1. Brief History
1.1. 1940's- interest in neurons, neural networks and their relationship to mathematics and learning.
1.2. 1950- Turing's paper
1.3. 1956- Dartmouth conference
1.4. 1950's and 1960's- enthusiasm and optimism
1.5. Late 1960's and 1970's- realization that further progress was really hard
1.6. 1980's- expert systems, neural networks
1.7. 1990's to present- intelligent agents
1.8. 2000's- robot pets, self-driving cars
2. Machine Learning
2.1. Allow machines to learn from its experience and available data without programming explicitly
2.2. Deep Learning
2.2.1. subfield of ML where artificial neural networks, algorithms inspired by human brain
2.2.1.1. requires large amounts of labeled data like driverless car
2.2.1.2. requires substantial computing power
2.3. Goal
2.3.1. a self-learning machine
2.4. Data input
2.4.1. utilizes data sets to acquire knowledge and experience
2.5. Applications
2.5.1. enabled system provides insight to its user
2.5.1.1. Share Market Prediction
2.5.1.2. Healthcare
2.5.1.3. Fraud Detection
2.5.1.4. Manufacturing
3. Robotics
3.1. the study of creating intelligent and efficient robots
3.2. Aspects
3.2.1. mechanical construction
3.2.1.1. form or shape designed to accomplish a particular
3.2.2. electrical components
3.2.2.1. power and control the machinery
3.2.3. computer program
3.2.3.1. determines what, when and how a robot does something
3.3. Inputs iin analog signal in the form of speech waveform or images
3.4. need special hardware with sensors and effectors
4. Aspects
4.1. scientific (nature of intelligence)
4.1.1. Sensing- computer vision, speech recognition, language understanding
4.1.2. Thinking- knowledge representation, problem solving/planning, learning
4.1.3. Acting- robotics, speech and language synthesis
4.2. engineering (design and production of intelligent agents)
4.2.1. can operate autonomously in complex environments
4.2.2. can be organic, robotic or pure software
4.2.3. not all are intelligent
5. System that think or act like human or rationally
5.1. Thinking
5.1.1. Humanly- cognitive modeling
5.1.2. Rationally- use of logic
5.2. Acting
5.2.1. Humanly- Turing Test Approach
5.2.2. Rationally- study of rational agents
6. Expert Systems (The Machine Performance)
6.1. an application using AI
6.2. Major components
6.2.1. Inference engine
6.2.1.1. contains rules to solve a specific problem
6.2.1.2. helpful for formulating conclusions
6.2.1.3. strategies
6.2.1.3.1. Forward and backward chaining
6.2.2. Knowledge base
6.2.2.1. repository of facts
6.2.3. User interface
6.2.3.1. medium of interaction between users
6.2.4. Knowledge acquisition module
6.2.4.1. knowledge engineer acquires exact information
6.3. Applications
6.3.1. provide expert advice and guidance for various activities