Lecture 1-Introduction to AI and its application.pdf

Lecture 1-Introduction to AI and its application.pdf

• 1.
  10/28/2024 1 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
• 2.
  10/28/2024 2 Introduction to ArtificialIntelligence 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? Introduction to Artificial Intelligence 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.
• 3.
  10/28/2024 3 Introduction to ArtificialIntelligence 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. Introduction to Artificial Intelligence 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.
• 4.
  10/28/2024 4 Introduction to ArtificialIntelligence 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. Introduction to Artificial Intelligence 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
• 5.
  10/28/2024 5 Introduction to ArtificialIntelligence 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." Introduction to Artificial Intelligence 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.
• 6.
  10/28/2024 6 Introduction to ArtificialIntelligence 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. Introduction to Artificial Intelligence 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.
• 7.
  10/28/2024 7 Introduction to ArtificialIntelligence 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. Introduction to Artificial Intelligence Applications of AI
• 8.
  10/28/2024 8 Introduction to ArtificialIntelligence 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