AI Fundamentals and Rational Thinking

Artificial Intelligence

• AI is a wide-ranging branch of computer science that focuses on building intelligent machines capable of performing tasks that typically require human intelligence.
• It encompasses various techniques, including machine learning, natural language processing, computer vision, and robotics.

Applications of AI

• Healthcare: Assisting in diagnosis, personalized treatment plans, automating administrative tasks, and enabling drug discovery.
• Finance: Fraud detection, risk assessment, algorithmic trading, and customer service chatbots.
• Transportation: Self-driving cars, optimizing traffic flow, improving logistics, and enhancing aviation safety.
• Retail: Personalized recommendations, improving customer service, optimizing pricing strategies, and enhancing supply chain management.
• Manufacturing: Optimizing production processes, predicting equipment failures, automating quality control, and enabling predictive maintenance.
• Education: Personalizing learning experiences, providing adaptive feedback, identifying at-risk students, and automating administrative tasks.
• Entertainment: Powering recommendation engines, creating personalized gaming experiences, and enabling virtual assistants.
• Environment: Monitoring environmental conditions, predicting natural disasters, optimizing energy consumption, and developing sustainable solutions.

History of AI

• AI is not new. Mechanical men are mentioned in ancient Greek and Egyptian myths.
• Early AI research, beginning with milestones in the 1940s and 1950s focused on developing algorithms for solving mathematical problems.
• The 1970s saw the first AI winter, due to a shortage of funding for AI research.
• In the 1980s, the resurgence of AI included the creation of the first intelligent humanoid robot (WABOT-1).

PEAS

• A framework for describing the essential components that shape an AI agent's behavior in its environment.
• Components are:
  • Performance measure: Criteria the agent uses to evaluate its actions.
  • Environment: Surrounding context affecting the agent's behavior.
  • Actuators: Mechanisms enabling the agent to interact with the environment.
  • Sensors: Mechanisms that allow the agent to perceive and gather information about the environment.

Data and Computation

• AI relies heavily on vast amounts of data for training machine learning models.
• Powerful computers and distributed computing platforms are essential to process and analyze large datasets.

Heuristic Function

• A heuristic function is a way to estimate the cost to reach the goal node (e.g., in pathfinding).

Task Environments

• Discrete vs Continuous: Discrete environments have limited percepts and actions (e.g., chess), while continuous environments have a continuous range of percepts and actions (e.g., self-driving cars).
• Known vs Unknown: Known environments have known results of actions, unknown environments require learning through exploring.
• Single Agent vs Multi-agent: Single agent environments involve a single interacting agent, while multi-agent environments involve multiple interacting agents.
• Episodic vs Sequential: Episodic environments involve single-shot actions (e.g., a room cleaner), while sequential environments require memory of past actions (e.g., a self-driving car).
• Deterministic vs Stochastic: Deterministic environments are predictable, stochastic environments are uncertain.
• Fully observable vs Partially observable: fully observable environments, gives complete state to the agent, partially observable environments gives incomplete state information to the agent.

Simple Reflex Agent

• The simplest type of agent.
• It takes decisions solely based on the current perceptions and ignores the rest of the percept history.
• Only effective in fully observable environments.

Model-Based Agent

• Agents that operate in partially observable environments.
• They maintain an internal model of the environment.

Utility-Based Agent

• Similar to goal-based agents, but they also consider the success at a given state.
• Useful when multiple possibilities exist. An agent choosing the best course of action to achieve a goal.

Goal-Based Agent

• The agent needs to know its goal (describes desirable situations).
• Helpful when current state alone isn't sufficient.
• It requires planning and searching.

Learning Agent

• Agents that learn from their experiences.
• Components include:
  • Learning element
  • Critic
  • Performance element
  • Problem generator

Depth First Search

• A recursive algorithm for traversing a tree or graph.
• It starts from the root node and follows each path to its greatest depth before moving to the next path.

Breadth First Search

• Breadth-first search explores all nodes at the current level before moving to nodes at the next level.

Uniform Cost Search

• An algorithm for traversing a weighted graph.
• It expands nodes based on their path cost from the root node.
• It seeks the path of lowest cumulative cost.

Depth Limited Search

• Similar to depth-first search, but with a predefined depth limit.
• Helps to avoid infinite loops in depth-first search.

Iterative Deepening Depth-First Search

• A combination of DFS and BFS.
• It gradually increases the depth limit until a goal is found.

Bidirectional Search

• Two simultaneous searches, one starting from the initial state and the other from the goal state.
• Aims to find the intersection of the search spaces to converge faster.

Thinking Rationally and Acting Rationally

• Thinking rationally emphasizes reasoning and logical inference.
• Acting rationally emphasizes choosing actions that maximize utility.

Thinking Humanly And Acting Humanly

• Thinking Humanly: Inspired by cognitive science, psychology, and neuroscience.
• Acting Humanly: Using techniques such as computer vision and natural language processing.

Turing Test

• A test to determine if a machine can think like a human.

PEAS Descriptions

• Part-picking robot: Tasks include picking correct parts and placing them in correct bins, minimizing time taken to pick and place, and avoiding collisions.
• Medical diagnosis system: Tasks include accurately diagnosing the patient's condition, providing personalized treatment plans, and minimizing unnecessary tests.

Artificial Neural Networks

• Inspired by the structure and function of the human brain.
• Consists of interconnected nodes (neurons) organized in layers.
• Used for tasks like image recognition, natural language processing, and speech recognition.

Support Vector Machines (SVM)

• A powerful algorithm used for classification and regression tasks.
• Models data by creating hyperplanes (or decision boundaries).
• It emphasizes selecting extreme data points (support vectors) to maximize the margin between data classes.

Entropy

• A measure of uncertainty or randomness in a dataset.
• Used in machine learning to assess the purity of data, guide decision-making, and evaluate the best feature for splitting in tasks like decision tree building.

Reinforcement Learning

• Learning through trial and error by interacting with an environment.
• The goal is to maximize reward over time.
• Learn optimal policies (strategies) using input from an environment, and feedback mechanisms to learn the best course of action.

Supervised Learning

• Algorithms learn mapping from input to output using labeled examples.
• Predicts outputs from new data with similar characteristics to the known datasets.

Unsupervised Learning

• Algorithms learn from unlabeled data to discover patterns, identify anomalies, and cluster data.
• Doesn't require corresponding output data to train.

Q-Learning

• Model-free reinforcement learning algorithm.
• Learns an optimal policy through interaction with the environment and evaluating the consequences of actions.

Association Rule Mining

• Discovers relationships between items in large datasets.
• Outputs are "if-then" rules (e.g., if X, then Y).
• Evaluated by support (how often X and Y appear together) and confidence (probability that Y appears given X).

Bias-Variance Trade-off

• A crucial aspect of machine learning models.
• Models with high variance tend to overfit.
• Models with high bias tend to underfit.
• The ideal model strikes a balance between these two extremes.

Knowledge Representation

• A field in AI focused on encoding knowledge in a form usable by AI systems.
• Methods include: rule-based systems, semantic networks, and first-order logic.

Hidden Markov Models (HMMs)

• Statistical models for systems with hidden states.
• Inferring hidden states based on observed data.

Temporal Difference Learning (TD Learning)

• A type of reinforcement learning that bootstraps from current value estimates.
• It learns by observing the difference between expected future rewards and current rewards.

Test your knowledge on the principles and methods of artificial intelligence, focused on thinking rationally and acting humanly. This quiz covers a variety of approaches, including reinforcement learning and techniques used in AI systems for complex reasoning tasks.