Read the summary and the most important questions on Artificial Intelligence

• Lecture 1 – Overview, Implications, History, Impact, and Potential of AI

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•  Artificial intelligence (AI)

  A branch of computer science. Goal is to construct general-purpose intelligence.
  After time they retreated form original def. to: construct machines that are capable of doing all of the things which, at the present time, people are better. These machines are called Artificial General Intelligences (AGI’s).

• Applications Associated with AI's

  Computer Vision, Converse Fluently in Human Language,  Natural Language Translation, Game Playing, Plan and Reason like Humans, Display: creativity, curiosity, and discover knowledge, Drive a car.

• Converse Fluently in Human Languages

  ASR (Automated Speech Recognition) – converting verbal speech into written text TTS (Text to Speech Generation) – converting written text into verbal speech
  NLU (Natural Language Understanding) – “understanding” the topic of conversation. NLU is a very hard problem. Limited progress to date. Micro-worlds

• Natural Language Translation

  Translate between different human languages, Written and spoken, Real-time and off-line.

• The two general approaches to solving the problems associated with AI

  Symbolic AI &
  Machine Learning:  Bioligically Inspired Machine Learning (Neural Networks). & Mathematical and Statistical inspired machine learning (Hidden Markov Models).

• states that physical symbol systems are capable of generating intelligent behavior.

   physical symbol system hypothesis

•   is a collection of physical patterns (such as written characters or magnetic charges), called symbols, together with a set of processes (or rules) for manipulating those symbols (e.g., creating, modifying, deleting, and reordering them).

  physical symbol system

• Lecture 2 – Overview, Implications, History, Impact, and Potential of AI (continued)

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• Five Stages of AI in History

  Early Optimism (mid 1950's-mid 60's), The AI Winter, Optimism Turned (1970's), Era of Expert Systems (1980's), Era of Incrementalism (1990's-Early 2000's), and Renewed Optimism (mid 2000's).

• Some Small Domains are Valuable

  Just because a problem domain had to be small didn’t mean the problem to be solved couldn’t be a “high value” problem. Led to the rise of expert systems.

• Era of Expert Systems

  1980's Focus on ‘practical’ problems. Very little forward progress on the “big problems”. Vision, Speech Recognition. Most AI researchers had pretty much “given up” on making near term substantial progress in AGI.