Introduction to AI

• Artificial Intelligence (AI) is presented by Dr. Naglaa Fathy, a lecturer in the Computer Science Department at the Faculty of Computers and Artificial Intelligence, Benha University.
• The course uses the textbook "Artificial Intelligence: A Modern Approach, Third Edition" by Stuart J. Russell and Peter Norvig as a reference.

Course Content

• Introduction to AI.
• Intelligent Agents: Includes agents, environments, rationality, and PEAS (Performance, Environment, Actuators, Sensors).
• Problem Solving and Search: Includes problem-solving agents, problem formulation, uninformed search algorithms (BFS, DFS, Uniform-cost, Iterative deepening), and informed search algorithms (Best-first, Greedy Best-first, A*).
• Constraint Satisfaction Problems.

Why Study AI?

• AI is more exciting than other areas of computer science.
• AI has a strong experimental component.
• AI is under development.
• AI utilizes both theory and practice.
• AI has a different methodology compared to other fields.
• Intelligent agents are becoming ubiquitous.
• Advances in AI are often adopted by other computer science areas.

Goals of AI

• Reduce the time needed to perform specific tasks.
• Improve human-machine interaction.
• Enhance the accuracy and speed of medical diagnoses.
• Improve communication between humans and machines.
• Help people learn new information more quickly.

Defining AI

• AI is a branch of computer science focused on simulating intelligent behavior in computers.
• AI can be considered the study of making computers perform tasks at or above human level.
• AI refers to the simulation of human intelligence in machines programmed to think and act like humans.

Four Approaches to AI

• Thinking humanly: Involves creating computers with minds.
• Haugeland in 1985 defined it as "machines with minds, in the full and literal sense”
• It also includes automating activities associated with human thinking like decision-making and problem-solving.
• Thinking rationally: Focuses on the study of mental faculties through computational models.
• Charniak and McDermott in 1985 characterized the study of mental faculties through the use of computational models.
• Winston in 1972 characterized the study of computations which enables perception, reasoning, and action.
• Acting humanly: Involves creating machines that perform functions requiring intelligence when done by humans.
• Kurzweil characterized this in 1990 as "creating machines that perform functions that require intelligence when performed by people"
• Focuses on how to make computers excel at tasks currently better performed by people.
• Rich and Knight characterized this in 1991 "how to make computers do things at which, at the moment, people are better"
• Acting rationally: It is concerned with the automation of intelligent behavior.

The Turing Test

• Introduced by Alan Turing in 1950, which assesses a machine's ability to think like a human.
• A computer is considered intelligent if it can mimic human responses under specific conditions.
• Natural language processing and knowledge representation are required for communication with humans and storing information effectively.
• Automated reasoning retrieves information to answer questions.
• Machine learning helps adapt to new circumstances.

Areas of AI

• Speech recognition
• Natural language processing (NLP)
• Computer vision
• Image processing
• Robotics
• Pattern recognition
• Machine learning
• Neural networks
• Deep learning

AI, Machine Learning, and Deep Learning

• Artificial Intelligence: Machine's capability to imitate intelligent human behavior.
• Machine Learning: Application of AI that allows a system to automatically learn and improve from experience.
• Deep Learning: Application of Machine Learning using complex algorithms and deep neural networks to train a model.

Application of AI

• Smartphones
• Smart Homes
• Security and Surveillance
• Smart Cars
• E-commerce
• Education
• Health care
• Cyber-security
• Social Media
• Marketing

Advantages of AI

• Creates more powerful and useful computers
• Reduces human error
• Increases speed and accuracy
• Automates repetitive jobs
• Improves decision-making processes
• Has daily applications
• Helps in mitigating risks

Disadvantages of AI

• High cost of creation
• Algorithmic complexity
• Loss of human connection
• Few experienced programmers in the field
• Emotionless nature

The Foundations of AI

• Philosophy: Explores the use of formal rules for valid conclusions, the origin of knowledge, and how knowledge leads to action.
• Mathematics: Focuses on formal rules for drawing valid conclusions, computability, and reasoning with uncertain information.
• Economics: Considers decision-making to maximize payoff, strategies when others may not cooperate, and long-term payoff considerations.

Foundations of AI Fields Continue

• Neuroscience: Explores how brains process information
• Psychology: Focuses on how humans and animals think and act
• Computer Engineering: Focuses on how we can build an efficient computer
• Control theory and cybernetics: Studies how artifacts operate under their own control.
• Linguistics: Focuses on how language relates to thought