduced funding.

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AI History
Rise of Machine Learning (1990-2010)
--Neural Networks and Backpropagation (1980-1990): The development of the
backpropagation algorithm allowed neural networks to learn from data more
effectively, leading to renewed interest in AI.
--Machine Learning and Data-Driven Approaches: The 1990s saw a shift from rule-
based AI to machine learning, where systems learned from large datasets. Techniques
such as decision trees, support vector machines, and ensemble methods became
popular.
Modern AI and the Deep Learning Revolution (2010-Present)
--Deep Learning: It is a subset of machine learning that uses deep neural networks to
model complex patterns in data. This approach has driven many recent AI
breakthroughs, including ImageNet Challenge, and AlphaGo.
--Ethical and Societal Implications: The rise of artificial intelligence has raised
ethical concerns, including bias, privacy, job displacement, and misuse of the
technology. Rather than focusing on technological innovations alone, the field is now
working to develop and deploy AI responsibly.
Categories of AI
 AI can be broadly categorized based on different criteria, such as the level of
intelligence exhibited, and the capabilities of the AI systems.
Based on Capabilities
Narrow AI/ Weak AI
o The AI system that performs a single/ dedicated task or a set of closely related
tasks is called a narrow AI system.
o Currently, it is the most common form of AI, operating within a limited context.
Examples: Face recognition
Self driving cars
Speech recognition
Image recognition
Recommendation systems on platforms like Netflix and Amazon

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Categories of AI
Based on Capabilities
General AI
o System that possess the ability to understand, learn, and apply intelligence across
a wide range of tasks, similar to human cognitive capabilities.
o General AI does not currently exist but is a major goal in AI research.
Example: Hypothetical AI systems that could perform any intellectual task that a
human can do.
Superintelligent AI
o Human intelligence would be surpassed on all levels, including creativity,
problem-solving, and social intelligence.
o There are significant ethical and existential concerns with this type of AI, since it
is purely theoretical.
Example: AI systems depicted in science fiction, such as those in movies like "The Matrix" or
"Ex Machina."
Categories of AI
Based on Functionality
Reactive Machine
o This type of machines can respond to specific stimuli or inputs but do not have
memory or the ability to use past experiences to influence future decisions.
Example: IBM’s Deep Blue, the chess-playing computer that defeated world
champion Garry Kasparov.
Limited Memory
o The systems having capability to store past experiences or data to improve
decision-making in future scenarios.
Example: Most modern AI applications, like self-driving cars.
Autonomous vehicles that use data from previous trips to improve their
navigation.

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Categories of AI
Based on Learning Approaches
Supervised Learning
o The AI systems are trained on labeled datasets.
o Input data is paired with the correct output.
o The system learns to map inputs to outputs, making it possible to predict
outcomes for new, unseen data.
Example: Email spam filtering, where the system learns to distinguish between
spam and non-spam emails.
Unsupervised Learning
o The AI systems are given data without explicit labels and must find patterns or
structures within the data.
o This is used for clustering, association, and dimensionality reduction tasks.
Example: Market basket analysis to find product purchase patterns in retail.
Categories of AI
Based on Learning Approaches
Reinforcement Learning
o The AI systems learn by interacting with an environment and receiving rewards
or penalties.
o The goal is to maximize cumulative rewards over time.
Example: Training robots to navigate through a space, where they receive positive
reinforcement for reaching a goal and negative reinforcement for hitting obstacles.
Deep Learning
o A subset of machine learning that uses neural networks with many layers (called
"deep") to model complex patterns in data.
o Deep learning is particularly effective for tasks like image and speech
recognition.
Example: Face recognition systems, autonomous driving systems.

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Applications of AI
Healthcare: AI is used for diagnostics, personalized treatment plans, drug discovery, and
managing health records.
Finance: AI is employed in algorithmic trading, fraud detection, credit scoring, and
personalized financial advice.
Transportation: Autonomous vehicles, traffic management systems, and predictive
maintenance rely on AI for efficient operation.
Entertainment: AI powers recommendation systems, content creation, and interactive
gaming experiences.
Manufacturing: AI optimizes supply chains, predictive maintenance, and quality control
processes.
Education: AI facilitates personalized learning experiences, automated grading, and
educational content creation.
Applications of AI

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Challenges and Ethical Considerations
 While AI offers numerous benefits, it also poses challenges such as bias in
algorithms, privacy concerns, job displacement, and the need for transparency and
accountability.
 Ethical considerations are increasingly important as AI systems become more
integrated into society, requiring careful regulation and oversight.
 AI is a transformative technology with the potential to revolutionize various
industries and improve human life, but it must be developed and deployed
responsibly to maximize its benefits and minimize risks.
The End

Lecture 1-Introduction to AI and its application.pdf

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Lecture 1-Introduction to AI and its application.pdf