AI (Artificial Intelligence) Introduction.pptx

AI (Artificial Intelligence) Introduction.pptx

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  AI (ARTIFICIAL INTELLIGENCE)Introduction: History, Applications and Future.
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  INTRODUCTION Artificial Intelligence (AI)is a branch of computer science focused on creating systems or machines that can perform tasks typically requiring human intelligence. These tasks include problem-solving, decision-making, learning, perception, language understanding, and more. AI aims to replicate or simulate human cognitive functions through algorithms, neural networks, and other computational methods.
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  KEY CONCEPTS INAI: Machine Learning (ML): A subset of AI that enables machines to learn from data and improve their performance over time without explicit programming. ML includes techniques like supervised learning, unsupervised learning, and reinforcement learning. Deep Learning: A more advanced subset of machine learning that uses neural networks with many layers (hence "deep") to model complex patterns in large datasets. Deep learning has been particularly successful in areas like image and speech recognition. Natural Language Processing (NLP): A field of AI focused on the interaction between computers and humans using natural language. NLP enables machines to understand, interpret, and respond to human language, which is crucial for applications like chatbots, language translation, and sentiment analysis.
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  KEY CONCEPTS INAI: Robotics: An area of AI where machines are designed to perform tasks that involve physical movement. Robotics often integrates AI to allow robots to navigate environments, recognize objects, and make decisions in real-time. Expert Systems: AI programs that mimic the decision-making ability of a human expert. These systems use knowledge-based rules to solve complex problems in specific domains, such as medical diagnosis or financial forecasting. Computer Vision: A field of AI that enables machines to interpret and understand visual information from the world, such as images or videos. This technology is used in applications ranging from facial recognition to autonomous vehicles.
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  KEY CONCEPTS INAI:
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  HISTORY The Birth ofAI (1950s - 1960s) 1950s: Early AI research focused on problem-solving and symbolic methods. Allen Newell and Herbert A. Simon developed the Logic Theorist and the General Problem Solver, programs capable of solving mathematical problems and proving theorems. 1957: Frank Rosenblatt developed the Perceptron, an early model of a neural network that could learn to recognize patterns. This was a significant step toward machine learning. 1960s: AI research expanded into robotics, with Shakey the robot, developed at Stanford Research Institute (SRI), becoming one of the first robots to navigate and perform tasks in a controlled environment.
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  HISTORY
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  APPLICATIONS OF AI: Healthcare:AI is used in diagnostic tools, personalized treatment plans, drug discovery, and robotic surgery. Finance: AI powers fraud detection, algorithmic trading, risk management, and customer service automation. Transportation: Autonomous vehicles, traffic management systems, and logistics optimization are driven by AI. Entertainment: AI is used in recommendation systems (like those on Netflix or Spotify), video game design, and content creation. Education: AI personalizes learning experiences, automates grading, and supports educational tools like tutoring systems.
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  CHALLENGES AND CONSIDERATIONS: Ethics:AI raises ethical concerns, including privacy issues, bias in decision-making, and the potential displacement of jobs. Transparency: Understanding how AI systems make decisions is crucial, especially in high-stakes areas like healthcare and criminal justice. Safety: Ensuring that AI systems operate safely and predictably is a major concern, particularly as they become more autonomous.
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  THE FUTURE OFAI: General AI Development: Progress toward human-like intelligence capable of performing a wide range of tasks. Healthcare Transformation: AI-driven personalized medicine, diagnostics, and drug discovery. Autonomous Systems: Expansion of self-driving vehicles and advanced robotics. Ethical AI: Growing focus on fairness, transparency, and regulation to address AI's societal impact. Workforce Changes: Increased automation, with a shift toward human-AI collaboration and the need for reskilling. Creative AI: AI's growing role in art, music, and personalized entertainment.
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  THE FUTURE OFAI: Education Revolution: Personalized learning and lifelong education powered by AI. Environmental Impact: AI solutions for sustainability and climate change mitigation. Quantum AI: Potential breakthroughs in AI capabilities through quantum computing. Global Challenges: AI applications to address issues like poverty, disease, and inequality