> For the complete documentation index, see [llms.txt](https://sejkai.gitbook.io/academic/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sejkai.gitbook.io/academic/ncku-artificial-intelligence/introduction.md).

# Introduction

## What is AI ?

AI 從二戰後急速成長，而在 [**1956 年**](https://zh.wikipedia.org/wiki/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%8F%B2) 被正式確立

一些定義將 AI 分為四大類型 :

* Thinking Humanly
* Thinking Rationally
* Acting Humanly
* Acting Rationally

### Acting Humanly : The Turing Test

當一個 Computer 在進行 Turing test

並且詢問者在詢問完一些問題後

不知道他詢問的對象是 Computer 還是 Human

AI 就達成了 **Acting Humanly**

這個電腦需具備 :

* Natural language processing
* Knowledge representation
* Automated reasoning
* Machine learning
* Computer vision
* Robotics

> The study of how to make computers do things at which, at the moment, people are better. (Rich and Knight, 1991)

### Thinking Humanly : Cognitive Modeling

當電腦具備一些類似人類的思考模式時

* Through introspection (反思)
* Through psychological experiments
* Through brain imaging

**Cognitive Science** 結合了 AI 和 Psychology，嘗試建立起精準又可以測試的人腦理論。

> The art of creating machines that perform functions that require intelligence when performed by people (Kurzweil, 1990)

### Thinking Rationally : Law of Thought

當 AI 能夠自己解決任何 logical solvable problem (**Logicist tradition**) 時

有兩大障礙 :

* Logical notation 沒辦法輕鬆表達 informal knowledge
* 解決問題 in principle 和解決問題 in practice 有非常大的差別
  * 簡單的問題都有可能有幾百種 facts 可以讓電腦資源癱瘓

> The study of the computations that make it possible to perceive, reason, and act. (Winston, 1992)

### Acting Rationally : Rational Agent

當沒有預設的最佳解時，AI 依然可以給出最佳解

有兩大優點 :

* More general than "Laws of thought"
  * 因為找到最佳解只是在數種答案中找到其中一種
* More amenable (經得起檢驗)

> AI ...is concerned with intelligent behavior in artifacts. (Nilsson, 1998)

## Foundations of AI

* Philosophy
  * 智慧從哪兒來、智慧怎麼變成動作
* Mathematics
  * 什麼可以被計算、如何從不確定的資訊來推論
* Economics
  * 怎麼做可以得到最大效益
* Neuroscience
  * 大腦怎麼處理資料
* Psychology
  * 人類跟動物是怎麼思考跟動作的
* Computer engineering
  * 怎麼樣建立一個有效率的 computer
* Control theory and cybernetics
  * 如何控制人工智慧
* Linguistics
  * 語言跟思考的關係在哪裡

## History of AI

### The gestation of artificial intelligence (1943–1955)

* McCulloch and Walter Pitts (1943) 推出一種 artificial neuron 可以計算一些 function & logic
* Donald Hebb (1949) 升級並改造了兩個 neurons 的連結
* Marvin Minsky and Dean Edmonds (1950) 建立第一個 neural network
* Alan Turing 在 1947 年就有類似課程

### The birth of artificial intelligence (1956)

* [Dartmouth conference](https://zh.wikipedia.org/wiki/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%8F%B2)
  * John McCarthy, Marvin Minsky, Claude Shannon,  Nathaniel Rochester ...

### Early enthusiasm, great expectations (1952–1969)

* Newell and Simon (1952-1969) 發明 General Problem Solver (GPS)
  * 可能是第一個 "thinking humanly" approach.
* Newell and Simon (1976) 定義 [physical symbol system hypothesis](https://en.wikipedia.org/wiki/Physical_symbol_system)
* Nathaniel Rochester (1952-1969) 在 IBM 建立一些 AI 專案
* Herbert Gelernter (1959) 建立 Geometry Theorem Prover 可以證明一些理論
* Arthur Samuel (1952-) 寫出 checkers
* McCarthy (1958) 定義 high-level language Lisp，在之後三十年主導 AI 領域
  * Advice Taker (hypothetical program)
* Minsky with microworlds
* McCulloch and Pitts with neural networks

### A dose of reality (1966–1973)

* AI researchers’ overconfidence
* 在 machine translation efforts 上心有餘而力不足
* Scalability: 找到 principle 的 solution 不代表找到方法可以實作
* fundamental limitations on the basic structures

### Knowledge-based systems: The key to power? (1969–1979)

* 之前的方法都是 **Weak methods**
* 要突破 weak methods 必須要有更多專業知識進到 AI 領域
* DENDRAL program (Buchanan et al., 1969) 可以解決 inferring molecular structure
* Feigenbaum and others at Stanford developed MYCIN to diagnose blood infections
* domain knowledge 的重視也在 natural language 這塊出現

### AI becomes an industry (1980–present)

* expert systems, vision systems, robots, and software and hardware specialized for these purposes
* AI Winter

### The return of neural networks (1986–present)

* mid-1980s 一些科學家重新設計 Bryson and Ho (1969) 的 back-propagation learning algorithm
* 造就新的 connectionist models 出現，他們被視為是 symbolic models & logicist approach 的兢爭對手

### AI adopts the scientific method (1987–present)

* AI 理論大致上不會再被改變，並且開始出現一些 real-world applications
* Speech recognition
* Machine translation
* Data mining
* Probabilistic reasoning

### The emergence of intelligent agents (1995–present)

* Whole agent problem 重新出現
* search engines, recommender systems, and website aggregators
* AI 與更多領域結合在一起

### The availability of very large data sets (2001–present)

* 以前的 computer science 重視 algorithm
* 而新的說法認為 data 更為重要一些
* 要表達各種 Knowledge 的方法比起 hand-coded，使用 learning methods on big data 可能更加有效

## The State of the Art

* Robotic vehicles
* Speech recognition
* Autonomous planning and scheduling
* Game playing
* Spam fighting
* Logistics planning
* Robotics
* Machine translation
* …