CS-323-AI-LEC0-FA-24.pptx

CS-323 Artificial Intelligence Module 1: Introduction to AI Instructor: Dr. Tabassum Learning CLO1:Explore AI contemporary techniques of ObjectivesCLO2:Solve problems using AI CLO3: Demonstrate the use of modern tools The Brain The brai...

CS-323 Artificial Intelligence Module 1: Introduction to AI Instructor: Dr. Tabassum Learning CLO1:Explore AI contemporary techniques of ObjectivesCLO2:Solve problems using AI CLO3: Demonstrate the use of modern tools The Brain The brain contains billions of nerve cells arranged in patterns that coordinate thought, emotion, behavior, movement and sensation. Each neuron is connected to more than 1,000 other neurons, making the total number of connections in the brain around 60 trillion!  Natural Intelligence Human Brain: Capability is learn from experience, think and create Neurons Nerve cells, known as neurons, send and receive nerve signals. They have two main types of branches coming off their cell bodies. Dendrites receive messages from other nerve cells. Axons carry outgoing messages from the cell body to other cells — such as a nearby neuron or muscle cell. Interconnected with each other, neurons provide efficient, lightning-fast communication. Neurons Network Neurons Neurons communicate with each other using electrochemical signals. In other words, certain chemicals in the body known as ions have an electrical charge. Ions move in and out of the neuron across the cell membrane and affect the electrical charge of the neuron.  Neurotransmitters A nerve cell communicates with other cells through electrical impulses when the nerve cell is stimulated. Within a neuron, the impulse moves to the tip of an axon and causes the release of chemicals, called neurotransmitters, that act as messengers. Neurotransmitters pass through the gap between two nerve cells, known as the synapse. They then attach to receptors on the receiving cell. This process repeats from neuron to neuron as the impulse travels to its destination. This web of communication that allows you to move, think, feel and communicate. Firing of Neurons When a neuron is at rest, the cell body, or soma, of the neuron is negatively charged relative to the outside of the neuron. A neuron at rest has a negative charge of approximately -70 millivolts (mV) of electricity. However, when a stimulus comes it causes the neuron to take in more positive ions, and thepositively charged. neuron becomes more Once the neuron reaches a certain threshold of approximately -55mV, an event known as an action potential occurs and causes the neuron to “fire.” Where this course will take you? Artificial Intelligenc e AI Artificial Intelligence (AI) encompasses developing computer systems capable of performing tasks that typically require human intelligence. These tasks include learning from experiences, interpreting complex data, making decisions based on the information gathered, understanding natural language, and recognizing patterns or objects. The essence of AI lies in its ability to mimic cognitive functions associated with the human mind, such as learning and problem- solving. What is AI? What it can do? Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. The more data AI systems have and the more they practice, the better they become at their tasks. This ability to learn and improve without constant human instruction makes AI so powerful and versatile in solving complex problems.  What is AI? What it can do? 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Despite this, foundational work in machine learning, expert systems, and natural language processing laid the groundwork for future advancements. AI History 1990s to 2000s: A resurgence in AI research was fueled by improvements in computer hardware, increased data availability, and new algorithms. This era saw the rise of the internet, which significantly enhanced data collection and distribution, facilitating machine learning and the development of more sophisticated AI applications. 2010s to Present: The current boom in AI is driven by breakthroughs in deep learning, big data, and computational power. AI Subsets Machine Learning Machine Learning (ML) is a subset of AI focused on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. It involves algorithms that allow computers to learn from and make predictions or decisions based on data. Deep Learning Deep Learning is a subset of machine learning that uses neural networks with many layers (hence, “deep”). These neural networks attempt to simulate the behavior of the human brain—albeit at a very basic level— allowing the machine to learn from large amounts of data. Deep learning has been instrumental in advancing fields like natural language processing, computer vision, and audio recognition.  AI Key Components Artificial Intelligence (AI) is built upon several key components that work together to enable machines to perform tasks that typically require human intelligence. These components include algorithms, data, computing power, and models. AI Key Components- 1.Algorithms Algorithms are step-by- step procedures or formulas for solving problems. In AI, algorithms process data, make decisions, and learn from patterns. AI Operation Algorithms: Algorithms are rules or instructions AI follows to perform tasks or solve problems. They play a crucial role in AI systems, dictating the logic behind decision-making, pattern recognition, and learning from data. AI Key Components 2. Data Data is the foundation of AI. AI systems require large amounts of data to learn and make accurate predictions. The quality and quantity of data significantly impact the performance of AI models. Types of Data Structured Data: Databases and spreadsheets are organized in a tabular format. Examples include customer information, transaction records, and sensor readings. Unstructured Data: Data that is not organized in a predefined manner, such as text, images, audio, and video. Examples include social media posts, emails, and medical images. AI Key Components- Data Processing Data often needs to be preprocessed before it is fed into AI models. Data preprocessing includes cleaning, normalizing, and transforming data to ensure it is in the right format and quality for  AI systems require lots of data to learn and make informed decisions. Data processing in AI involves collecting, cleaning, and structuring data to make it suitable for analysis. Data This process includes handling missing values, removing outliers, Processing and converting data into a format the algorithms can work with efficiently. Once processed, this data feeds into the AI models, enabling them to learn from past examples and improve their performance over time. AI Key Components 3.Computing power Computing power is crucial for training and deploying AI models. The complexity and size of modern AI models require significant computational resources to process large datasets and perform complex calculations. AI Key Components Computing power Types of Computing Resources Central Processing Units (CPUs): General- purpose processors that handle various computing tasks. While essential, they are often not sufficient for high-performance AI tasks. Graphics Processing Units (GPUs): Specialized processors designed for parallel processing are ideal for training deep learning models. GPUs can efficiently handle the massive computations required for training neural networks. Tensor Processing Units (TPUs): Custom- designed processors by Google specifically for AI tasks, offering optimized performance AI Key Components- 4. Models AI models are mathematical representations of real-world processes created by training algorithms on data. These models can make predictions, recognize patterns, and make decisions based on new input data. Types of Models Machine Learning Models: These include linear regression, logistic regression, decision trees, random forests, and support vector machines. They are used for tasks like classification, regression, and clustering. Types of Models Deep Learning Models: These are neural networks with multiple layers capable of learning complex patterns in data. Examples include convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequential data, and transformers for natural language processing (NLP). Ensemble Models: These combine multiple models to improve performance and robustness. Techniques like bagging, boosting, and stacking create ensemble models. Model Training and Evaluation Training involves adjusting the model’s parameters using algorithms to minimize prediction errors. This process requires iterative testing and refinement. Evaluation involves assessing the model’s performance using accuracy, precision, recall, and F1 score metrics. Cross-validation and testing on unseen data help ensure the model generalizes well.  Model Deployment and Maintenance Maintenance includes monitoring the model’s performance over time, retraining it with new data as needed, and updating it to improve accuracy and adapt to changing conditions. The algorithms are programmed, the data is trained, and the computing power is available. Once trained, AI models need to be deployed in real-world applications. This involves integrating the model into software systems and ensuring it can handle live data. Types of AI Narrow AI (or Weak AI) Refers to AI systems designed and trained for a specific task. These systems operate within a limited pre- defined range or set of contexts. Examples of Narrow AI are prevalent in our daily lives, including speech recognition systems like Siri or Alexa, recommendation systems on Netflix or Amazon, and facial recognition technologies. Though highly effective within its scope, Narrow AI lacks a human’s understanding or consciousness. General AI (or Strong AI) General AI represents a system that can understand, learn, and apply its intelligence to solve problems with the same competence as humans. This level of AI could perform any intellectual task a human can do. General AI is still theoretical, with no existing systems displaying this level of versatility and adaptability. Its vast potential, however, encompasses the ability to reason, solve puzzles, make judgments under uncertainty, plan, learn, and integrate prior knowledge in decision- making processes. Super AI Super AI is a hypothetical stage of artificial intelligence that surpasses human intelligence across all fields, including creativity, general wisdom, and problem-solving. Its implications are profound, raising questions about ethics, governance, and the very fabric of human society. While the development of Super AI could lead to unparalleled technological advancements and solutions to complex global challenges, it poses significant risks if not properly managed, including ethical dilemmas and existential threats to humanity. Applications of AI Applications Smart Home Devices: AI powers smart thermostats, lights, and security systems, allowing for automated home management that adapts to the user’s habits and preferences, improving energy efficiency and security. Personal Assistants: Digital assistants like Siri, Alexa, and Google Assistant use AI to understand natural language, making it possible to set reminders, control smart devices, and obtain information through voice commands. Applications Online Customer Support: AI chatbots and virtual assistants provide round-the-clock customer service across many websites and applications, offering instant responses to queries and improving user experience. Recommendation Systems: Streaming services like Netflix and Spotify use AI to analyze your viewing or listening habits, recommending movies, TV shows, or music tailored to your tastes. Applications E-commerce: AI enhances online shopping experiences through personalized recommendations, virtual try-on features, and optimized logistics, making shopping more convenient and tailored to individual preferences. Healthcare: AI applications include diagnostic tools that analyze medical images, wearable devices monitoring health metrics, and personalized treatment plans, significantly improving patient care and outcomes. Finance: AI drives algorithmic trading, fraud detection, and personalized financial advice, transforming how individuals and institutions interact with financial markets and manage personal finances. Applications Education: AI-powered educational platforms offer personalized learning experiences, adapting the content to match the learner’s pace and understanding, enhancing engagement and effectiveness. Navigation and Transportation: AI improves route optimization, traffic management, and ride-sharing services, making transportation more efficient. Autonomous vehicles powered by AI are set to revolutionize how we commute. Content Creation: AI tools assist in generating written content, music, artwork, and even video games, offering new ways for creators to innovate and produce content. AI Pros and Cons  Impact of AI AI technology has become integral to various aspects of business and everyday life. While it offers numerous advantages, it also comes with significant challenges. Enhanced Efficiency and Productivity Description: AI can automate repetitive tasks, freeing human resources to focus on more complex and creative work. This leads to increased productivity and efficiency. Examples: Manufacturing: AI-powered robots perform repetitive tasks like assembly and quality control, significantly speeding up production lines. Customer Service: AI chatbots handle routine inquiries, allowing human agents to address more complex issues. Improved Decision-Making Description: AI systems can analyze vast amounts of data quickly and accurately, providing insights that help make informed decisions. Examples: Healthcare: AI analyzes medical data to aid in diagnosis and treatment plans, improving patient outcomes. Finance: AI algorithms predict market trends and optimize investment strategies. Cost Savings Description: By automating processes and improving efficiency, AI helps reduce operational costs. Examples: Retail: AI optimizes supply chain management, reducing waste and lowering inventory costs. Energy: AI systems manage energy use in buildings, reducing consumption and costs. 24/7 Availability Description: AI systems can operate continuously without fatigue, providing services and support around the clock. Examples: Customer Support: AI-powered chatbots offer 24/7 customer service, handling inquiries anytime. Healthcare Monitoring: AI monitors patient vitals continuously, alerting healthcare providers to anomalies. Personalization and Enhanced User Experience Description: AI enables the personalization of services and products, enhancing user experience by tailoring offerings to individual preferences. Examples: E-commerce: AI recommends products based on user browsing and purchase history. Streaming Services: Platforms like Netflix use AI to suggest content that matches user interests. Job Displacement and Economic Disruption Description: AI and automation can lead to job losses, particularly in industries reliant on routine tasks. Examples: Manufacturing: Automation has replaced many assembly line jobs. Customer Service: AI chatbots and virtual assistants reduce the need for human agents. Bias and Discrimination Description: AI systems can perpetuate and even exacerbate biases in their training data, leading to unfair outcomes. Examples: Hiring Algorithms: AI tools used for recruitment have been found to favor male candidates over female ones due to biased training data. Credit Scoring: AI algorithms can discriminate against certain demographic groups if the training data reflects existing biases. Privacy and Security Concerns Description: AI systems often require access to vast personal data, raising significant privacy and security issues. Examples: Surveillance: AI-powered surveillance systems can lead to invasions of privacy if not properly regulated. Data Breaches: AI systems storing sensitive information are targets for cyber-attacks. Ethical and Moral Dilemmas Description: AI systems can pose ethical and moral questions regarding decision-making in life-critical situations. Examples: Autonomous Vehicles: Decisions made by self-driving cars in accident scenarios raise ethical questions about the value of human lives. Healthcare: AI deciding on patient treatment plans without human oversight can lead to ethical concerns about accountability and consent. High Implementation Costs Description: Developing and implementing AI systems can be expensive, requiring significant investment in technology, infrastructure, and skilled personnel. Examples: Healthcare: Implementing AI-driven diagnostic tools requires substantial investment in technology and training. Manufacturing: Setting up AI-powered automation systems involves high initial costs for equipment and software. AI Limitations AI Limitations 1.Technical Limitations 1.AI systems are limited by the data they are trained on, which can lead to biases or inaccuracies if the data is flawed. 2.The complexity of real-world problems often exceeds the current capabilities of AI, requiring human intervention for nuanced decision- making. 2.Ethical and Societal Challenges 1.Issues like privacy concerns, algorithmic biases, and the ethical use of AI are at the forefront of discussions. 2.The potential for job displacement due to automation poses significant societal challenges. 3.Future Challenges 1.Ensuring its alignment with human values and interests as AI advances remains a critical challenge. 2.Researchers, policymakers, and society are continually balancing innovation with regulation and addressing the global implications of AI technology. Future of AI Future Healthcare: Personalized Medicine Impact: Tailored treatment plans based on genetic information, significantly improving outcomes for chronic and genetic diseases. Environmental Management: Climate Action Impact: AI-driven models to predict climate change impacts and optimize resource use, helping to mitigate environmental damage and promote sustainability. Automotive: Autonomous Vehicles Impact: The widespread adoption of self-driving cars reduces accidents caused by human error and transforms urban transportation. Manufacturing: Smart Factories Impact: AI-powered robotics and predictive maintenance lead to safer, more efficient, cost- effective production lines. Future Finance: Automated Trading and Personal Finance Impact: AI algorithms dominate stock market trading and personal finance apps, providing customized financial advice and revolutionizing how individuals and institutions invest. Education: Adaptive Learning Platforms Impact: Personalized education experiences that adapt to student learning styles, improving accessibility and outcomes in education. Retail: Hyper-personalized Shopping Impact: AI is transforming online and in-store shopping experiences with personalized recommendation and automated inventory management, enhancing customer satisfaction and loyalty. Future Cybersecurity: Proactive Threat Detection Impact: AI systems identify and neutralize cyber threats before they cause damage, significantly enhancing data protection. Agriculture: Precision Farming Impact: AI optimizes crop yields and reduces resource waste through precise monitoring and managing farm conditions. Entertainment: Customized Content Creation Impact: AI generates personalized entertainment content, including music, video games, and movies, reshaping the creative landscape. Self-test What is the difference between AI, ML, and DL? What are the key components of AI? What is the difference between weak and strong AI? What are the applications of AI?