Artificial Intelligence Overview

Artificial Intelligence (AI)

• AI is a branch of computer science focused on creating systems capable of tasks requiring human intelligence, including learning, reasoning, problem-solving, perception, and decision-making.
• Key components include machine learning, natural language processing, computer vision, and robotics.
• Applications span healthcare, finance, transportation, and entertainment, automating processes, improving efficiency, and creating new capabilities.

Evolution of AI

• Early beginnings (1950s-1960s): Turing's concept of a machine simulating human intelligence and the Dartmouth conference.
• First AI programs (1950s-1970s): Logic Theorist and General Problem Solver demonstrated machine intelligence.
• AI winter (1970s-1980s): Reduced funding and interest due to limited computational power and unrealistic expectations.
• Expert systems and revival (1980s-1990s): Renewed interest in AI with expert systems and the success of IBM's Deep Blue.
• Modern AI (2000s-Present): Rise of big data, advancements in computational power leading to significant progress in machine learning, especially deep learning.

Types of AI

• Narrow AI (Weak AI): Designed and trained for a specific task (e.g., voice assistants, self-driving cars).
• General AI (Strong AI): Possesses human-like intelligence, understanding, learning, and applying knowledge across various tasks (still theoretical).
• Superintelligent AI: Surpassing human intelligence in all aspects (hypothetical).

Key AI Terminologies

• Algorithm: Set of rules to help an AI system learn and make decisions.
• Artificial Neural Network (ANN): A computing system inspired by the human brain.
• Deep Learning: Machine learning using neural networks with many layers to analyze data.
• Machine Learning (ML): Allows AI systems to learn from experience and improve without explicit programming.
• Natural Language Processing (NLP): Enables machines to understand and respond to human language.
• Supervised Learning: Training AI on labeled data, where input comes with the correct output.
• Unsupervised Learning: Training AI on unlabeled data; it finds patterns and relationships.
• Reinforcement Learning: AI learns through rewards and penalties for actions in an environment.

Domains of AI

• Machine Learning (ML): Using algorithms to learn from and make predictions based on data.
• Natural Language Processing (NLP): Enabling machines to understand human language.
• Computer Vision: Interpreting visual data for tasks such as image recognition.
• Robotics: Creating autonomous robots for various tasks.
• Expert Systems: AI systems utilizing knowledge-based approaches to solve specific problems.
• Reinforcement Learning: Training AI through feedback mechanisms for decision-making.
• Speech Recognition: Understanding and interpreting human speech.

Python Programming Basics

• Character Sets: Letters, digits, and special characters recognized by Python.
• Tokens: Keywords, identifiers, literals, operators, and punctuators in Python.
• Data Types: Numeric (integer, float), string, boolean, list, tuple, and dictionary.
• Control Statements: Conditional statements (if-else, if-elif-else) and looping statements (for, while) for controlling program flow.

CSV Files and Libraries

• CSV files: Comma-separated value format for tabular data.
• NumPy: Python library for numerical computations, especially with arrays and matrices.
• Pandas: Python library for data manipulation and analysis, often using dataframes for tabular data.
• Scikit-learn: Python library for various machine learning algorithms.

Data Pre-processing

• Data cleaning: Removing errors and inconsistencies from data.
• Data transformation: Converting data into appropriate formats.
• Data integration: Combining data from different sources.
• Data reduction: Reducing data volume while maintaining integrity.
• Data discretization: Converting continuous data into discrete categories.

Machine Learning Algorithms

• Supervised Learning: Learning from labeled data—predict output based on input. Classification (categorizing items) and Regression (predicting numerical values).
• Unsupervised Learning: Discovering hidden patterns in unlabeled data, such as Clustering (grouping data points), and Association (finding patterns of relationships).
• Reinforcement Learning: Learning through trial-and-error interactions with an environment to achieve rewards.

Classification Problems

• Binary classification: Two possible outcomes (e.g., spam/not-spam).
• Multiclass classification: Three or more possible outcomes (e.g., image categorization of cats, dogs, rabbits).
• Multilabel classification: Multiple labels for each data point (e.g., image categorization of a picture having multiple elements—cat, grass, sunshine).

Natural Language Processing (NLP)

• Tokenization: Breaking down text into smaller units like words.
• Text preprocessing: Cleaning the text for better analysis (lowercasing, removing punctuation, etc).
• Part-of-speech tagging: Identifying the grammatical role of each word.
• Named entity recognition: Identifying names of people, places, and things.
• Parsing: Understanding the grammatical structure of sentences.
• Semantic analysis: Extracting meaning from words and sentences.
• Sentiment analysis: Determining the emotional tone of a text.
• Machine translation: Converting text from one language to another.
• Text generation: Creating new text based on input.

Al Ethics and Values

• Fairness: Ensuring AI systems are unbiased.
• Transparency: Making AI systems decisions understandable.
• Accountability: Establishing responsibility for AI systems.
• Privacy: Protecting user data.
• Safety and Security: Designing AI systems for robustness against attacks.
• Human-AI Collaboration: Encouraging human oversight in crucial AI applications.
• Societal Impact: Assessing the impact of AI on society, promoting equitable access to Al, and encouraging public discussions.

The Global Demand

• AI's rapidly increasing demand across industries, such as healthcare, finance, retail, and manufacturing.
• Significant investments in AI research and development by countries and companies.
• Growth of the AI job market and continued demand for AI professionals in various fields.

Capstone Project

• A comprehensive assignment showcasing student understanding and application of a particular topic.
• Typical process includes: topic selection, research, project development, presentation, and reflection.
• Encourages critical thinking, problem-solving, and independent learning.

Design Thinking

• A structured approach to problem-solving, emphasizing empathy, ideation, prototyping, and testing to create user-centered solutions.

Data Literacy

• Ability to read, understand, create, and communicate data, essential for informed decisions.
• Includes data organization, summary statistics, identifying patterns, communicating data insights, and critical evaluation of data sources.
• Steps include data cleaning, transformation, and integration.

Data Collection

• Process of gathering information to analyze and make decisions.
• Methods include surveys, interviews, observations, and experiments.