Week 1: Artificial Intelligence: Defined
Objectives:
1. Define artificial intelligence (AI).
2. Discuss the fundamentals of AI.
Introduction to Artificial Intelligence
AI refers to machines' ability to mimic or enhance human cognitive functions like reasoning and
learning. It is widely used today in areas such as digital cameras, intelligent electrical grids, and
business automation. AI applies concepts from economics, probability theory, mathematics, and
computer science to solve real-world problems efficiently.
History and Evolution of AI
1940s-1960s: Early AI research by pioneers like Alan Turing and John von Neumann.
1965: Development of early AI systems like Shakey and ELIZA, laying the foundation
for modern AI assistants like Siri and Alexa.
1970s: AI progress slowed due to overhyped expectations and funding cuts.
1980s-1990s: AI resurged with advancements in computer vision, speech recognition,
and machine learning.
2000s-Present: Breakthroughs in neural networks and deep learning have enabled AI to
match human-level performance in tasks like image recognition.
AI continues to evolve, helping automate complex tasks such as fraud detection, logistics
planning, and natural language processing.
Week 2: Artificial Intelligence: A Paradigm
Objectives:
1. Describe artificial intelligence (AI).
2. Compare general AI and narrow AI.
3. Discuss machine learning (ML).
Advancements in AI
By 2001, AI had surpassed human performance in areas like machine translation and object
classification. Breakthroughs such as reinforcement learning and generative models allowed AI
to learn complex behaviors with minimal data. Over the past decade, deep learning has enhanced
AI applications in computer vision, natural language processing, and speech recognition.
AI Fields and Misconceptions
AI is a broad field in computer science that includes:
Machine Learning (ML): Enables computers to improve performance through data
analysis and pattern recognition. It includes:
o Supervised Learning: Uses labeled data for predictions.
o Unsupervised Learning: Identifies patterns in unlabeled data.
Deep Learning: A subset of ML that uses artificial neural networks to achieve high
accuracy in tasks like speech recognition and image processing.
Neural Networks
Inspired by the human brain, neural networks consist of layers of interconnected neurons that
process information. Deep learning models improve with more data, unlike traditional ML
models that reach a performance limit. Common neural network types include:
Convolutional Neural Networks (CNNs): Used for image processing.
Recurrent Neural Networks (RNNs) & Long Short-Term Memory (LSTMs):
Applied in speech and text analysis.
General AI vs. Narrow AI
Narrow AI: Designed for specific tasks (e.g., image recognition, chess-playing).
General AI: Aims to perform all human tasks with intelligence and adaptability, but
remains a future goal in AI research.
AI in Various Industries
AI is transforming industries worldwide, revolutionizing fields such as healthcare, finance,
manufacturing, and autonomous systems. Its impact continues to grow as research advances.
Week 3: AI in Other Sectors
Objectives:
1. Identify the uses of AI in various sectors.
2. Discuss AI applications in healthcare.
AI in Different Industries
Autonomous Vehicles: Companies like Tesla have equipped cars with AI-powered
sensors, cameras, and software for self-driving capabilities. AI is also being explored for
autonomous trucks, impacting road safety and logistics.
Finance & Banking: AI aids in hedge fund investments, fraud detection, and predictive
analytics, helping estimate stock prices and market trends.
Retail: AI enhances stock management, product recommendations, and customer service
through chatbots and automation.
Healthcare: AI assists in diagnosing diseases using medical images, records, and reports,
enabling quicker diagnosis and treatment monitoring. AI is expected to save millions of
lives by 2035.
Advancements in AI Software
Generative Adversarial Networks (GANs): These AI models generate realistic text,
images, and audio, with applications in design, media, and creativity.
GPT-3 (by OpenAI): A deep-learning-based natural language processing (NLP) model
capable of text generation, math problem-solving, and conversation modeling.
DALL·E: An AI model that generates images from text descriptions, trained on datasets
like ImageNet.
AlphaGo (by DeepMind): An AI system that mastered the complex board game Go
using reinforcement learning.
RoBERTa (by Facebook AI Research): A deep learning model designed for NLP tasks
like machine translation and sentence classification.
