Introduction to Artificial Intelligence.pptx

Introduction to Artificial Intelligence.pptx

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  Introduction to Artificial Intelligence
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  What is ArtificialIntelligence? • Definition: AI is the simulation of human intelligence in machines designed to think and act like humans. • Key Areas: Machine learning, neural networks, natural language processing
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  Brief History ofAI •1950s: Birth of AI; Alan Turing's "Computing Machinery and Intelligence" and the Turing Test. •1956: Dartmouth Conference - Birthplace of AI as an academic discipline. •1960s-1970s: Early AI programs •1980s: Rise of Expert Systems. •1990s-2000s: Advancements in machine learning, Deep Blue defeats Garry Kasparov. •2010s-Present: Big data, deep learning, AI in everyday applications (e.g., Siri, self-driving cars).
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  AI Winter • Definition:Periods of reduced funding and interest in AI research due to unmet expectations and slow progress. • First AI Winter (1974-1980) • Context: • Early enthusiasm for AI led to high expectations. • Initial successes in symbolic AI and expert systems. • Causes: • Overpromising and underdelivering on AI capabilities. • Limited computational power and data storage. • Challenges in handling real-world complexity. • Consequences: • Funding cuts from government and private sectors. • Decline in academic interest and research output. • Shift of focus to more immediately practical technologies.
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  AI Winter • SecondAI Winter (1987-1993) • Context: • Rise of expert systems in the 1980s with commercial applications. • Introduction of specialized AI hardware. • Causes: • Market collapse for Lisp machines (specialized AI hardware). • High cost and limited scalability of expert systems. • Disappointment with the performance of AI in practical applications. • Consequences: • Major companies and startups going out of business. • Reduced investment in AI research and development. • Focus shifted to other areas of computer science and engineering.
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  Todays’ AI • KeyTechnologies 1.Machine Learning (ML) and Deep Learning (DL): 1. Machine Learning: Algorithms that allow computers to learn from and make predictions or decisions based on data. 2. Deep Learning: A subset of ML involving neural networks with many layers (deep neural networks), enabling advanced image and speech recognition. 2.Natural Language Processing (NLP): 1. Language Models: AI models like OpenAI's GPT-4 and Google's BERT that understand and generate human language, powering chatbots, translation services, and more. 2. Speech Recognition and Generation: Technologies like Google's Assistant, Amazon's Alexa, and Apple's Siri that understand and respond to spoken language. 3.Computer Vision: 1. Image Recognition: Used in applications like facial recognition, autonomous vehicles, and medical imaging. 2. Video Analysis: AI systems that analyze video footage for security, entertainment, and sports analytics. 4.Reinforcement Learning: 1. Techniques where AI learns by interacting with an environment to maximize cumulative rewards, applied in robotics, gaming, and autonomous systems.
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  Todays’ AI Industry ApplicationDescription Healthcare Medical Imaging AI systems that analyze medical images to detect diseases like cancer. Healthcare Drug Discovery AI models predicting how different molecules might interact, speeding up the development of new drugs. Healthcare Personalized Medicine AI algorithms analyzing patient data to provide tailored treatment plans. Finance Algorithmic Trading AI-driven trading strategies in stock markets. Finance Fraud Detection AI systems monitoring transactions to detect fraudulent activities. Finance Customer Service Chatbots and virtual assistants providing financial advice and support. Transportation Autonomous Vehicles Self-driving cars and drones using AI for navigation and decision-making. Transportation Traffic Management AI systems optimizing traffic flow in cities to reduce congestion. Retail Recommendation Systems AI algorithms suggesting products to customers based on their preferences and behavior. Retail Inventory Management AI models predicting demand and optimizing stock levels. Manufacturing Predictive Maintenance AI systems predicting equipment failures before they occur. Manufacturing Quality Control AI-based image recognition ensuring products meet quality standards. Entertainment Content Creation AI generating music, art, and writing. Entertainment Personalized Experiences AI recommending movies, shows, and music based on user preferences.
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  Todays’ AI • Ethicaland Societal Considerations 1.Bias and Fairness: Ensuring AI systems do not perpetuate or amplify biases present in training data. 2.Privacy: Balancing the benefits of AI with the need to protect personal data. 3.Job Displacement: Addressing the impact of AI automation on employment and ensuring a smooth transition for affected workers. 4.Regulation: Developing policies and frameworks to govern the ethical use of AI.
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  Historical milestones inthe development of AI
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  Period Year Event EarlyFoundations (Before 1950s) 1943 Warren McCulloch and Walter Pitts published a seminal paper on artificial neurons, laying the groundwork for neural networks. Early Foundations (Before 1950s) 1949 Donald Hebb introduced the concept of Hebbian learning, which describes how neurons might adapt during learning. The Birth of AI (1950s) 1950 Alan Turing published 'Computing Machinery and Intelligence,' introducing the Turing Test as a measure of machine intelligence. The Birth of AI (1950s) 1956 The Dartmouth Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, officially coined the term 'artificial intelligence.' Early AI Programs (1950s-1960s) 1956 Logic Theorist, considered the first AI program, was developed by Allen Newell and Herbert A. Simon. Early AI Programs (1950s-1960s) 1957 Frank Rosenblatt developed the Perceptron, an early type of artificial neural network. Early AI Programs (1950s-1960s) 1961 The first industrial robot, Unimate, was introduced, demonstrating early robotics applications. Growth and Challenges (1970s-1980s) 1970 AI research faced its first 'AI winter' due to unmet expectations and reduced funding. Growth and Challenges (1970s-1980s) 1972 The first expert system, DENDRAL, was developed for chemical analysis. Growth and Challenges (1970s-1980s) 1980 The Japanese Fifth Generation Computer Systems project aimed to create intelligent computers and reinvigorated AI research. Resurgence and Advancements (1980s-1990s) 1986 Geoffrey Hinton, David Rumelhart, and Ronald Williams published a paper on backpropagation, a key algorithm for training neural networks. Resurgence and Advancements (1980s-1990s) 1997 IBM's Deep Blue defeated world chess champion Garry Kasparov, showcasing the power of AI in strategic games. Modern AI (2000s-Present) 2006 Geoffrey Hinton and his team developed deep learning techniques, leading to significant improvements in machine learning. Modern AI (2000s-Present) 2011 IBM's Watson won the quiz show 'Jeopardy!' by competing against human champions. Modern AI (2000s-Present) 2012 The AlexNet neural network, developed by Geoffrey Hinton's team, won the ImageNet competition, significantly advancing computer vision. Modern AI (2000s-Present) 2016 DeepMind's AlphaGo defeated Go champion Lee Sedol, a milestone in AI's ability to handle complex games. Modern AI (2000s-Present) 2020 OpenAI's GPT-3, a state-of-the-art language model, demonstrated the potential of large-scale unsupervised learning. Modern AI (2000s-Present) 2022 DeepMind's AlphaFold solved the protein folding problem, making significant contributions to biological research. Modern AI (2000s-Present) 2023 The release of OpenAI's GPT-4, an even more advanced language model, marked another leap in natural language processing capabilities.
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  Title: Influential Figuresin Artificial Intelligence Name Contribution Notable Work Alan Turing Theoretical Computer Science, AI Turing Test, Turing Machine John McCarthy Coined 'Artificial Intelligence' LISP, AI Conferences Marvin Minsky AI Research Pioneer Frames Theory, Neural Networks Geoffrey Hinton Deep Learning Pioneer Backpropagation, Neural Networks Yoshua Bengio Deep Learning, AI Ethics Unsupervised Learning Research Andrew Ng AI Education Advocate Google Brain, Coursera AI Courses Fei-Fei Li Computer Vision Leader ImageNet, AI for Social Good Elon Musk AI Safety Proponent OpenAI, AI Implications Research Demis Hassabis DeepMind Co-founder AlphaGo, Reinforcement Learning
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  Types of AI •NarrowAI (Weak AI): Specialized in one task (e.g., voice assistants). •General AI (Strong AI): Machines with the ability to understand, learn, and apply knowledge in different contexts, similar to human intelligence (still theoretical). •Superintelligent AI: Hypothetical AI that surpasses human intelligence in all aspects (speculative future).
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  Narrow AI Examples •Speech Recognition: Siri, Alexa, Google Assistant. • Image Recognition: Face recognition software. • Recommendation Systems: Netflix, Amazon.
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  General AI • Definition:AI that can perform any intellectual task that a human can. • Current Status: Still in the research phase, not yet achieved.
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  Superintelligent AI • Definition:AI that surpasses human intelligence. • Potential Impact: Transformative impact on society, ethical considerations, and existential risks