Annals of Artificial Intelligence. The Case That A.I. Is Thinking, ChatGPT does not have an inner life. Yet it seems to know what it’s talking about. By James Somers
人工智慧公司 Anthropic 的執行長 Dario Amodei 一直預測，到 2027 年，在生物學、數學、工程學和寫作等領域「比諾貝爾獎得主更聰明」的人工智慧可能會上線。他設想，數百萬個模型副本將高速運轉，每個副本都進行著自己的研究：一個「資料中心裡的天才王國」。2026年6月，OpenAI 的 Sam Altman 撰文稱，該行業正處於構建「數位超級智慧」的邊緣。 「2030年代很可能與以往任何時期都截然不同，」他斷言。同時，大多數人目前日常使用的AI工具，卻讓人想起曾經的微軟Office「助手」Clippy，它其實更像是個煩人的搗蛋鬼。 Zoom的AI工具會建議你問它「有什麼破冰話題可以分享？」或指示它「寫一封簡短的訊息表達感謝」。 Siri擅長設定提醒，但除此之外就沒什麼其他功能了。我的一個朋友在Gmail裡看到一個按鈕，上面寫著「感謝並分享趣事」。他點擊後，Google的AI編造了一個關於他從未去過的土耳其旅行的趣事。
人工智慧倉促且不均衡的推廣，造成了一種迷霧，讓人很容易得出這樣的結論：一切都只是炒作而已，沒什麼好看的。誠然，炒作之風盛行：Amodei 的時間線簡直如同科幻小說一般。 （人工智慧模型的發展速度遠沒有那麼快。）但認為大型語言模型只是在重新排列單詞，則是另一種一廂情願的想法。我曾經也認同這種觀點。我曾安慰自己說，人工智慧與真正的智慧或理解力幾乎無關。我甚至還為它的缺點而歡呼——為它「主隊」加油。後來，我開始在程式設計師的工作中運用人工智慧，因為我擔心如果不用它，就會落後。 （我的雇主是一家貿易公司，對包括 Anthropic 在內的多家人工智慧公司進行了投資和合作。）許多人認為，編寫程式碼是人工智慧最擅長的；程式碼比散文更有結構，而且通常可以自動驗證給定程式是否有效。我的轉變非常迅速。起初，我甚至會參考人工智慧模型來代替查閱資料。然後我給他們佈置了一些小小的、獨立的難題。最終，我讓他們承擔了真正的工作──我畢生致力於此的那種工作。我親眼目睹這些模型在幾秒鐘內就理解了數千行程式碼的複雜細節。它們能夠發現細微的錯誤，並協調複雜的新功能。最終，我被調到一個快速發展的團隊，該團隊致力於更好地利用人工智慧工具，並開發我們自己的工具。
據說科幻作家威廉·吉布森曾說過，未來已來，只是尚未普及——這或許可以解釋為什麼人工智慧似乎催生了兩種截然不同的文化：一種是漠視，另一種是著迷。在我們的日常生活中，能夠預訂假期或報稅的人工智慧「代理」往往不受歡迎，但我的一些同事卻使用人工智慧編寫大量程式碼，有時甚至同時運行多個編碼代理。模型有時會犯一些業餘錯誤，或者陷入一些無意義的循環，但隨著我學會如何有效地使用它們，它們讓我在一個晚上就能完成以前需要一個月才能完成的工作。不久前，我甚至在完全不懂如何開發 iOS 應用程式的情況下，就做出了兩個 iOS 應用程式。
我曾經有個老闆說過，面試應該要檢視應徵者的優勢，而不是他們的弱點。大型語言模型有許多弱點：它們會臆造出聽起來合情合理的謊言；即使你錯了，它們也會唯唯諾諾；它們會被簡單的謎題難倒。但我記得，曾經人工智慧模型那些顯而易見的優勢——流暢性、反應靈敏、能夠「理解」別人在說什麼——被視為至高無上的聖杯。當你親身體驗過這些優勢後，你會不禁思考：這種理解的錯覺要達到怎樣的程度，你才會不再稱之為錯覺？
今年夏天一個酷熱難耐的日子，我的朋友馬克斯和家人在遊樂場聚會。不知什麼原因，兒童灑水器被關掉了，馬克斯的妻子答應大家她先生會去修。面對一群臉漲得通紅的六、七歲孩子，馬克斯走進工具棚，希望能找到一個醒目的「開」字開關。然而，他看到的卻是一團迷宮般的古老管道和閥門。正當他準備放棄時，他靈機一動，掏出手機，拍了張照片，並附上問題描述，輸入到 ChatGPT-4o 中。人工智慧思考了一會兒，或許根本沒思考，但它還是說他看到的是灌溉系統中常見的止回閥。他看到底部那個黃色的球閥了嗎？那很可能就是控制水流的閥門。 Max 試了一下，水流打開了，操場上頓時響起一片歡呼聲。
ChatGPT 只是機械地把文字組合在一起，還是它了解問題？答案或許能教導我們一些關於理解自身的重要知識。 「神經科學家必須面對這個謙卑的事實，」加州大學柏克萊分校的神經科學教授曹多麗（Doris Tsao）告訴我，「機器學習的進步讓我們對智慧的本質有了比過去一百年來神經科學所發現的更多了解。」曹多麗最著名的成就是破解了獼猴如何感知面孔。她的團隊學會了預測當猴子看到特定面孔時哪些神經元會激活；更令人驚訝的是，給定一組神經元激活模式，曹多麗的團隊就能渲染出這張臉。他們的工作建立在人工智慧模型中面孔表徵方式的研究之上。如今，她最喜歡問別人的問題是：“你從ChatGPT中獲得了什麼最深刻的見解？”她說：“我的答案是，我認為它徹底揭開了思維的神秘面紗。”
我們是如何走到今天的，最簡單的解釋是這樣的：在20世紀80年代，一個由認知心理學家和電腦科學家組成的小團隊試圖在機器中模擬思維。其中較著名的包括大衛·魯梅爾哈特、傑弗裡·辛頓和詹姆斯·麥克萊蘭，他們後來在加州大學聖地牙哥分校組成了一個研究小組。他們將大腦視為一個龐大的網絡，其中的神經元以特定模式放電，進而引發其他神經元群的放電，如此循環往復；這種模式的互動就是思維。大腦透過改變神經元之間連結的強度來進行學習。至關重要的是，科學家們透過創建人工神經網路來模擬這個過程，並應用一種名為梯度下降的簡單演算法來提高其預測的準確性。 （該演算法可以比作一個徒步旅行者從山頂走到山谷；最終找到方向的一個簡單策略是確保每一步都向下。）在大型網路中使用此類演算法稱為深度學習。
人工智慧領域的其他人曾懷疑神經網路是否足夠複雜以應對現實世界的任務，但隨著網路規模的擴大，它們開始解決以前無法解決的問題。人們曾花費大量篇幅撰寫論文，致力於開發區分手寫數字或識別圖像中人臉的技術；然而，深度學習演算法卻能消化底層數據，發現問題的微妙之處，使這些項目顯得過時。深度學習很快便征服了語音辨識、翻譯、圖像描述、棋盤遊戲，甚至連預測蛋白質折疊方式的問題也迎刃而解。
如今領先的人工智慧模式利用網路的大量資料進行訓練，採用一種名為「下一個詞元預測」的技術。模型透過猜測接下來要讀取的內容，然後將這些猜測與實際出現的內容進行比較來學習。錯誤的猜測會促使神經元之間的連結強度改變；這就是梯度下降。最終，模型在預測文本方面變得如此出色，以至於它似乎能夠理解事物並做出有意義的判斷。這值得我們深思。一群人試圖揭開大腦運作的秘密。隨著他們的模型逐漸發展到類似大腦的大小，它開始做出一些事情…
人工神經網路和真正的神經網路一樣，都能壓縮經驗。 DeepSeek 是目前最優秀的開源人工智慧模式之一，它能夠創作小說、提供醫療診斷建議，還能用數十種語言流利地表達，聽起來就像母語人士一樣。它使用基於數TB資料的“下一個詞元預測”演算法進行訓練。但當你下載這個模型時，它的大小只有訓練資料的六百分之一。這就像是網路的精華，被壓縮到你的筆記型電腦上。特德·姜（Ted Chiang）曾將早期版本的 ChatGPT 比作網絡的模糊 JPEG 圖片，這話不無道理——但在我看來，這恰恰是這些模型變得越來越智能的原因。姜在他的文章中指出，要壓縮一個包含數百萬個算術範例的文字文件，你不會創建一個 zip 文件，而是會編寫一個計算器程式。 「理解文本才能實現最大程度的壓縮，」他寫道。或許，法學碩士們正在開始這麼做。
想像一個電腦程式能夠真正理解、真正思考，這似乎有些不自然，甚至令人反感。我們通常將思考理解為一種有意識的行為，就像喬伊斯筆下的內心獨白，或是普魯斯特白日夢中感官記憶的流動。或者，我們可能認為思考指的是推理：一步一步解決問題。在討論人工智慧時，我們常常混淆這些不同的思考方式，這使得我們的判斷過於片面。一種觀點認為，ChatGPT 顯然沒有思考，因為它顯然沒有進行普魯斯特式的白日夢；另一種觀點則認為，ChatGPT 顯然在思考，因為它比你更能解決邏輯難題。
然而，其中蘊含著更為微妙的因素。我不認為 ChatGPT 擁有內在的意識，但它似乎知道自己在說什麼。理解——掌握正在發生的事情——是一種被低估的思考方式，因為它大多是無意識的。印第安納大學認知科學與比較文學教授道格拉斯‧霍夫施塔特（Douglas Hofstadter）喜歡說，認知就是辨識。霍夫施塔特因其關於心靈和意識的著作《哥德爾、埃舍爾、巴赫：一條永恆的金辮》（Gödel, Escher, Bach: An Eternal Golden Braid）而聞名，該書榮獲1980年普利策獎。霍夫施塔特經過數十年的研究發展出來的理論是，「視作」是思考的本質。你會把一塊顏色看成汽車，把另一塊看成鑰匙圈；無論字母「A」是用什麼字體寫成的，或是字跡多麼潦草，你都能認出來。霍夫施塔特認為，同樣的原理也適用於更抽象的感知。當一位國際象棋大師觀察棋盤時，多年的練習會轉化為一種觀察方式：白方的像很弱；這個殘局很可能是和棋。你會把河中的漩渦視為危險的訊號，不宜過河。你把參加的會議看作是皇帝的新衣。我那快兩歲的兒子意識到，上午晚些時候推著嬰兒車散步可能是個吃羊角麵包的機會，於是就提出了相應的要求。在霍夫斯塔特看來，這就是智能的精髓。
霍夫斯塔特是人工智慧領域最早的「通貨緊縮論者」之一，而我的懷疑也源自於他的觀點。他曾寫道，大多數人工智慧研究與真正的思考幾乎無關。 2000年代我在大學讀書時，也認同他的觀點。當然也有例外。他覺得加州大學聖地牙哥分校的研究小組很有意思。他也欣賞一位名氣不大的芬蘭裔美國認知科學家彭蒂·卡內爾瓦（Pentti Kanerva）的工作。卡內爾瓦注意到高維空間數學中一些不尋常的性質。在高維空間中，任兩個隨機點之間的距離都可能非常遙遠。但出乎意料的是，每個點周圍都有一大片鄰近點，所以只要你“足夠接近”，就能輕鬆找到它。這讓卡內瓦想起了記憶的運作方式。在1988年出版的《稀疏分佈式記憶》一書中，卡內瓦論證了思想、感覺和回憶都可以用高維度空間中的座標來表示。大腦似乎是儲存這些資訊的完美硬體。每段記憶都有一個位址，由回憶時活化的神經元定義。新的體驗會啟動新的神經元群，代表新的位址。兩個地址在許多方面可能不同，但在其他方面可能相似；一種感知或記憶會觸發附近的其他記憶。乾草的氣味會讓人想起夏令營的回憶。貝多芬第五號交響曲的前三個音符會引出第四交響曲。你從未見過的棋局會讓你想起以前的棋局——不是所有棋局，只是那些在附近發生的棋局。
人們常說人工智慧是個黑盒子，但事實恰恰相反：科學家可以探究單一人工神經元的活動，甚至可以對其進行修改。 「擁有一個能夠體現人類智慧理論的運作系統——這是認知神經科學的夢想，」普林斯頓大學神經科學家肯尼斯·諾曼告訴我。諾曼創建了海馬體的電腦模型，海馬體是大腦中儲存情境記憶的區域，但過去的模型非常簡單，他只能向模型輸入一些粗略的、近似人類思維的資訊。 「現在，你可以給記憶模型提供與人類完全相同的刺激，」他說。
萊特兄弟在早期製造飛機的過程中研究鳥類。他們注意到，鳥類會逆風起飛，儘管人們可能會認為它們應該順風起飛，而且它們會扭曲翼尖以保持平衡。這些發現影響了他們最初的滑翔機設計。隨後，他們建造了一個六英尺長的風洞，以便在精確控制的條件下測試一套人造機翼。他們的下一輪滑翔機飛行試驗取得了更大的成功。奇怪的是，直到他們製造出一台可飛行的機器之後，才有可能真正理解鳥類是如何飛行的。
人工智慧使科學家能夠將思維本身置於風洞中進行測試。在一篇題為《論大型語言模型的生物學》的論文中，人類學家觀察了克勞德對查詢的回應，並描述了「迴路」——一系列特徵的級聯，這些特徵共同執行複雜的計算。 （調用正確的記憶是思考的第一步；在迴路中組合和操作這些記憶可以說是另一步。）長期以來，人們對大型語言模型的一個批評是，由於它們一次只能生成一個響應詞，因此無法進行計劃或推理。但是，當你讓克勞德完成一首詩中的押韻對句時，大腦中會啟動一個迴路，思考新一行的最後一個詞，以確保它押韻。然後，它會反向推導，將整行詩句組合起來。人格心理學研究者將此視為他們的模型確實會進行規劃的證據。稍微瞇起眼睛，你或許會第一次感受到，大腦內部的運作機制似乎盡收眼底。
不過，你真的需要瞇起眼睛仔細觀察。 「我擔心的是，人們的態度從『我對此非常懷疑』轉變為完全放下戒備，」普林斯頓大學的神經科學家諾曼告訴我。 「很多事情仍然有待解決。」我就是諾曼所說的那些人之一。 （或許我太容易被「稀疏分散式記憶體」和人本主義模型的表面上的融合所打動。）過去一兩年裡，我開始相信最近因其人工智慧研究而獲得諾貝爾獎的杰弗裡·辛頓在2020年對記者卡倫·郝所說的話：「深度學習將無所不能。」但我們也看到，更大的模型並不總是更好的模型。模型性能與規模的關係曲線已經開始趨於平緩。找到模型尚未處理過的高品質資料變得越來越困難，而運算能力也日益昂貴。 GPT-5於8月發布，它只是一次漸進式的改進——但卻令人大失所望，幾乎戳破了人工智慧投資泡沫。此時此刻，我們需要一種折衷的懷疑態度：既要認真對待當前的人工智慧模型，又不能認為已經沒有難題可解決了。
這些問題中或許最關鍵的是如何設計一個學習效率堪比人類的模式。據估計，GPT-4 在訓練過程中接觸了數萬億個單字；而兒童只需幾百萬個單字就能流利使用英語。認知科學家告訴我們，新生兒的大腦具有某些“歸納偏差”，能夠加速學習。 （當然，大腦是數百萬年進化的結果——它本身也是一種訓練數據。）例如，人類嬰兒會認為世界是由物體構成的，而其他生物擁有信念和意圖。當媽媽說「香蕉」時，嬰兒會將這個詞與她正在看到的整個黃色物體聯繫起來——而不僅僅是香蕉尖或香蕉皮。嬰兒會進行一些小實驗：我可以吃這個嗎？我可以把這個丟多遠？他們的動機源自於慾望、好奇心和挫折感等情緒。孩子總是試著做一些略微超出自身能力的事情。他們的學習之所以高效，是因為它是具身化的、適應性的、有意識的和持續的。或許，真正理解世界需要參與其中。
相較之下，人工智慧的經驗極為匱乏，根本不能稱之為「經驗」。大型語言模型是基於已經極其精細的資料進行訓練的。 「我認為它們之所以有效，是因為它們依附於語言，」加州大學柏克萊分校的神經科學家曹告訴我。
相較之下，人工智慧的經驗極為匱乏，根本不能稱之為「經驗」。大型語言模型是基於已經極其精細的資料進行訓練的。 「我認為它們之所以有效，是因為它們依附於語言，」加州大學柏克萊分校的神經科學家曹告訴我。
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