人工智能粵拼jan4 gung1 zi3 nang4英文artificial intelligence,簡稱「AI」),又有叫機械智能machine intelligence),泛指由機械展示嘅智能,相對於同第啲動物展示嘅自然智能(natural intelligence)。喺科學上,「人工智能」一詞亦都俾人攞嚟指專門研究人工智能嘅認知科學電腦科學交界領域[1],呢個領域上嘅研究會關注嘅問題包括點樣教機械做推理知識表示計劃學習自然語言處理感知以及郁同操控物體等嘅作業[1],而呢啲研究其中一個終極目標就係想造出強人工智能(strong AI)-即係能夠展現出同人無異嘅智能嘅 AI [2]

2018 年喺日內瓦舉行嘅《AI for Good》峰會嗰度所展示嘅機械人蘇菲亞(Sophia);佢內置人工智能,曉用語言同人傾偈。
食鬼遊戲嘅片段;啲鬼由電腦操控,但曉追捕玩家-展現咗簡單嘅近似有智能行為。
一個簡單語義網絡;個網絡抽象化噉表達出幾樣事物之間嘅關係,做到知識表示

人工智能嘅理論基礎建基於智能體(intelligent agent)嘅概念:喺最廣義上,一嚿物體如果有能力感知佢四圍嘅環境,並且運用所得嘅資訊嚟提升自己達到目的機會率嘅話,噉佢就算係一個智能體-包括人在內嘅動物都符合呢個定義。人工智能領域嘅目標就係研究點樣人工噉整一啲智能體出嚟,最常見嘅做法係參考心理學以及神經科學呢啲研究自然智能嘅領域嘅研究,跟住再編寫電腦程式嚟模仿人同第啲動物所展現嘅智能[3][4]

除咗科學,人工智能上嘅研究又引起咗唔少哲學社科上嘅討論:「人可以整機械嚟模擬人類智能」嘅宣言引起咗一連串(到咗廿一世紀初仲有人關注嘅)心靈哲學問題同埋「人工噉創造一個有人類水平智能嘅物體係咪合乎道德」等嘅討論[5];亦都有唔少人覺得人工智能如果唔受控嘅話會對人造成威脅[6],例子可以睇吓廿一世紀初科幻故仔常見嘅人工智能叛變(AI takeover)橋段,或者有人擔心人工智能會搞到有大量嘅人失業[7]

人工智能領域誕生於廿世紀中:喺 1956 年,有班工程師宣稱,因為人類智能可以用科學理論描述(可以睇認知科學),所以用運算機械模擬人類智能係有可能嘅[8],引起學界熱議;而自從嗰陣開始,人工智能領域有過好幾波嘅熱潮[9][10],又試過因為研究失敗等嘅原因搞到有排冇人肯出錢資助(即係所謂嘅人工智能低谷;AI winter),有過幾波唔同嘅技術革新[11][12]。到咗廿一世紀,人工智能經已係一個蓬勃嘅獨立科學領域,有得按照所使用嘅技術或者想達到嘅目的分做多個唔同嘅子領域,而且呢啲子領域好多時仲要專化到彼此之間溝通唔到[13][14]

基本理論概念

睇埋:認知科學電腦科學同埋統計學

智能體

內文: 智能體

智能體(intelligent agent,IA)係人工智能領域上嘅基本概念。人工智能嘅目標係人工噉創造出智能體,而喺最抽象化嘅層面嚟講,一個智能體可以想像成一嚿具有以下部份嘅物體:

  • 感知外界嘅能力,可以睇吓感應器(sensor),例:耳仔等嘅感官
  • 某啲按「if... then...」式條件做決策嘅法則,可以睇吓條件陳述式(conditional statement),例:「如果睇到有嘢食,就行埋去」;
  • 感知到嘅外界狀態同智能體嘅內部運作法則(睇埋下面知識表示)會主宰個智能體會採取乜嘢行動(action),而
  • 執行器(actuator)會負責實際採取行動,例:肌肉會做出適當嘅動作,等隻動物行埋嘢食嗰度;
  • 行動會改變環境(environment)嘅狀態,而環境上嘅改變會由感應器感知到。

如是者,智能體就會一路同環境互動,一路嘗試達到自己嘅目的。唔同智能體喺複雜度上可以有好大差異,而一個智能體嘅複雜度同佢嘅智能(intelligence)息息相關[3][4]。智能體同環境之間互動形成嘅系統可以畫成以下噉嘅抽象圖解:

呢個圖入面嘅每件物件可以用數值表示。例如環境嘅狀態可以想像成一個有若干個維度向量(vector),例:[2.5, 3.9, 1.0,...],個向量當中每一個數值表示某個描述世界狀態嘅數值,例如個環境係一間房,第一個數字表示間房嘅平均溫度、第二個數字表示間房嘅亮度... 等等;於是部電腦就可以透過處理 [1.2, 4.3, 6.4,...][1.1, 4.3, 6.4,...] 等嘅陣列知道環境嘅狀態同埋由環境狀態當中計出要採取嘅行動[3]

一個智能體會有某啲目的(goal):一個智能體嘅目的可以想像成一個「個智能體想達到嘅狀態」,可以表達成一個特定嘅環境狀態,而「現時狀態同目的之間嘅距離」可以諗做代表現時狀態以及目的狀態嗰兩個向量之間嘅歐幾里得距離;另一方面,目的又可以用函數嚟表達,即係用數字話俾個智能體知佢幾時做啱幾時做錯[註 1],簡單嘅例子有「if 自己贏咗場象棋,then 輸出係 1(達到目的),else 輸出就係 0(達唔到目的)」[3]

演算法

 
井字過三關
內文: 演算法

人工智能嘅基礎係演算法(algorithm)[15],一個演算法係一段唔含糊嘅指令,能夠教一部運算機械執行某啲作業,由某啲特定嘅輸入(input)嗰度做運算計出相應嘅輸出(output)。一個人造嘅智能體會由一大柞演算法組成。舉個簡單例子說明,好似以下呢段教一部電腦玩井字過三關嘅指令串噉,就係一段演算法[16]

  1. 如果有人有個「威脅」(即係霸咗一行嘅兩個格),噉就霸淨低嗰個格;否則,
  2. 如果有一個招係可以「分叉」並且(為我方)製造兩個「威脅」嘅話,出嗰招;否則,
  3. 如果中間位冇人霸住,霸咗個中間位佢;否則,
  4. 如果對手霸咗個角落頭位,霸咗相反嗰個角落頭位佢;否則,
  5. 如果得嘅話,霸一個冇人霸嘅角落頭位;否則,
  6. 是但霸一個位。

再舉個例說明,如果一個設計者想整一個教部電腦揸車嘅人工智能程式,噉佢就需要有個演算法教部電腦架車快得滯嗰陣要點做、一個演算法教部電腦喺前面係倔頭路嗰陣要點做... 等等。有好多人工智能都曉由數據嗰度學習,識得靠經驗學新嘅啟發法(heuristics;指解決難題嘅捷徑),甚至乎自己寫新嘅演算法。

學習

呢段片顯示一班機械人慢慢噉學識一齊合作推郁物件。
內文: 學習

喺最廣義上,學習(learning)可以定義為指一個智能體吸收知識技能同埋行為嘅過程,包括獲取新知識、新技能、或者新行為,又可以係改變舊有嘅知識、技能、同行為;數學性啲噉睇嘅話,一個智能體可以想像成一個刺激-反應模型(stimulus-response model)[17][18],最簡單嗰條式如下[19]

 

當中

  係個時間點   嘅反應(例如「郁手接波」);
  係一個向量,包括個反應嘅相關刺激(例如「見到個波向自己飛緊埋嚟」)嘅數值;

個模型講嘅係「一個智能體嘅反應係佢感知到嘅刺激嘅函數 )」,即係「刺激決定反應」,而學習就可以想像成   呢個函數改變嘅過程-個智能體做咗一次反應,然後   改變,喺下次再感知到個刺激嗰陣反應就可能會唔同咗。機械學習係人工智能嘅一個重要子領域,指教人造智能體學習嘅研究[20][21]

奥坎剃刀

內文: 奥坎剃刀

奥坎剃刀(Occam's razor)係機械學習上嘅一個重要概念。奥坎剃刀意思係「假設第啲因素不變,一個學習者會偏好比較簡單啲嘅理論假說,除非比較複雜啲嗰個模型(例如)解釋同預測現實嘅能力勁好多」。當一個人工智能(通常因為設計得唔好)喺學習嗰陣為咗要令自己心目中嗰個「描述世界點運作」嘅數學模型完美符合過去數據,而選擇一個太複雜嘅模型,個人工智能就有過適(overfitting)嘅問題:雖然話過度複雜嘅模型解釋過去數據嘅能力比較勁,但統計學上嘅研究表明,呢啲模型解釋將來數據嘅能力通常會渣啲。為咗防止過適,設計人工智能嘅人好多時都會想鼓勵個程式學一啲能夠充分解釋數據得嚟又唔係太複雜嘅模型[22]

例如想像下圖:

家陣有一個智能體,佢要學習兩個變數(圖中嘅 X 軸Y 軸)之間成乜嘢關係,等自己將來能夠由 X 嘅數值預測 Y 嘅數值;佢要嘅係,嘗試搵一條有返咁上下合乎過去數據(每個黑點係一個個案)嘅,用條線做佢心目「兩個變數之間成乜嘢關係」嘅模型;藍色嗰條線有過適嘅問題-藍色線完美噉符合過去嘅數據,但藍色線條式會複雜過直線嘅好多,而實證研究顯示,通常呢啲咁複雜嘅線解釋將來數據嘅能力(以「將來用條線做預測嗰陣嘅誤差」衡量)會比較渣。

「建立能夠用嚟做預測嘅數學模型」呢一點喺人工智能設計上相當重要:比較複雜嘅智能體會具有「描述世界嘅內部模型」(internal model of the world),意思係個智能體對「世界係點運作」嘅理解,可以想像成一大柞   數值[註 2],而呢點對於個智能體做決策嚟講不可或缺-例:個智能體憑過往數據估計出條線,可以用變數 X 嘅數值預測變數 Y 嘅數值,而呢個模型能夠幫個智能體做「如果我採取咗行動   令變數 X 嘅值變成噉噉噉,環境變數 Y 會變成   嘅機會率會最大化,而我嘅目的係想要環境變數 Y 變成  ,所以我要作出   呢個行動」等嘅判斷[23]

學錯嘢

除此,一個學習緊嘅智能體仲可以有「學錯嘢」嘅問題。舉個例說明,假想而家有個設計者想訓練一個人工智能程式學識分辨嘅圖片,佢搵一大柞啡色嘅馬同黑貓嘅圖片返嚟,再入落去個程式嗰度訓練個程式;喺呢個情況之下,個程式可能會學錯嘢,諗住啡色嘅物體就係「馬」,黑色嘅物體就係「貓」[24][25]。要點樣防止人工智能學錯嘢喺圖形辨識(pattern recognition)-專門研究點樣教機器分辨圖像嘅人工智能子領域-上係一個相當受關注嘅課題[26][27]

想像有一個人,佢從來未見過馬同貓;家陣有個老師攞住呢兩幅相,同佢講「嗱,左手邊嘅係馬,右手邊嘅係貓」,佢好有可能會以為「色水」係一種可以攞嚟分辨馬同貓嘅特性。

複雜性

內文: 複雜性

喺對人工智能嘅學習嘅研究當中,要點樣應付複雜性(complexity)係一個重要嘅課題[28][29]

  • 某啲曉學習嘅人工智能程式係理論上如果有無限咁多嘅數據、時間同記憶嘅話,係會有能力完美噉接近任何嘅函數,包括任何可以準確噉描述成個現實世界嘅函數-用日常用語講,即係話只要有足夠嘅數據、時間同記憶,呢啲人工智能程式就能夠學識預測任何嘢。呢啲程式理論上能夠將可能嘅假說(hypothesis)冚唪唥考慮嗮佢,再將啲假說逐個逐個睇吓同數據吻唔吻合,最後推導嗮成個宇宙嘅知識出嚟。不過,
  • 因為組合性爆發(combinatorial explosion;指「可能性嘅數量」隨問題嘅複雜性而有爆發性嘅增長)嘅問題,「考慮嗮所有可能嘅情況」通常喺實際應用上都係冇可能做到嘅,例如係教個人工智能程式捉棋噉,國際象棋喺兩個棋手都行咗第一步之後個棋盤會有 400 個可能嘅形勢,喺兩個棋手都行咗第二步之後個棋盤會有 197,742 個可能嘅形勢,而喺兩個都行咗第三步之後呢個數字會超過 100 萬(「可能性嘅數量」隨「行咗嘅步嘅數量」增長得好犀利),就算用先進嘅電腦行都要嘥極大量嘅時間先至能夠考慮嗮所有嘅可能性;而圍棋仲複雜,有成 10170 個可能情況[註 3]-部電腦運算能力再勁都唔會喺限定時間之內計得嗮。
 
一盤國際象棋

因為組合性爆發嘅問題,人工智能研究當中有好多都集中於思考點樣喺有限嘅數據同時間之內盡可能令人工智能程式學最多嘅嘢,其中一個方向係思考點由「所有可能嘅情況」當中揀一小部份最有可能啱用嘅情況出嚟考慮[28]。舉個例說明,假想而家有一架內置咗人工智能程式嘅自駕車,佢主人叫佢搵由香港廣州嘅最短駕駛路線,喺絕大多數情況之下,個人工智能程式喺做運算嗰陣都大可以安心噉略過(例如)嗰啲由香港經哈爾濱去廣州嘅駕駛路線(因為呢啲路線基本上冇可能會係最短路線)-於是個程式唔使嘥精神時間去考慮呢啲路線,可以喺可能嘅路線當中淨係揀一小部份出嚟考慮[30];又例如因為喺 2016 年打低咗九段(即係最高等級)圍棋棋手李世石而出名嘅人工智能程式 AlphaGo 噉,就用咗蒙地卡羅樹搜索(Monte Carlo Tree Search)嘅做法,噉講意思係指,foreach 棋步,AlphaGo 會用一啲特殊演算法估計邊一啲可能棋步嘅後果最有需要睇,再做若干次嘅模擬,想像每一個呢啲可能棋步行完之後自己嘅贏面會點變,然後就按模擬結果揀要行邊步[31]

重要問題

 
數獨係一個典型嘅智力遊戲;一個數獨難題可以用推理嚟解,但現實世界嘅問題好少可係有得齋靠推理嚟解嘅。
睇埋:認知

人工智能研究其中一個分法係按「想解啲乜嘢問題」:原則上,人工智能嘅終極目標係要創造出能夠令電腦同第啲機械展示出智能;最大嗰個問題-模擬自然智能-有得分類做一大柞子問題,包括係學習解難等嘅能力都係先進嘅自然智能嘅必要部件,所以人工智能領域就要將呢啲能力逐個逐個噉創造出嚟。以下係廿一世紀初人工智能領域當中多人關注嘅問題。

推理同解難

睇埋:推理同埋解難

人工智能其中一個最基本嘅課題係點樣教機械解難(problem solving)。早期嘅研究興集中於整一啲演算法嚟模仿人類解智力遊戲嗰陣用嘅逐步演繹推理(deductive reasoning)[32],呢啲演算法通常會涉及一大柞嘅「如果... 就做...」(if... then...)指令;但呢種做法有個問題-因為組合性爆發等嘅問題,實際應用上要處理嘅問題好多時都閒閒地有成幾千幾萬個可能性要考慮,冇可能吓吓都靠設計者講明俾部電腦聽要做乜[28]。所以到咗 1980 年代晚期同 1990 年代,人工智能研究開始運用嚟自概率論經濟學等領域嘅概念,並且發展出用嚟應付不確定性(uncertainty)嘅方法[33][34]。事實係,認知科學領域嘅研究顯示咗,人腦好少可會真係用逐步嘅推理嚟解決問題嘅[35]

舉個例說明,數獨呢個智力遊戲可以用相對簡單、而且具有決定性(deterministic)嘅「如果... 成立,就做...」法則嚟搵到答案,而第啲智力遊戲都可以用類似嘅演繹推理方法解決。但人類喺日常生活當中解難嗰陣唔會點用呢種推理方式,好似係「要買啲乜嘢禮物氹女朋友」呢個難題噉,會涉及(例如)估吓個女朋友想要乜,呢類問題好難齋靠「如果... 成立,就做...」嘅明文邏輯嚟解決[36]

機械感知

內文: 機械感知

機械感知(machine perception)旨在教一部機械由感應器(包括鏡頭、咪高峰同雷達呀噉)嗰度感應外界嘅資訊並且了解佢四圍嘅環境-而唔係吓吓都靠設計員話俾佢聽[37]:生物型嘅智能體-人同第啲動物-冚唪唥都曉自己用等嘅感官嚟接收有關佢哋周圍環境嘅資訊同埋處理分析呢啲資訊,所以如果人工智能要做到好似人噉嘅智能,就一定要識做同樣嘅嘢。好似係電腦視覺(computer vision)噉,就係指教人工智能處理視覺資訊嘅領域[38],個設計者可以(例如)寫一個會由部電腦嘅鏡頭攞數據嘅程式,而且個程式內置一個之前事先訓練咗,曉(例如)辨認圖入面邊忽係人面乜嘢唔係-跟住佢就會有一個能夠靠部電腦個鏡頭知道自己面前有冇人面嘅程式。除此之外,機械感知呢樣嘢仲可以攞嚟做語音辨識[39]、認人樣同認物件[40]

 
邊緣檢測(edge detection)演算法曉搵出一幅圖像當中邊一忽係物件嘅邊緣-簡單講就係亮度數值突然改變嘅位置。
左:眼珠;右:網絡攝影機;呢兩樣嘢都內置會對有反應嘅結構,而反應嘅特性係光嘅特性嘅函數,所以兩者都能夠做到「感應光」嘅效果-廣義上可以算係感應光嘅感應器

機械學習

內文: 機械學習

機械學習(machine learning)專門研究能夠令人工智能程式隨經驗自動改善自己嘅演算法[33][41]。喺理想嘅情況下,能夠學習嘅程式會按自己所經歷嘅嘢改變佢內部嘅參數(parameter),等自己下次做嘢嗰陣能夠更加成功。舉個簡單例子說明:想像而家有架內置人工智能程式嘅自駕車,佢個程式嘅設定係佢會喺同前面架車距離 2 米或者以下嗰陣先耷逼力,呢個「2 米」嘅數值就係個程式內部嘅一個參數;而跟住有一次架自駕車喺離前面架車 3 米嗰陣,前面架車突然間耷逼力,架自駕車差少少撞埋去,一個識學習嘅程式就應該要考慮吓係咪要根據呢個經驗將個參數變做「4 米」或者「5 米」,以求降低日後撞車嘅機會率[42][43]。佢內部嘅源碼應該會包含類似以下噉嘅內容:

float brake_distance; // 有個變數表示「要喺邊個距離耷逼力」。

if distance_from_front_car <= brake_distance {
    brake; // 如果離前面架車近得滯,就耷逼力;假設個程式經已有方法知道離前面架車有幾近。
}
... // 而跟住要有某啲演算法界定乜嘢為止「差少少撞車」,而 if「差少少撞車」呢個情況發生,噉個 brake_distance 嘅數值要永久提升,同埋提升幾多等等。

機械學習有得分做監督式學習(supervised learning)同非監督式學習(unsupervised learning)兩大種:前者指個設計者會特登俾一啲數據個程式睇,同埋明文噉話俾佢知乜嘢為止啱嘅答案乜嘢為止錯嘅答案[43];而後者指個設計者唔會噉樣做[44],例如係教人工智能程式將啲嘢分類噉,用監督式學習定非監督式學習都有可能做得到:用監督式學習嘅話,個設計員一般會先攞一大柞樣本返嚟,並且逐個逐個樣本將個樣本嘅類別列明,再俾個程式睇啲數據同教佢要睇邊啲部份嚟分,順利嘅話,個程式會慢慢變到識得將未來撞到嘅樣本分類;而用非監督式學習嘅例子就有聚類分析(cluster analysis)等[45]

知識表示

內文: 知識表示

知識表示(knowledge representation)係古典人工智能研究上重要嘅一環,研究點樣教一個人工智能程式組織手上嘅知識,並且用呢啲知識解決一啲複雜嘅作業。舉個例說明,家吓有個設計者想寫個人工智能程式嚟幫手做地理學助教,佢想個程式曉解答一啲簡單嘅地理問題,想個程式了解北美洲喺 2020 年有邊幾多個國家,於是佢喺寫個程式嗰陣,可以用以下呢段[46][47]

NorthAmericanCountries = ("The United States", "Canada", "Mexico")

呢段源碼教個程式話「北美洲國家」呢個類別包括咗「美國」、「加拿大」同「墨西哥」三個內容。當有個學生問個程式「北美洲喺 2020 年有邊幾個國家」嗰陣,個人工智能程式會耖嗮佢手上有嘅資訊,搵出「北美洲國家」呢個類別包含嘅內容,再將嗰三個名俾出嚟做輸出。同一道理,呢種做法可以用嚟教任何人工智能程式將啲嘢-例如係將動物或者語言-分類。除咗呢種分類性嘅手法,知識表示研究仲有好多方法教人工智能程式組織自己手頭上嘅知識[48][49]

上述呢個等級式嘅知識組織方法有唔少局限:首先,呢種做法假設咗類別之間有遞移性(transitivity),噉講即係話,佢假設咗「如果(分類上)A 屬於 B 而 B 屬於 C,噉 A 屬於 C」,但研究顯示,人嘅心靈所用嘅認知機制唔係噉嘅,例如「」會屬「傢俬」呢個類別,「一張爛爛地嘅凳」呢個物體屬「凳」,但喺現實好難說服人喺答「傢俬呢個類別包含啲乜」嗰陣答「一張爛爛地嘅凳」-所以喺最少一方面,純等級式嘅知識組織法唔似人類智能[50];除此之外,如果要一個人工智能程式做到人做到嘅智能,齋靠分類係唔夠嘅-除咗將物件分類,人仲會識得描述物件嘅特性(「美國有 200 年歷史左右,國旗有紅、藍、同白三隻色...」)同物件彼此之間嘅關係(「美國同加拿大友好,同俄羅斯唔友好...」)[51][52][53]

本體

 
一個本體表達概念之間嘅關係(英文)。
內文: 本體 (訊息科學)

現時人工智能學界一種常見嘅知識表示做法係本體(ontology)[54]:一個本體會好似幅附圖噉,包含一大柞概念(幅附圖包括咗哺乳動物鯨魚等等),指明嗮每一對概念之間嘅關係-鯨魚「屬於」哺乳動物、鯨魚「住喺」水入面、哺乳動物「屬於」動物... 等等。本體式嘅知識表示法喺好多實用領域上都有用,例如係喺臨床醫學上幫手做決定[55]、知識發現[56]同埋好多其他方面[57]。而且廿一世紀初嘅人工智能界仲有網絡本體語言(Web Ontology Language)呢款程式語言專門攞嚟整本體[58]

以下呢段係一段用網絡本體語言表達嘅一個本體,用嚟描述一啲有關意大利薄餅嘅知識[54]

Namespace(p = <http://example.com/pizzas.owl#>)
Ontology( <http://example.com/pizzas.owl#>
   Class(p:Pizza partial
     restriction(p:hasBase someValuesFrom(p:PizzaBase)))
   DisjointClasses(p:Pizza p:PizzaBase)
   Class(p:NonVegetarianPizza complete
     intersectionOf(p:Pizza complementOf(p:VegetarianPizza)))
   ObjectProperty(p:isIngredientOf Transitive
     inverseOf(p:hasIngredient))
)

呢段碼 ObjectProperty 嗰行會同個程式指明個本體入面某啲物件嘅特性,呢行指令講咗幾樣嘢:首先,「係某某嘅原料」(isIngredientOf)呢個關係係有遞移性嘅(Transitive)-即係教部電腦,如果 A 係 B 嘅原料而 B 係 C 嘅原料,噉佢可以推斷 A 係 C 嘅原料;呢行指令仲表明咗「係某某嘅原料」係「原料包含咗」(hasIngredient)嘅反轉(inverse)-所以運用呢個本體嘅人工智能程式可以做(例如)「夏威夷薄餅原料包含咗菠蘿,所以菠蘿係夏威夷薄餅嘅原料」嘅推理[54]

內隱知識

睇埋:內隱知識

要點樣令人工智能展示內隱知識(tacit knowledge)係另一個受注目嘅課題。內隠知識係指嗰啲難以言喻嘅知識,好似係一個象棋大師可能會直覺噉覺得某一步「太危險」所以唔行,但如果問返佢,佢唔會講得出點解佢覺得嗰步太危險[59]認知心理學嘅研究顯示,人有能力用直覺(intuition)做判斷,喺呢啲情況之下,做判斷嘅人唔識用言語形容佢嘅思考過程,但實驗結果係,好多時用直覺做判斷估中嘅機會率大過純粹隨機斷估。噉即係話喺人用直覺做判斷嘅過程當中個腦實係處理咗啲用言語形容唔到嘅知識-呢啲知識就係所謂嘅內隱知識[60]。內隱知識對人嘅日常生活好緊要,因為人冇可能吓吓做決定(例如決定行路嗰陣邊隻腳踩出去先)都要有意識噉諗過度過先做。所以要做出好似人噉嘅人工智能,噉就實要能夠令人工智能具有好似內隱知識噉嘅行為[61]

自動計劃

內文: 自動計劃

一個有返咁上下智能嘅智能體一定要識得幫自己設目的並且嘗試達到呢啲目的[62]。要做到呢樣嘢,一個人工智能就要有能力想像未來-用某啲方式(好似係電腦數據)嚟向自己表達周圍環境嘅狀態以及預測自己同第啲智能體嘅行動會點樣改變周圍環境嘅狀態,仲要曉計每種可能狀態對佢自身嘅效益(utility;簡單啲講就係有幾能夠幫到佢達到目的)以及按佢嘅運算結果做決策[63][64]。古典嘅自動計劃研究會用一個理想化(唔多現實)嘅模型:將做緊計劃嗰個智能體想像成好似係世上唯一一個做緊計劃嘅系統噉,喺呢種模型入面,個智能體可以完美噉預測佢嘅行動嘅結果[65]。但現實存在嘅智能體係唔會噉嘅,無論人定人工智能都好,佢哋喺計劃嗰陣梗係要受制於身邊其他智能體嘅行動,所以實會有不確定性。噉即係表示,一個曉好似人噉計劃嘅人工智能實要識得處理不確定性以及按自己行動嘅結果評估自己嘅進度[66]

喺 2000 年代中,電子遊戲業界出現咗好似噉嘅自動計劃演算法,用嚟控制一隻遊戲嘅 NPC[67]

 Start # 初始化
   個 AI 內部有若干個目的(goal)同若干個行動(action);
 
 Foreach 時間點
   計每個目的有幾高 priority;
   按現時世界嘅狀態等資訊,搵出可能行動之中邊個最能夠幫手達到目的;
   模擬吓個行動係咪真係能夠幫手達到目的;
   採取行動。

自然語言處理

 
語言學上有所謂嘅形式文法嚟分析字嘅詞性,可以攞嚟幫手做 NLP。
內文: 自然語言處理

自然語言處理(natural language processing,NLP)係指研究教人工智能理解人類語言嘅領域[68]。一個夠勁嘅自然語言處理程式會令人類可以就噉用把口講或者用筆寫嚟同機械溝通,唔使用(好多時都唔係咁易用)嘅程式語言,而且仲可以攞嚟由人寫嘅書以及網頁等嘅來源提取有用嘅資訊[69]或者做機械翻譯(machine translation)等嘅作業[70]

自然語言處理好常用。拼寫檢查(spell checking)就係一個比較簡單嘅例子,好似 Microsoft Word 同埋 Google 搜尋器都有用到。拼寫檢查會做嘅嘢係檢查吓一段用字母寫嘅字有冇串錯[71],拼寫檢查嘅一種可能步驟係噉嘅[72]

  1. 成立一個字庫,內有「邊啲字母串係真嘅字」同埋「每個字有幾常出現」等等嘅資訊;
  2. 當檢查緊嗰份文件當中包含一個字庫冇嘅字嗰陣,顯示一條紅線喺個字下面;
  3. 如果個用家想嘅話,由個字庫當中揀返個字提議嚟代替紅線咗嗰個字-由個字庫嗰度揀出一個同紅線咗嗰個字最相近嘅字,如果有多過一個字係同嗰個字最相近嘅,揀佢哋當中最常見嗰個。

2010 年代嘅自然語言處理程式好多時會用多種策略,能夠喺頁數或者段數嘅層次嗰度有可接受嘅準確度,但依然缺乏理解文章內容嘅能力,仲未曉將獨立嘅句子分類,而且呢啲程式通常都因為時間成本問題而冇辦法攞嚟喺商業上應用[73]

強人工智能

內文: 強人工智能人工智能完全

強人工智能(strong artificial intelligence)可以話係人工智能領域嘅終極目標:強人工智能係指能夠展現出所有自然智能有嘅特性嘅人工智能[74],會完美噉通過圖靈測試(Turing test;睇下面);廿世紀嘅學界作出咗多次創造強人工智能嘅嘗試,但次次都衰收尾,遠遠噉低估咗呢個作業嘅難度,而到咗廿一世紀,一個典型嘅人工智能專家多數都會集中於解決一至兩個問題,而唔會大想頭到諗住創造出能夠好似人類噉普遍解決問題嘅人工智能程式[75][76]。有好多人工智能專家都相信,呢啲淨係曉解決一至兩個問題嘅人工智能程式終有一日會俾人砌埋一齊做一個強人工智能[77][78]

廿一世紀初嘅人工智能領域有「AI 欠缺常識」嘅問題[79]:同廿一世紀初嘅人工智能比起上嚟,人好擅長喺冇受訓嘅情況之下對物理或者心理現象做判斷,例如就算係一個好細個嘅細路都識做「如果我將支筆碌過張枱嘅表面,支筆最係會跌落地」噉嘅推論;又例如人能夠易如反掌噉理解「市議員拒絕俾啲請願者攞允許,因為佢哋主張用暴力」噉嘅句子,但一個廿一世紀初嘅人工智能程式好多時會唔明呢句嘢係話市議員主張暴力定係請願者主張暴力[80]。廿一世紀初嘅人工智能喺常識上嘅缺乏表示咗佢哋成日都會犯一啲人唔會犯嘅錯,而且犯錯嘅方式對人嚟講好匪夷所思,例如 AlphaGo 可以喺圍棋比賽嗰度鍊贏人類嘅圍棋國際冠軍,而 Deepmind 仲喺 2010 年代成功發展出曉玩唔同 Atari 2600 遊戲嘅人工智能[77][81],不過呢啲程式答唔到好似「點樣知一杯牛奶滿唔滿」呢啲(對人嚟講)簡單得好交關嘅問題[82][83]

第啲問題

  • 機械人學(robotics)-一個人工智能程式嘅輸出可以用一大柞數值表達物件嘅位置同方向以及係「每個關節要拗彎幾多角度」等嘅資訊,呢啲資訊可以用嚟控制機械手臂,而事實係,機械手臂喺廿一世紀初嘅工廠嗰度好常見[84][85]
  • 人工情感智能(affective computing)-人工智能程式俾出嘅數字性輸出可以用嚟代表「表情」嘅資訊(例如一個笑容會涉及嘴角向上掦若干角度)[86][87],所以有科學家研究點樣用程式令到電腦曉做俾表情等嘅社交動作,即係令到人工智能出現類似人類嘅情緒同埋能夠同人類進行社交[88][89]

... 等等。

 
一隻機械手臂

運算方法

睇埋:模控學同埋運算神經科學

一個人工智能程式要展示自己嘅智能,佢實要攞一啲輸入(input),用佢內部嘅演算法做一啲運算(computation),再俾返個輸出(output)出嚟,而佢嘅輸出會決定佢表現好唔好。舉個例說明,喺一個人工智能程式捉象棋嗰陣,個程式會係噉收到「個棋盤係乜嘢形勢」嘅資訊(輸入),再做一啲運算,決定自己要行邊一步(輸出),而佢所行嘅步最後就會決定佢贏定輸(表現好唔好),喺呢個過程當中,一個人工智能程式由輸入值計輸出值,即係話輸出值係輸入值嘅函數 -當中函數   可以有好多唔同款[90][91]

 
電腦科學上常用嘅基本輸入輸出模型

人工智能史上出名嘅運算方法(  可能嘅款)有以下呢啲[92][93][94]

符號人工智能

內文: 符號人工智能

符號人工智能(symbolic AI),又有叫老派人工智能(good old-fashioned AI,簡稱「GOFAI」)[95],係最早期(同埋俾好多人認為係最易明)嘅人工智能運算方法:喺 1950 年代人工智能領域啱啱起步嗰陣,啲科學家好多都認為人類智能只不過係對邏輯符號嘅玩弄[96],而符號人工智能做法就將所受嘅輸入數值用一大柞邏輯符號計算,再按照呢啲計算俾個輸出數值出嚟睇;舉個例說明,如果家吓有個設計者想教部電腦幫手睇病(輸入值係「有關病人嘅資訊」,而輸出值係「診斷」),噉就要教佢「if 一個普遍健康嘅大人發燒then 佢有可能係感冒」、「if 一個普遍健康嘅大人發燒,then 佢都有可能係肺炎」... 等嘅若干條法則[97]

即係話符號性人工智能嘅做法建基於三個諗頭[97]

  • 表示一個智能系統嘅模型可以完全明文噉定義(即係忽略咗內隱知識);
  • 呢個模型當中嘅知識可以用邏輯符號表達;同埋
  • 認知作業可以描述為做喺呢啲符號身上嘅形式作業。

符號人工智能呢種做法可以話係比較原始,而且冇幾耐啲科學家就發現,喺好多情況之下都會因為可能性太多,搞到設計者做唔到逐個逐個情況講嗮俾部電腦聽要點做(睇上面有關複雜性嘅討論)[98][99]

啟發法

內文: 啟發法

廿世紀早期嘅學者好多都認為,人嘅認知功能可以用一啲相對簡單嘅啟發法(heuristics)嚟表達[97]:佢哋會將要解決嘅問題想像為一柞「狀態」(state),而解決問題嘅過程就係個人工智能嘗試由初始嗰個狀態變到佢目的狀態嘅過程,佢哋認為人類會用某啲相對簡單-但唔一定啱-嘅認知捷徑嚟判斷點樣由一個狀態移去下一個狀態(例如係好似「如果我將隻棋行得太出,佢通常會俾對手食咗」呢一啲直覺同無意識嘅假設),而呢啲簡單直接、通常啱用嘅規則就係所謂嘅啟發法。採取呢個做法嘅研究者認為,要令人工智能做到好似人噉嘅智能,要做嘅就係用認知科學領域嘅實驗搵出人思考嗰陣用啲乜嘢啟發法,並且開發出一啲模擬人腦所用嗰啲啟發法嘅演算法,再用呢啲連串嘅演算法嚟模擬人所展示嘅智能。呢種做法一路去到 1980 年代中期都仲有好多人用[100]

 
喺一盤國際象棋之中,行出去嘅棋(同企後排受嚴密保護嘅棋比起嚟)好多時會比較容易俾對手食。所以「如果我想保護隻棋,就要將佢擺喺後排」係一個簡單嘅啟發法。

基於邏輯

睇埋:邏輯編程

又有好多符號人工智能研究者主張,人工智能唔使模擬人嘅認知功能所涉及嘅啟發法,而係應該用一啲實啱(相對於啟發法嘅「通常啱」)嘅邏輯法則嚟指定乜嘢係「正確答案」。例如係如果要教一部電腦點樣知道兩個人係咪彼此嘅兄弟姐妹,運用基於邏輯做法嘅人工智能研究者會教電腦以下呢條法則[97]

siblings(X,Y) :− parent(Z,X) and parent(Z,Y)

呢條法則講嘅係,「如果有一個人 Z,佢係 X 嘅父母,又係 Y 嘅父母,噉 X 同 Y 就係兄弟姐妹嘅關係。」呢類思考嘅方式唔似人(好多時用直覺多過邏輯嘅)日常思路,但就係一條絕對正確同合邏輯嘅思路。相比之下,用認知模擬嘅人工智能研究者會研究吓一般人類用乜嘢(無意識、未必完全穩陣嘅)法則判斷兩個人係兄弟姐妹嘅機會率,再用演算法模擬呢啲法則[101][102]

基於知識

睇埋:專家系統

由 1970 年代開始,電腦嘅記憶量開始愈嚟愈大,令到各門嘅人工智能專家都開始嘗試將「知識」嘅成份加落去人工智能應用嗰度。呢股「知識革命」引起咗專家系統(expert system)嘅發展[103]。專家系統係第一種真係成功嘅人工智能軟件,一個專家系統會建基於一個知識基礎之上,並且運用一大柞「if... then...」嘅法則嚟做推理,例如係一個用嚟診斷病人嘅專家系統噉,佢內部會有一大堆有關唔同嘅病同啲病嘅症狀嘅資訊,而如果俾個病人佢睇嘅話,佢會用一大柞「如果個病人有咳,佢可能係...」同「如果個病人有咳但係又唔係肺炎,噉佢又有可能係...」等嘅法則嘗試搵出一個診斷。但要整一個專家系統就實要有個真嘅人類專家幫手,而吓吓都逐條逐條法則都明文講嗮俾個人工智能程式知會好嘥時間-專家往往都因為有人需要佢哋嘅服務而好唔得閒,好難請佢哋幫手整專家系統[104][105]

問題

符號人工智能最大問題係,呢種做法要求設計者將個人工智能做判斷要用嘅法則冚唪唥都明文(explicitly)噉講嗮俾部電腦知,但由 1980 年代開始,人工智能學界開始留意到,呢種做法喺好多情況之下根本行唔通[95]:首先,佢哋學起嘢上嚟好撈絞-如果個設計者想教一條新法則俾個程式聽,佢就要人手噉由(閒閒地成幾萬行長嘅)源碼當中搵嗮所有同條新法則衝突嘅法則出嚟,再攞走呢啲舊法則,嘥時間得好緊要[106];而且符號人工智能根本唔能夠模擬到自然智能-認知心理學等領域嘅研究清楚噉表明咗,人類智能好多時都會依賴直覺等無意識、難以明文噉講出嚟嘅法則。所以廿一世紀初嘅人工智能好少可會真係齋靠用符號性嘅方法,而好多時啲新嘅人工智能做起運算上嚟都起碼會用啲混合型(hybrid)方法-即係結合符號性同非符號性嘅人工智能[107][108]

統計方法

睇埋:統計學

人工智能要解嘅問題當中有好多都會涉及不確定性(uncertainty)-意思即係話,個程式成日都焗住要喺唔完全知道嗮所需嘅資訊嘅情況之下行事。透過運用概率輸經濟學等領域嘅知識,人工智能專家諗咗一柞工具出嚟去應付呢啲問題[109]

貝葉斯網絡

 
一個簡單嘅貝葉斯網絡
內文: 貝葉斯網絡

貝葉斯網絡(Bayesian network)係一種可以用嚟教電腦做推理[110]、學習[111]同計劃[112]等工作嘅工具。一個貝葉斯網絡會考慮大量變數,並且用一柞基於貝葉斯定理(Bayes' Theorem)嘅方程模擬唔同變數之間嘅關係。舉個簡單例子說明,假想家吓有一個貝葉斯網絡,佢會睇某啲變數(包括咗「有冇落雨」同埋「啲灌溉花灑有冇開著」等)嘅數值,並且計出「啲草係濕嘅」呢個狀態係「真」嘅機會率,途中會用到(例如)以下呢條式[113][114]

 

當中   係指「啲草濕咗」呢個狀態,  係指「啲灌溉花灑著咗」呢個狀態,而   係指「有落雨」呢個狀態。成條式用日常用語講嘅話係噉嘅:「嗰三個狀態都係真嘅機會率」( )等如「如果有落雨( )而且啲灌溉花灑著咗( ),啲草濕咗( )嘅機會率」( )乘以「如果有落雨( ),啲灌溉花灑著咗( )嘅機會率」( )乘以「有落雨嘅機會率」( )。

個設計者寫好程式之後,可以(例如)搵一大柞有關呢幾個變數之間嘅關係嘅數據俾個程式睇,跟住叫個程式用呢啲過往嘅數據,計出變數同變數之間嘅關係係點,而個程式就可以攞嚟預測未來[113]。貝葉斯式人工智能有相當廣泛嘅用途,例如 Xbox Live 噉,喺幫啲打緊網上遊戲嘅玩家搵比賽加入嗰陣,就會用到考慮嗰個玩家嘅贏率嘅貝葉斯網路[115]:一種做法係整個人工神經網絡(睇下面),用每一個玩家喺先前嗰啲比賽嗰度「幾常成功殺敵」同埋「幾常俾敵人殺」等嘅資訊做個網絡嘅輸入,而呢個網絡嘅輸出就係「如果個分隊係噉嘅話,A 隊贏嘅機會率係幾多」,並且寫個演算法搵出令到呢個機會率最接近 50% 嘅分隊法(即係盡可能令場比賽勢均力敵)[115]

問題

同符號性嘅人工智能比起上嚟,好似貝葉斯網路啲噉嘅貝葉斯式人工智能都有唔好處:佢哋內部要計大量嘅機會率,搞到佢哋喺運算上撈絞好多(即係比較「computationally expensive」-要嘥多好多時間同記憶體),好多時為咗慳啲時間同記憶體,啲設計者焗住要簡化佢哋啲模型,例如係如果個設計者用嘅電腦冇返咁上下運算能力,佢可能就冇得用涉及打圈結構嘅貝葉斯網路-即係話模型嘅可能複雜度受制於運算能力上嘅局限,而唔係個模型模擬現實嘅能力[115]

神經網絡

 
一個典型嘅人工神經網絡有三大層:輸入(input)層負責由外界接收訊號,隱藏(hidden)層負責計由輸入層攞到嘅訊號,而輸出(output)層俾嘅數值就會係輸出。
內文: 人工神經網絡

人工神經網絡(artificial neural network)係建基於人腦入面嘅神經細胞(neuron)嘅結構嘅:一個動物嘅腦會由好多神經細胞組成,一個成年男人嘅腦有成差唔多 860 億粒神經細胞,每粒神經細胞會由第啲神經細胞嗰度接收電同化學訊號,而當一粒神經細胞受到啲訊號刺激嗰陣,佢就可能會射自己嘅電同化學訊號,呢個訊號跟手就可能會刺激第啲神經細胞,於是乎訊號就噉喺個網絡嗰度傳開去[116]。人工神經網絡運用嘅係同一道理。喺整人工神經網絡嗰陣,個設計者會設下一大柞人工神經細胞(artificial neuron),每粒神經細胞有一個變數代表佢嘅「活躍程度」,而且有某啲方式(通常係一個矩陣)表示人工神經細胞之間嘅連結。舉個例說明,每粒人工神經細胞嘅活躍程度可以用以下呢條式代表[117][118]

 ;呢個就係所謂嘅啟動函數(activation function)。

當中,  代表粒神經細胞嘅啟動程度,  代表前一層神經細胞當中第   粒嘅啟動程度,而   就係其他神經細胞當中第   粒嘅權重(指嗰粒神經細胞有幾影響到  )。如果個設計者人手噉俾某一啲輸入落去個神經網絡輸入層當中嗰啲神經細胞,噉跟住嗰啲層嘅神經細胞就會受到輸入層嗰啲嘅刺激,並且改變佢哋嘅活躍程度,而最後嗰層嘅神經細胞(輸出層)就會負責表示個輸出。跟住落嚟,一種可能做法係運用反向傳播算法(backpropagation)「訓練」個人工神經網絡[119][120]-個設計者會將個網絡俾嘅輸出同佢想個網絡俾嘅輸出比較,計個誤差值,再用個誤差值嚟計吓應該點樣調較啲神經細胞之間嘅權重。如果一切順利,個網絡就慢慢變到愈嚟愈俾到正確嘅答案[121][122]

神經進化

內文: 神經進化

除咗反向傳播算法之外,廿一世紀初仲有咗所謂嘅神經進化(neuroevolution)方法嚟訓練人工神經網絡[123]:用呢種做法嘅設計者運用進化論當中物競天擇(natural selection)嘅原理;根據物競天擇嘅理論,大自然嘅每一個生物物種嘅內部都有個體差異,而呢啲個體差異令到一個族群嘅個體當中有啲比較擅長生存同繁殖後代,呢啲個體就比較有機會將自己嘅基因傳俾下一代;同一道理,用神經進化整人工智能嘅過程係一開始嗰陣複製一大柞彼此之間相似,但彼此之間(喺權重值等方面)有少少差異嘅神經網絡出嚟,再俾啲神經網絡各自噉做幾次個設計者想佢哋做嗰樣作業,表現最好嗰啲網絡就會俾個設計者複製,生產下一代(似表現好嗰啲網絡)嘅子代網絡,表現唔好嗰啲網絡就會被淘汰-於是乎個設計者手上有嘅神經網絡就會變到愈嚟愈勁[123][124]

問題

有好多神經網絡相關領域嘅學者批評神經網絡係「黑盒」(black box)[125]。人喺用個腦諗完嘢解決完問題之後,曉將自己嘅思考過程口頭報告返出嚟。但一個神經網絡唔會識做呢樣嘢,唔會話到俾研究者聽佢經過啲乜嘢思考過程先能夠達到佢所俾嘅輸出。所以有人將神經網絡比喻做一個黑盒-就算佢嘅輸出係準確嘅都好,個研究者都淨係知個神經網絡嘅輸入同輸出,冇辦法知道當中嘅思考過程。即係話對於「個神經網絡俾嘅答案點解係啱」呢條問題,目前唯一嘅答覆係「因為一部醒過人類嘅系統俾咗個答案出嚟」,雖然呢點唔損害神經網絡喺實用上預測現象嘅能力,但噉表示神經網絡嘅可解釋度(explainability)低得好交關,令好多學者都覺得難以接受[126]。因為噉,有好多科學家都致力研究點樣先可以拆解一個經歷咗訓練、做緊準確預測嘅神經網絡[127]

 
喺科學同工程學上,一個黑盒系統嘅輸入同輸出之間有已知嘅關係,問題係研究者唔知佢內部點樣運作,點樣由輸入產生輸出[128]

應用

內文: 人工智能嘅應用

人工智能對於任何嘅智能性作業嚟講都好有用[129]。比較廣為人知嘅人工智能應用例子包括咗識揸自己嘅交通工具(包括咗同埋無人飛機呀噉)、醫療上嘅診斷、數學定理證明、藝術創作、玩遊戲、搜尋器、認相入面嘅影像以及係網上廣告等等[130][131][132]

電子遊戲

 
一班人喺度玩食鬼;啲鬼由電腦操控,但曉追捕玩家-展現咗簡單嘅智能。
內文: 電子遊戲嘅人工智能

電子遊戲人工智能(video game AI)係指電子遊戲(electronic game)用嘅人工智能:電子遊戲係能夠同玩家互動、以娛樂玩家為目的嘅電腦程式;而電子遊戲入面好多時會涉及由電腦控制,同玩家進行對局嘅角色NPC[133]遊戲開發者為咗想玩家得到樂趣,通常會想呢啲由電腦控制嘅角色有返咁上下聰明,能夠為玩家提供一定嘅挑戰(睇埋心流[134];噉即係話佢哋會想 NPC 展現一定程度嘅智能,而「教電腦程式做出類似有智能噉嘅行為」正正就係 AI 呢個領域嘅重心[135][136]

舉個簡單例子,食鬼入面嘅敵人由電腦控制,一個教電腦控制啲敵人嘅可能演算法如下[137]

Pac-Man.pos
clyde_target = random_tiles // 將 clyde_target 設做隨機一格

while game == in_play: // 當隻遊戲進行嘅每一個時間點,
    case player of:
      Blinky:  move 1 tile toward Pac-Man.pos // 第一隻鬼要向主角位置(Pac-Man.pos)行一步。
      Inky:    move 1 tile toward (Pac-Man.pos + 4) // 第二隻鬼要向主角位置前四格行一步。
      Clyde:   if Clyde.pos == clyde_target: // Clyde 呢隻鬼要向佢嘅目標位置前進,如果到咗目標位置,揀個新嘅目標位置。
                   clyde_target = (clyde_target + 1) % 10 
               else:
                   move 1 tile toward clyde_target

上述嘅會令啲敵人曉追趕主角-有少少似有智能嘅噉[138]

早期-廿世紀中-嘅電子遊戲經已有喺度用相對簡單嘅 AI,而廿一世紀初及後,電子遊戲嘅 AI 仲成為咗遊戲製作上嘅一個大課題。遊戲製作嘅專家會研究用乜嘢演算法整一隻遊戲嘅 AI 先最可以令玩家過癮,而且 AI 仲有俾人運用嚟做控制 NPC 以外嘅工作,例如係做遊戲測試(game testing;喺隻遊戲出街前測試隻遊戲玩起上嚟點)同遊戲分析(game analytics;對玩家嘅行為作出分析)等都有用到 AI 相關嘅技術[115][139]

自駕車

一架自駕車行嘅片
內文: 自駕車

人工智能係自駕車(driverless car)科技當中不可或缺嘅一環。直至 2016 年為止,總共有 30 間主要公司都有喺度用人工智能整自駕車,而「人工智能可以點樣幫助自駕車技術」係人工智能領域當中好受關注嘅一環[140][141][142]

  • 自駕車人工智能其中一個最緊要嘅部份係教架車了解佢周圍環境嘅佈局。一般嚟講,一架自駕車會內置咗佢會行駛嘅地區嘅地圖,會包括咗交通燈同埋行人路嘅位置等嘅訊息,亦都有一啲研究嘗試令到自駕車唔使內置地圖都曉自己按照經驗學識佢周圍環境係乜嘢樣[143]
  • 喺自駕車設計上,「要點樣確保乘客嘅安全」係一個重要嘅議題。一架自駕車嘅程式實會內置一啲教架車點樣處理危險情況嘅演算法,但係好似「當架車實炒、一係撞到路人一係令乘客受傷嗰陣,架車應該揀保護個路人定保護個乘客」呢啲問題都仲係好難搞[144]

... 等等。

醫療應用

人工智能喺醫療上嘅用途好廣泛:

  • 人工智能程式可以幫手評估落藥嗰陣要落幾重:喺醫療上,落藥要落幾多係一條關乎人命嘅問題(例如手術麻醉噉,如果落嘅麻醉藥劑量大得滯會搞出人命),而喺 2016 年,有份喺加州做嘅研究就發現,有一條多得人工智能先搵到嘅數學方程式可以用嚟評定要落幾大劑量嘅免疫抑制劑落去病人身上。喺醫療上,評估劑量不嬲都係一個好嘥時間精神嘅程序,所以呢種人工智能程式幫醫療界慳到唔少錢[145]
  • 人工智能程式可以幫手決定點樣醫癌症:可以用嚟醫癌症嘅藥同疫苗有成多個 800 種,所以對於醫生嚟講,要決定點樣醫一個癌症病人絕非易事。微軟發展咗一個叫「Hanover」嘅人工智能程式,呢個程式曉記住嗮所以同癌症有關嘅研究論文,知道每一種醫療方法喺邊種情況之下最有效,並且用攞到嘅訊息決定某一個特定病人應該用乜嘢方法醫[146][147]。有研究指呢種程式喺診斷癌症嘅表現同真人醫生一樣咁好[147]
  • 又有研究試過用人工智能程式嘗試監察高風險嘅病人,評估佢哋有病嘅風險,而呢啲資訊對於治療同保險等方面都會有用[148]

... 等等。

第啲應用

  • 人工智能甚至仲可以攞嚟整藝術品:人工神經網絡嘅輸入層嘅每粒神經細胞可以設做幅輸入圖像嘅一粒像素[149],而輸出層嘅每一粒神經細胞同一道理可以作為輸出圖像嘅像素。而中間嘅隱藏層做嗰啲運算會令到幅輸出圖像同輸入圖像有少少似,但又唔同。舉個例子說明,有研究者成功噉整出能夠將是但一幅相變做印象派作品嘅人工神經網絡[149]
  • 廿一世紀嘅金融界會用人工智能程式嚟監察買家同賣家嘅活動。呢啲程式喺見到某啲異常活動嗰陣會通報俾銀行等嘅機構聽,等做起探測詐騙等嘅罪行嗰陣容易好多[150]
  • 有啲人工智能程式曉睇一個人上啲乜嘢網站,並且靠呢啲資訊嚟賣幫手賣廣告:例如如果個程式探測到某個用家零舍鍾意搜尋有關打機嘅資訊嘅話,個程式就會俾個用家多啲睇到同打機相關嘅廣告[151]

... 等等。

哲學問題

 
圖靈測試嘅圖解;A 係部受試機械,B 係個對照人類,C 係個評判。評判唔會見到 A 同 B,只能夠通過文字同兩者溝通。
內文: 人工智能哲學

人工智能哲學(philosophy of artificial intelligence)係對人工智能嘅哲學探討:人工智能同哲學-尤其係心靈哲學(philosophy of mind)-相當有關,因為兩個領域都關注智能心靈意識以至自由意志(free will)等嘅概念。再加上人工智能嘅技術理論上可以引致人造心靈嘅出現,而噉做會引起唔少道德上嘅問題,所以道德哲學等嘅領域都對人工智能嘅相關討論有興趣[152]

人工智能哲學上嘅思考會試圖回答以下嘅問題[153]

  • 機械有冇可能展現智能?佢哋有冇可能解決嗮所有人曉透過思考處理嘅問題?
  • 人類智能同人工智能本質上係咪一樣?人腦電腦有乜異同?
  • 機械有冇可能好似人類噉,擁有自己嘅精神、意識同心理狀態?

... 等等。

圖靈測試

內文: 圖靈測試

圖靈測試(Turing test,簡稱「TT」)係人工智能哲學上一個出名嘅議題。圖靈測試係由英國數學家亞倫圖靈(Alan Turing)喺 1950 年諗出嚟嘅一個測試,用嚟檢驗一部機械係咪展現到好似人噉嘅有智能行為。最基本嗰種圖靈測試步驟如下:一次測試會有一個人類負責做評判,跟住又有一個人類同部受試嘅機械,兩者分別噉同個評判講嘢;個評判唔會見得到個人類同個受試者,淨係有得用鍵盤同熒幕等嘅方法同受試者傾偈,最後個評判就要答,兩個受試者當中邊個係人邊個係機械-而如果搵咗一班評判返嚟之後,發現班評判嘅判斷嘅準確性明顯好過隨機靠撞(答中率等如 50%)嘅話,嗰部受試機械就算得上係通過咗圖靈測試,展現出同人類無異嘅智能[154][155]

圖靈測試喺人工智能哲學上引起咗廣泛討論。例如有學者批評圖靈測試指出,嚴格嚟講,就算一部機械通過咗圖靈測試,都只係表示佢曉喺一個人工環境下做某啲工作,但有智能嘅行為要求嘅係能夠喺自然環境下生存,所以圖靈測試喺測試機械智能上嘅功用有限[154][156]。因為噉,有學者諗出咗新版嘅圖靈測試,好似係所謂嘅「真正完整圖靈測試」(Truly Total Turing Test,TRTTT)噉,就認為一部機械要算得上展現人類智能,佢就需要能夠喺自然環境下達成人類能夠達成嘅重大成就,包括係-好似人類噉樣-創作出藝術品音樂遊戲以及語言等嘅文化產物[157]

虛構作品嘅描繪

 
一個演員扮成科學怪人嘅樣
睇埋:數碼朋卡

人工智能早喺 19 世紀初經已喺科幻(sci-fi)或者類似體裁嘅創作當中出現:由英國作家瑪莉雪萊(Mary Shelley)寫嘅經典科幻小說《科學怪人》(Frankenstein)涉及咗一個人類科學家創造咗一隻有血有肉、有智能嘅生命體(喺廣義上算係人工智能),而呢隻生命體仲有自己嘅意志,仇視佢嘅創造者同埋對佢嘅創造者造成威脅,可以算係早期用咗人工智能橋段嘅科幻作品[158]

人工智能叛變

內文: 人工智能叛變友好嘅人工智能

自從嗰陣開始,西人就有喺度憂慮人工製造嘅有智能物體會對人構成威脅,出咗兩派見解:一方面,人工智能叛變(AI takeover)呢條橋喺科幻創作(尤其係反烏托邦性數碼朋卡)當中成日出現[註 4],廿世紀尾嘅《未來戰士》(Terminator)故仔講能夠控制全世界嘅電腦系統嘅人工神經網絡程式「天網」為咗唔想俾人閂佢而嘗試消滅人類、《廿二世紀殺人網絡》(The Matrix)故仔就係講機械同人類打仗,而且仲將人嘅精神韞喺虛擬世界入面嚟攞佢哋嘅身體做能源、廿一世紀初嘅作品《智能叛侶》(Ex Machina)嘅其中一個主要角色就係一個為咗唔想俾人韞住而呃人謀殺人嘅人工智能機械人;另一方面,又有一啲作品描繪一啲對人好忠心好友善嘅人工智能[註 5],好似係日本嘅《叮噹》同《小飛俠阿童木》噉,都係以對人友好嘅人工智能機械人做主角[159][160]

機械人三定律

內文: 機械人三定律

機械人三定律(the Three Laws of Robotics)係由美國著名科幻作家阿西莫夫(Isaac Asimov)喺佢多份作品當中提出嘅諗頭。根據佢嘅見解,人工智能機械人必需遵守三條法則:

  1. 唔准傷害人類或者透過唔採取行動令到人類受傷害;
  2. 要服從人類俾嘅指令,除非嗰個指令違反咗法則一;
  3. 要保護自己,除非呢一點違反咗法則一或者法則二。

呢三條定律確保咗啲機械人會聽話,肯為咗服務人類而犧牲自己得嚟,又唔會俾某啲人類利用嚟做一啲傷害他人嘅嘢[161]。機械人三定律喺西方社會係一個社會大眾喺有關機械人道德嘅討論上常見嘅話題,常見到俾人當咗係橋段嘅一種[註 6],但專業嘅人工智能研究者多數都因為嫌呢三條定律有歧義等嘅問題,而好少可會認真噉看待佢哋[162]

對人性嘅反思

睇埋:心靈哲學

除此之外,又有一啲文學影視作品試圖透過比較人工智能同埋人類智能嚟令到讀者觀眾反思乜嘢為止「人」,呢啲作品好多時都係描述有能力感受(物理上同心理上嘅)痛苦嘅人工智能,並且透過描繪佢哋嘅掙扎嚟引起讀者觀眾討論呢啲人工智能應唔應該俾世人當成「人」。例如係由美國大導演史匹堡(Spielberg)執導嘅戲《人工智能》(A.I.: Artificial Intelligence)噉,套戲嘅主角係一個俾人創造,用嚟俾啲冇仔女嘅夫婦有得湊細路嘅機械男仔大衛,佢由俾養母抗拒至受接納,後嚟再因為想變成一個有血有肉嘅人類男仔而冒險,當中有一幕係噉嘅:大衛俾一班仇視機械人嘅人捉咗,而當嗰班仇視機械人嘅人打算當眾處死大衛嗰陣,啲圍觀嘅人見到佢迫真嘅外形同埋佢嘅行為舉止,以為佢係一個真嘅人類細路而反對處死佢-呢一幕令到好多觀眾反思內部用金屬造但有情感嘅大衛算唔算係一個「人」[163]

註釋

  1. 睇埋強化懲罰
  2. 數學上,「 」係指「事件   發生咗嘅話,事件   發生嘅機會率」。可以睇吓概率論
  3. 可以睇吓 AlphaGo
  4. 可以睇吓 TV TropesA.I. Is a Crapshoot
  5. 可以睇吓 TV TropesBenevolent A.I.
  6. 可以睇吓 TV TropesThree Laws Compliant

睇埋

相關領域

參考文獻

教科書

  • Hutter, Marcus (2005). Universal Artificial Intelligence. Berlin: Springer.
  • Jackson, Philip (1985). Introduction to Artificial Intelligence (2nd ed.). Dover.
  • Luger, George; Stubblefield, William (2004). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). Benjamin/Cummings.
  • Neapolitan, Richard; Jiang, Xia (2018). Artificial Intelligence: With an Introduction to Machine Learning. Chapman & Hall/CRC.
  • Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4.
  • Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall.
  • Russell, Stuart J.; Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall.
  • Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press.
  • Winston, Patrick Henry (1984). Artificial Intelligence. Reading, MA: Addison-Wesley.
  • Rich, Elaine (1983). Artificial Intelligence. McGraw-Hill.
  • Bundy, Alan (1980). Artificial Intelligence: An Introductory Course (2nd ed.). Edinburgh University Press.
  • Poole, David; Mackworth, Alan (2017). Artificial Intelligence: Foundations of Computational Agents (2nd ed.). Cambridge University Press.

研究史

  • Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks.
  • McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd..
  • Newquist, HP (1994). The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think. New York: Macmillan/SAMS.
  • Nilsson, Nils (2009). The Quest for Artificial Intelligence: A History of Ideas and Achievements. New York: Cambridge University Press.

其他文獻

  • D. H. Autor, "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015). 29(3), Journal of Economic Perspectives, 3.
  • TechCast Article Series, John Sagi, "Framing Consciousness".
  • Boden, Margaret, Mind As Machine, Oxford University Press, 2006
  • Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93.
  • Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", Scientific American, vol. 316, no. 6 (June 2017), pp. 60–65.
  • Johnston, John (2008). The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press.
  • Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. Multiple tests of artificial-intelligence efficacy are needed because, "just as there is no single test of athletic prowess, there cannot be one ultimate test of intelligence." One such test, a "Construction Challenge", would test perception and physical action—"two important elements of intelligent behavior that were entirely absent from the original Turing test." Another proposal has been to give machines the same standardized tests of science and other disciplines that schoolchildren take. A so far insuperable stumbling block to artificial intelligence is an incapacity for reliable disambiguation. "[V]irtually every sentence [that people generate] is ambiguous, often in multiple ways." A prominent example is known as the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence—such as "he", "she" or "it"—refers.
  • E. McGaughey, "Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy" (2018). SSRN, part 2(3).
  • Myers, Courtney Boyd ed. (2009). "The AI Report". Forbes, June 2009.
  • Raphael, Bertram (1976). The Thinking Computer. W.H. Freeman and Company.
  • Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference approach" (PDF). Journal of Informetrics. 4 (4): 447–459.
  • Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649.
  • Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994.
  • Tom Simonite (29 December 2014). "2014 in Computing: Breakthroughs in Artificial Intelligence". MIT Technology Review.

  1. 1.0 1.1 This list of intelligent traits is based on the topics covered by the major AI textbooks, including:
    • Russell & Norvig 2003.
    • Luger & Stubblefield 2004.
    • Poole, Mackworth & Goebel 1998.
    • Nilsson 1998.
  2. General intelligence (strong AI) is discussed in popular introductions to AI:
    • Kurzweil 1999 and Kurzweil 2005.
  3. 3.0 3.1 3.2 3.3 Definition of AI as the study of intelligent agents:
    • Poole, Mackworth & Goebel 1998, p. 1, which provides the version that is used in this article. Note that they use the term "computational intelligence" as a synonym for artificial intelligence.
    • Russell & Norvig (2003). (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field" (Russell & Norvig 2003, p. 55).
    • Nilsson 1998.
    • Legg & Hutter 2007.
  4. 4.0 4.1 Russell & Norvig 2009, Ch. 2.
  5. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition." (McCorduck 2004, p. 34) "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized." (McCorduck 2004, p. xviii) "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction." (McCorduck 2004, p. 3) She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods." (McCorduck 2004, pp. 340–400).
  6. "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews.
  7. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian.
  8. Solomonoff, R. J. (1985). The Time Scale of Artificial Intelligence; Reflections on Social Effects, Human Systems Management, Vol 5, P. 149-153.
  9. Optimism of early AI:
    • Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109.
    • Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109.
  10. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI:
    • McCorduck 2004, pp. 426–441.
    • Crevier 1993, pp. 161–162,197–203, 211, 240.
    • Russell & Norvig 2003, p. 24.
    • NRC 1999, pp. 210–211.
  11. First AI Winter, Mansfield Amendment, Lighthill report
    • Crevier 1993, pp. 115–117.
    • Russell & Norvig 2003, p. 22.
    • NRC 1999, pp. 212–213.
    • Howe 1994.
  12. Second AI winter:
    • McCorduck 2004, pp. 430–435.
    • Crevier 1993, pp. 209–210.
    • NRC 1999, pp. 214–216.
  13. Pamela McCorduck (2004, pp. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other."
  14. AI applications widely used behind the scenes:
    • Russell & Norvig 2003, p. 28.
    • Kurzweil 2005, p. 265.
    • NRC 1999, pp. 216–222.
  15. "an algorithm is a procedure for computing a function (with respect to some chosen notation for integers) ... this limitation (to numerical functions) results in no loss of generality", (Rogers 1987:1).
  16. Domingos 2015, Chapter 1.
  17. Greg Cashman. (2000). "International Interaction: Stimulus–Response Theory and Arms Races". What causes war?: an introduction to theories of international conflict. Lexington Books. pp. 160–192.
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  19. Meyer, A. F., Williamson, R. S., Linden, J. F., & Sahani, M. (2017). Models of neuronal stimulus-response functions: elaboration, estimation, and evaluation. Frontiers in systems neuroscience, 10, 109.
  20. Evolutionary algorithms are the living, breathing AI of the future. VB.
  21. Domingos 2015, Chapter 5.
  22. Domingos 2015, Chapter 6, Chapter 7.
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  28. 28.0 28.1 28.2 Intractability and efficiency and the combinatorial explosion:
    • Russell & Norvig 2003, pp. 9, 21–22.
  29. Domingos 2015, Chapter 2, Chapter 3.
  30. Hart, P. E.; Nilsson, N. J.; Raphael, B. (1972). "Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"". SIGART Newsletter, (37): 28–29.
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  32. Problem solving, puzzle solving, game playing and deduction:
    • Russell & Norvig 2003, chpt. 3–9,
    • Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9,
    • Luger & Stubblefield 2004, chpt. 3,4,6,8,
    • Nilsson 1998, chpt. 7–12.
  33. 33.0 33.1 Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260.
  34. Uncertain reasoning:
    • Russell & Norvig 2003, pp. 452–644,
    • Poole, Mackworth & Goebel 1998, pp. 345–395,
    • Luger & Stubblefield 2004, pp. 333–381,
    • Nilsson 1998, chpt. 19.
  35. Dane, E., Baer, M., Pratt, M. G., & Oldham, G. R. (2011). Rational versus intuitive problem solving: How thinking "off the beaten path" can stimulate creativity. Psychology of Aesthetics, Creativity, and the Arts, 5(1), 3.
  36. Sherin, B. (2006). Common sense clarified: The role of intuitive knowledge in physics problem solving. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 43(6), 535-555.
  37. Machine perception:
    • Russell & Norvig 2003, pp. 537–581, 863–898
    • Nilsson 1998, ~chpt. 6.
  38. Computer vision:
    • ACM 1998, I.2.10
    • Russell & Norvig 2003, pp. 863–898.
    • Nilsson 1998, chpt. 6.
  39. Speech recognition:
    • ACM 1998, ~I.2.7
    • Russell & Norvig 2003, pp. 568–578.
  40. Object recognition:
    • Russell & Norvig 2003, pp. 885–892.
  41. Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".(Turing 1950) In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".(Solomonoff 1956).
  42. This is a form of Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E."
  43. 43.0 43.1 Learning:
    • ACM 1998, I.2.6,
    • Russell & Norvig 2003, pp. 649–788,
    • Poole, Mackworth & Goebel 1998, pp. 397–438,
    • Luger & Stubblefield 2004, pp. 385–542,
    • Nilsson 1998, chpt. 3.3, 10.3, 17.5, 20.
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  46. Knowledge representation:
    • ACM 1998, I.2.4,
    • Russell & Norvig 2003, pp. 320–363,
    • Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345,
    • Luger & Stubblefield 2004, pp. 227–243,
    • Nilsson 1998, chpt. 18.
  47. Knowledge engineering:
    • Russell & Norvig 2003, pp. 260–266,
    • Poole, Mackworth & Goebel 1998, pp. 199–233,
    • Nilsson 1998, chpt. ≈17.1–17.4.
  48. Limitations of Knowledge Representation Models. 互聯網檔案館歸檔,歸檔日期2018年8月13號,..
  49. Kwasnik, B. H. (1999). The role of classification in knowledge representation and discovery.
  50. Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts):
    • Russell & Norvig 2003, pp. 349–354,
    • Poole, Mackworth & Goebel 1998, pp. 174–177,
    • Luger & Stubblefield 2004, pp. 248–258,
    • Nilsson 1998, chpt. 18.3.
  51. Representing events and time: Situation calculus, event calculus, fluent calculus (including solving the frame problem):
    • Russell & Norvig 2003, pp. 328–341,
    • Poole, Mackworth & Goebel 1998, pp. 281–298,
    • Nilsson 1998, chpt. 18.2
  52. Causal calculus:
    • Poole, Mackworth & Goebel 1998, pp. 335–337.
  53. Representing knowledge about knowledge: Belief calculus, modal logics:
    • Russell & Norvig 2003, pp. 341–344,
    • Poole, Mackworth & Goebel 1998, pp. 275–277
  54. 54.0 54.1 54.2 OWL Example with RDF Graph. Ontologies and Semantic Web.
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  61. Expert knowledge as embodied intuition:
    • Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI)
    • Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.)
    • Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.)
  62. Planning:
    • ACM 1998, ~I.2.8,
    • Russell & Norvig 2003, pp. 375–459,
    • Poole, Mackworth & Goebel 1998, pp. 281–316,
    • Luger & Stubblefield 2004, pp. 314–329,
    • Nilsson 1998, chpt. 10.1–2, 22.
  63. Information value theory:
    • Russell & Norvig 2003, pp. 600–604.
  64. Multi-agent planning and emergent behavior:
    • Russell & Norvig 2003, pp. 449–455.
  65. Classical planning:
    • Russell & Norvig 2003, pp. 375–430,
    • Poole, Mackworth & Goebel 1998, pp. 281–315,
    • Luger & Stubblefield 2004, pp. 314–329,
    • Nilsson 1998, chpt. 10.1–2, 22.
  66. Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning:
    • Russell & Norvig 2003, pp. 430–449.
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    • ACM 1998, I.2.7
    • Russell & Norvig 2003, pp. 790–831
    • Poole, Mackworth & Goebel 1998, pp. 91–104
    • Luger & Stubblefield 2004, pp. 591–632.
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  70. Applications of natural language processing, including information retrieval (i.e. text mining) and machine translation:
    • Russell & Norvig 2003, pp. 840–857,
    • Luger & Stubblefield 2004, pp. 623–630.
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  85. Moving and configuration space: Russell & Norvig 2003, pp. 916–932.
  86. Emotion and affective computing:
    • Minsky 2006.
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    • McCorduck 2004, pp. 51–107.
    • Crevier 1993, pp. 27–32.
    • Russell & Norvig 2003, pp. 15, 940.
    • Moravec 1988, p. 3.
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  99. The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt.
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    • McCorduck 2004, pp. 251–259.
    • Crevier 1993.
  102. AI research at Edinburgh and in France, birth of Prolog:
    • Crevier 1993, pp. 193–196.
    • Howe 1994.
  103. Knowledge revolution:
    • McCorduck 2004, pp. 266–276, 298–300, 314, 421.
    • Russell & Norvig 2003, pp. 22–23.
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  107. Revival of connectionism:
    • Crevier 1993, pp. 214–215.
    • Russell & Norvig 2003, p. 25.
  108. Computational intelligence
    • IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine.
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    • ACM 1998, ~I.2.3,
    • Russell & Norvig 2003, pp. 462–644,
    • Poole, Mackworth & Goebel 1998, pp. 345–395,
    • Luger & Stubblefield 2004, pp. 165–191, 333–381,
    • Nilsson 1998, chpt. 19.
  110. Bayesian inference algorithm:
    • Russell & Norvig 2003, pp. 504–519,
    • Poole, Mackworth & Goebel 1998, pp. 361–381,
    • Luger & Stubblefield 2004, pp. ~363–379,
    • Nilsson 1998, chpt. 19.4 & 7.
  111. Bayesian learning and the expectation-maximization algorithm:
    • Russell & Norvig 2003, pp. 712–724,
    • Poole, Mackworth & Goebel 1998, pp. 424–433,
    • Nilsson 1998, chpt. 20.
  112. Bayesian decision theory and Bayesian decision networks:
    • Russell & Norvig 2003, pp. 597–600.
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  114. Bayesian networks:
    • Russell & Norvig 2003, pp. 492–523,
    • Poole, Mackworth & Goebel 1998, pp. 361–381,
    • Luger & Stubblefield 2004, pp. ~182–190, ≈363–379,
    • Nilsson 1998, chpt. 19.3–4.
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    • Luger & Stubblefield 2004, pp. 467–474,
    • Nilsson 1998, chpt. 3.3.
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