IAI +001

AI

AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

• As a research field, AI aims to develop techniques including algorithms, methods and models to enable systems to perform tasks which require intelligence when performed by humans.
• AI is concerned with developing machines or computer agents that are capable of performing tasks that typically require human intelligence.
• AI is about the study and construction of agent programs that perform well in a given environment, for a given agent architecture.
• AI is a true universal field.
• The term "Artificial Intelligence" was coined by John McCarthy for the Dartmouth Summer Research Project in 1956, marking the formal beginning of AI as a research field.

History

• 1943-1956: Inception of AI
• 1966-1973: A dose of reality
• 1969-1986: Expert systems
• 2011-present: Deep learning

Turing reward winners

• Defining the foundation of the field based on representation and reasoning
  • 1969 Marvin Minsky
  • 1971 John McCarthy
• Making fundamental contributions to AI and human cognition
  • 1975 Allen Newell & Herbert Simon
• Developing expert systems that encode human knowledge to solve real-world problems
  • Ed Feigenbaum & Raj Reddy
• Honored for "probably approximately correct learning (PAC learning)", a foundational theoretical framework for AI and ML
  • 2010 Leslie Valiant
• Developing probabilistic reasoning techniques that deal with uncertainty in a principled manner
  • 2011 Judea Pearl
• Making "deep learning" a critical part of modern computing.
  • 2018 Yoshua Bengio & Geoffrey Hinton & Yann LeCun
• Recognized for lifetime contributions to reinforcement learning, a core method in modern AI
  • 2024 Andrew Barto & Richard Sutton

Nobel Prize in Physics

• For foundational discoveries and inventions that enable machine learning with artificial neural networks
  • 2024 John J. Hopfield & Geoffrey Hinton

Dimensions of AI

Action (Behavior) vs Thinking (Thought)

AI Tree

PEAS

• Performance: The performance measure that defines the criterion of success
• Environment: The agent’s prior knowledge of the environment
• Actuators: The actions that the agent can perform through actuators
• Sensors: The agent’s percept sequence to date through sensors

Agent Type	Performance Measure	Environment	Actuators	Sensors
Taxi driver	Safe, fast, comfortable transportation	Roads, traffic, passengers	Steering wheel, accelerator, brakes	Cameras, GPS, speedometer
Medical diagnosis system	Healthy patient, reduced costs	Patient, hospital, staff	Display of questions, tests, diagnosis, treatments	Touchscreen/voice entry of symptoms and findings
Satellite image analysis system	Correct categorization of objects, terrain	Orbiting satellite, downlink weather	Display of scene categorization	High-resolution digital camera

Rational Agent

Agent = architecture + program

• Agent function: abstract mathematical description
• Agent program: concrete implementation, running within some physical system
• Agent architecture: running on some sort of computing device with physical sensors and actuators

Weak vs Strong AI

• Weak AI: treats a computer as a device that can simulate the performance of a brain
• Strong AI: a theoretical form of machine intelligence that is equal to human intelligence.

Why AI becoming so hot

• Big data
• Cloud computing
• Deep learning

Risks

• Lethal autonomous weapons
• Surveillance and persuasion
• Biased decision making