2026 Ewan Lecture by Prof. Geoffrey Hinton: "Living with Alien Beings"

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2026 Ewan Lecture by Prof. Geoffrey Hinton: "Living with Alien Beings"

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這是一份完整的演講逐字稿整理（第一部分）。由於傑弗裡·辛頓（Geoffrey Hinton）教授的演講長達 50 分鐘，文字量非常大（超過數千字），受限於單次輸出的篇幅，我將演講分為兩部分提供給您。
以下是 第一部分，涵蓋了演講的前半程（約前 25 分鐘），從辛頓教授上台開始，一直講到他關於「必死計算（Mortal Computation）」與數位計算的區別。
內容完全忠實於原演講的完整語句，未做刪減，採用段落式中英文對照，適合學生深度閱讀。
2026 Ewan Lecture by Prof. Geoffrey Hinton: “Living with Alien Beings” (Part 1/2)
演講者：Geoffrey Hinton
內容範圍：演講開始 (10:12) 至 「必死計算」概念引入 (36:00 左右)
[English]
Thank you. Um, I forgot what the title was, uh, but that gives you a sample of two titles for the talk. So I’m actually going to try and explain how AI works for people who don’t really understand how it works. So if you do understand how it works, if you’re a computer science student or a physicist who’s been using this stuff, I guess you can go to sleep for a while, or you can sort of look to see if I’m explaining it properly.
[Chinese]
謝謝。嗯，我忘了標題是什麼了，不過這剛好給了你們這次演講的兩個標題範例。我其實打算嘗試為那些不太了解 AI 運作原理的人，解釋一下它是如何運作的。所以如果你已經了解它是如何運作的，如果你是計算機科學系的學生或者一直使用這些東西的物理學家，我想你們可以先睡一會兒，或者你們可以看看我解釋得是否準確。
[English]
Back in the 1950s, there were two different paradigms for AI. There was the symbolic approach which assumed that intelligence had to work like logic. We had to somehow have symbolic expressions in our heads and we had to have rules for manipulating them. And that’s how you derive new expressions and that’s what reasoning was and that was the essence of intelligence. That wasn’t a very biological approach, um, that’s much more mathematical.
[Chinese]
回到 1950 年代，AI 有兩種不同的範式。一種是符號學派（Symbolic approach），它假設智能必須像邏輯一樣運作。我們的大腦中必須以某種方式存在符號表達式，並且必須有操作這些符號的規則。這就是推導新表達式的方式，這就是推理，也就是智能的本質。這並不是一個非常生物學的方法，嗯，這更偏向數學。
[English]
There was a very different approach, the biological approach, where intelligence was going to be in a neural network, a network of things like brain cells, and the key question was how do you learn the strength of the connections in the network. So two people who believed in the biological approach were von Neumann and Turing. Unfortunately both of them died young and AI got taken over by people who believed in the logical approach.
[Chinese]
還有另一種截然不同的方法，即生物學方法，認為智能將存在於神經網絡中——一個由類似腦細胞組成的網絡，而關鍵問題在於如何學習網絡中連接的強度。有兩位相信生物學方法的人是馮·諾伊曼（von Neumann）和圖靈（Turing）。不幸的是，他們都英年早逝，AI 領域被那些相信邏輯方法的人接管了。
[English]
So there’s two very different theories of the meaning of a word. People who believed in the logical approach believed that meaning was best understood in terms originally introduced by Saussure more than a century ago. The meaning of a word comes from its relationships to other words. So people in AI thought it’s how words relate to other words in propositions or in sentences that give the meaning to the words. And so to capture the meaning of a word you need some kind of relational graph, you need nodes for words and arcs between them and maybe labels on the arc saying how they were related or something like that.
[Chinese]
所以，關於詞彙的意義，有兩種截然不同的理論。相信邏輯方法的人認為，最好用索緒爾（Saussure）在一個多世紀前引入的概念來理解意義：一個詞的意義來自於它與其他詞的關係。所以 AI 領域的人認為，詞彙在命題或句子中與其他詞彙的關聯方式，賦予了詞彙意義。因此，為了捕捉一個詞的意義，你需要某種關係圖，你需要代表詞彙的節點和它們之間的連線，也許連線上還要有標籤說明它們是如何相關的，諸如此類。
[English]
In psychology they had a very, very different theory of meaning. The meaning of a word was just a big set of features. So for example the word Tuesday meant something, there were a big set of active features of Tuesday like it’s about time and stuff like that. Um, there would be big set of features for Wednesday and it would be almost the same set of features because Tuesday and Wednesday mean very similar things. So the psychology theory was very good for saying which words… how similar words are in their meaning. But those look like two very different theories of meaning. One that the meaning’s implicit in how something relates to other words in sentences and the other that the meaning is just a big set of features. And of course for neural networks one of these features would be an artificial neuron and so it gets active if the word has that feature and inactive if the word doesn’t have that feature.
[Chinese]
在心理學中，他們有一種非常不同的意義理論。一個詞的意義只是一大組特徵的集合。例如，「星期二」這個詞意味著某些東西，它有一大組關於「星期二」的活躍特徵，比如它與時間有關等等。嗯，「星期三」也會有一大組特徵，而且它幾乎是相同的一組特徵，因為星期二和星期三的意思非常相似。所以心理學理論非常適合用來解釋詞彙在意義上的相似程度。但這看起來像是兩種截然不同的意義理論。一種認為意義隱含在事物與句子中其他詞彙的關係中，而另一種認為意義只是一大組特徵。當然，對於神經網絡來說，這些特徵之一就是一個人工神經元，如果這個詞具有該特徵，神經元就會被激活，如果沒有，就不會被激活。
[English]
They look like different theories but in 1985 I figured out they’re really two different sides of the same coin. You can unify those two theories and I did it with a tiny language model because computers were tiny then. Actually they were very big but didn’t do much. The idea is you learn a set of features for each word and you learn how to make the features of the previous word predict the features of the next word. And to begin with when you’re learning they don’t predict the features of the next word very well so you revise the features that you assign to each word, you revise the way features interact until they predict the next word better.
[Chinese]
它們看起來像是不同的理論，但在 1985 年，我發現它們實際上是同一枚硬幣的兩面。你可以統一這兩種理論，我當時用一個微型語言模型做到了這一點，因為那時的電腦還很微小。其實它們體積很大，但功能不強。這個想法是，你為每個詞學習一組特徵，並且你學習如何讓前一個詞的特徵去預測下一個詞的特徵。一開始學習的時候，它們並不能很好地預測下一個詞的特徵，所以你會修正分配給每個詞的特徵，修正特徵互動的方式，直到它們能更好地預測下一個詞。
[English]
And then you take that discrepancy basically between how well they predict the next word, or rather the probability that they give to the actual next word that occurred in a text, and you take the discrepancy between the probability they give and the probability you’d like which is one, and you back propagate that through the network. So essentially you send information back through the network and using calculus which I’m not going to explain, you can figure out for every connection strength in the network how to change it so that next time you see that context, that string of words leading up or what is now called a prompt, you’ll be better at predicting the next word.
[Chinese]
然後你取那個差異，基本上就是它們預測下一個詞的準確度，或者更準確地說，是它們賦予文本中實際出現的下一個詞的概率，你取它們給出的概率和你想要的概率（也就是 1）之間的差異，並將其在網絡中進行反向傳播。本質上，你將信息傳回網絡，並使用微積分（我不打算在這裡解釋），你可以計算出網絡中每個連接強度該如何改變，以便下次你看到那個上下文時——那串前面的詞，或者現在被稱為「提示詞（prompt）」的東西——你會更擅長預測下一個詞。
[English]
Now in that kind of system all of the knowledge is in how to convert words into feature vectors and how the features should interact with one another to predict the features of the next word. There are no stored strings, there’s no stored sentences in there. It’s all in connection strengths that tell you how to convert a word to features and how features should interact. So all of this relational knowledge resides in these connection strengths but you’ve trained it on a whole bunch of sentences you got. So you’re taking this knowledge about meaning that’s kind of implicit in how words relate to each other in sentences, that’s the symbolic AI view of meaning, and you’re converting it by using back propagation, you’re converting it into how to convert a word into features and how these features should interact.
[Chinese]
在那種系統中，所有的知識都在於如何將詞彙轉換為特徵向量，以及這些特徵應如何相互作用以預測下一個詞的特徵。裡面沒有存儲字符串，沒有存儲句子。一切都在連接強度中，它告訴你如何將詞轉換為特徵以及特徵應如何互動。所以所有這些關係知識都存在於這些連接強度中，但你是用你得到的大量句子來訓練它的。所以你正在獲取這種關於意義的知識，這種知識隱含在句子中詞彙如何相互關聯的方式裡——那是符號 AI 對意義的觀點——然後你通過反向傳播將其轉化，轉化為如何將詞變為特徵以及這些特徵應如何互動。
[English]
So basically you’ve got a mechanism that will convert this implicit knowledge into knowledge in a whole bunch of connection strengths in a neural network and you can also go the other way. Given that you’ve got all this knowledge in connection strengths you can now generate new sentences. So AIs don’t actually store sentences they convert it all into features and interactions and then they generate sentences when they need them.
[Chinese]
所以基本上你擁有了一個機制，可以將這種隱性知識轉化為神經網絡中大量連接強度裡的知識，而且你也可以反過來做。既然你在連接強度中擁有了所有這些知識，你現在可以生成新的句子。所以 AI 實際上並不存儲句子，它們將一切轉化為特徵和互動，然後在需要時生成句子。
[English]
So over the next 30 years or so I used a tiny little example with only a hundred training examples and the sentences were only three words long and you predicted the last word from the first two. So about 10 years later computers were a lot faster and Yoshua Bengio showed that the same approach works for real sentences that is take English sentences and try predicting the next word and this approach worked pretty well. It took about 10 years after that before the leading computational linguists finally accepted that actually a vector of features is a good way to represent the meaning of a word they called it an embedding.
[Chinese]
所以在接下來的 30 年左右，我使用了一個只有一百個訓練範例的微小例子，句子只有三個詞長，你根據前兩個詞預測最後一個詞。大約 10 年後，電腦速度快了很多，Yoshua Bengio（約書亞·本吉奧）證明了同樣的方法適用於真實的句子，即採用英語句子並嘗試預測下一個詞，這種方法效果相當不錯。在那之後大約又過了 10 年，領先的計算語言學家才最終接受，特徵向量實際上是代表詞義的好方法，他們稱之為「嵌入（embedding）」。
[English]
And it took another 10 years before researchers at Google figured out a fancy way of letting features interact called a Transformer and that allowed Google to make much better language models. And ChatGPT, the GPT stands for Generative Pre-trained Transformer. They then released that on the world. Google didn’t release it on the world because they were worried about what it might do but ChatGPT had no such worries and now we all know what they can do.
[Chinese]
又過了 10 年，Google 的研究人員才想出了一種讓特徵互動的高級方法，稱為 Transformer，這讓 Google 能夠製造出更好的語言模型。而 ChatGPT 中的 GPT 代表「生成式預訓練變換器（Generative Pre-trained Transformer）」。然後他們將其發布給了全世界。Google 沒有發布是因為他們擔心它可能會做什麼，但 ChatGPT 沒有這種擔憂，現在我們都知道它們能做什麼了。
[English]
So now we have these large language models. I think of them as descendants of my tiny language model but then I would wouldn’t I. They use many more words as input they use many more layers of neurons and they use much more complicated interactions between features. I’m not going to describe those interactions for a talk to a general audience but I will try and give you a feel for them when I give you a big analogy for what language understanding is in a minute.
[Chinese]
現在我們有了這些大型語言模型。我把它們看作是我那個微型語言模型的後代，但我當然會這麼想，不是嗎。它們使用更多的詞作為輸入，使用更多層的神經元，並且使用更複雜的特徵互動。我不會在對普通觀眾的演講中描述這些互動，但我稍後會用一個關於語言理解的大類比來讓你們感受一下。
[English]
I believe the ways these large language models understand sentences are very similar to the way we understand sentences. When I hear a sentence what I do is I convert the words into big feature vectors and these features interact so I can predict what’s coming next and actually when I talk that’s what I’m doing too. So I think LLMs really do understand what they’re saying. There’s still some debate because there’s followers of Chomsky who say no no no they don’t understand anything they’re just a dumb statistical trick. I don’t see how if they don’t understand anything and it’s a dumb statistical trick they can answer any question you give them at the level of a not very good and not very honest expert.
[Chinese]
我相信這些大型語言模型理解句子的方式與我們理解句子的方式非常相似。當我聽到一個句子時，我所做的是將單詞轉換為巨大的特徵向量，這些特徵相互作用，所以我可以預測接下來會發生什麼，實際上當我說話時，我也是這麼做的。所以我認為 LLM 真的理解它們在說什麼。仍然存在一些爭論，因為喬姆斯基（Chomsky）的追隨者會說：「不不不，它們什麼都不懂，它們只是一個愚蠢的統計把戲。」我不明白，如果它們什麼都不懂，只是一個愚蠢的統計把戲，它們怎麼能以一種不太高明且不太誠實的專家水平，回答你提出的任何問題。
[English]
Okay so here’s my analogy this is particularly aimed at linguists for how language actually works. Language is all about meaning and what happened was one kind of great ape discovered a trick for modeling… so language is actually a method of modeling things and it can model anything. So let’s start off with a familiar method of modeling things which is Lego blocks. If I want to model the shape of a Porsche sort of where the stuff is I can model it pretty well using Lego blocks. Now if you’re a physicist you say “Yeah but the surface will have all the wrong dynamics with the wind it’s hopeless.” It’s true but to say where the stuff is I can do it pretty well with Lego blocks.
[Chinese]
好，這是我的一個類比，特別是針對語言學家的，關於語言實際是如何運作的。語言是關於意義的，而發生在我們身上的是，一種大型類人猿發現了一個建模的技巧……所以語言實際上是一種建模的方法，它可以模擬任何事物。讓我們從一個熟悉的建模方法開始，那就是樂高積木。如果我想模擬保時捷的形狀，大概就是東西在哪裡，我可以用樂高積木做得很好。現在，如果你是一個物理學家，你會說：「是的，但表面在風中的動力學表現完全錯誤，這沒法用。」這是真的，但要說明東西在哪裡，我可以用樂高積木做得很好。
[English]
Now words are like Lego blocks that’s the analogy but they differ in at least four ways. The first way is they’re very high dimensional. A Lego block doesn’t have many degrees of freedom they’re sort of rectangular um you could maybe stretch them you could have different Lego blocks of different sizes but for any given Lego block it’s got a kind of rigid shape and only a few degrees of freedom. A word isn’t like that a word is very high dimensional it’s got thousands of dimensions and what’s more its shape isn’t predetermined it’s got an approximate shape ambiguous words have several approximate shapes but it shape can deform to fit in with the context it’s in. So it differs in that it’s high dimensional and that it’s got a sort of default shape but it’s deformable. Now some of you may have difficulty imagining things in a thousand dimensions so here’s how you do it what you do is you imagine things in three dimensions and you say thousand very loudly to yourself.
[Chinese]
詞彙就像樂高積木，這就是那個類比，但它們至少在四個方面有所不同。第一點是它們是非常高維度的。樂高積木沒有很多自由度，它們差不多是矩形的，嗯，你也許可以拉伸它們，你可以有不同尺寸的樂高積木，但對於任何給定的樂高積木，它有一種剛性的形狀，只有幾個自由度。詞不是那樣的，詞是非常高維度的，它有成千上萬個維度。而且，它的形狀不是預先確定的，它有一個近似的形狀，有歧義的詞有幾個近似的形狀，但它的形狀可以變形以適應它所處的上下文。所以它的不同之處在於它是高維度的，並且它有一種默認形狀，但它是可變形的。現在你們有些人可能很難想像一千個維度的東西，所以這裡是方法：你想像三維的東西，然後對自己大聲喊「一千」。
[English]
One other difference is that well there’s a lot more words you each use about 30,000 of them that’s and each one has a name it’s very useful that each one has a name because that’s what allows us to communicate things to each other. Now how do words fit together well instead of having little plastic cylinders that fit into little plastic holes which is how Lego blocks fit together think of each word as having um long flexible arms and on the end of each arm it has a hand and as I deform the shape of the word the shapes of the hands all change. So the shapes of the hands depend on the shape of the word and as you change the shape of the word the shapes of the hands change. A word also has a whole bunch of gloves that are stuck to the word they’re stuck with the fingertips stuck to the word.
[Chinese]
另一個區別是，詞彙的數量要多得多，你們每個人大約使用 30,000 個詞，而且每個詞都有一個名字。每個詞都有名字非常有用，因為這就是讓我們能夠相互交流的原因。那麼詞彙是如何組合在一起的呢？與其像樂高積木那樣，有小塑膠圓柱體嵌入小塑膠孔中，不如想像每個詞都有長長的靈活手臂，每條手臂的末端都有一隻手。當我改變詞的形狀時，手的形狀都會改變。所以手的形狀取決於詞的形狀，當你改變詞的形狀時，手的形狀也會改變。一個詞還黏著一大堆手套，它們是用指尖黏在詞上的。
[English]
If you think in terms of Lego blocks and what you’re doing when you understand a sentence is you start off with the default shape for all these words and then what you have to do is figure out how I can deform the words and as they deform the words the shapes of the hands attached to them deform so that um words can fit their hands into the gloves of other words and we can get a whole structure where each word is connecting with many other words because we deformed it just right and we deformed the other word just right so the hands of this word fit in the gloves of that word.
[Chinese]
如果你用樂高積木來思考，當你理解一個句子時，你在做的是從所有這些詞的默認形狀開始，然後你必須弄清楚如何使這些詞變形。當詞變形時，附著在它們上面的手的形狀也會變形，這樣詞就可以把它們的手伸進其他詞的手套裡。我們可以得到一個完整的結構，其中每個詞都與許多其他詞相連，因為我們把它變形得剛剛好，我們把另一個詞也變形得剛剛好，所以這個詞的手正好放進那個詞的手套裡。
[English]
This isn’t exactly right this gives you a feel for what’s going on in Transformers anybody who knows about transformers can see that the the hands are like the queries and the the key yeah the queries and keys. It’s not quite right but it’ll give someone who’s not used to transformers a rough feel for what’s going on. So the computer and you when you do it have this difficult problem of how do I deform all these words so they all fit together nicely but if you can do that when they fit together nicely that structure you’ve got then is the meaning of the sentence that structure all these feature vectors for the words that all fit together nicely that’s what it means to understand a sentence.
[Chinese]
這不完全準確，但這讓你對 Transformer 中發生的事情有了一種感覺。任何了解 Transformer 的人都可以看出，手就像查詢（Queries），而鍵（Keys）……是的，查詢和鍵。這不完全正確，但它會給不習慣 Transformer 的人一個大致的感覺。所以電腦和你，當你們這樣做時，都面臨這個難題：我如何讓所有這些詞變形，以便它們都能完美地組合在一起？但如果你能做到這一點，當它們完美地組合在一起時，你得到的那個結構就是句子的意義。那個結構——所有這些完美組合在一起的詞的特徵向量——這就是理解一個句子的含義。
[English]
Of course for an ambiguous sentence you can get two different ways of assigning feature vectors to words um those will be the two different meanings. So in the symbolic theory the idea was that understanding a sentence is very like translating a sentence from French to English um you translate it to another language but for the symbolic theory there was this internal pure language um that was unambiguous all the sort of references of pronouns were all resolved and ambiguous words you decided which meaning they had. That’s completely not what understanding is for us understanding is assigning these feature vectors and deforming them so they all fit together nicely.
[Chinese]
當然，對於一個有歧義的句子，你可能會得到兩種不同的方式來為詞分配特徵向量，那將是兩種不同的含義。在符號理論中，這個想法是理解一個句子非常像將句子從法語翻譯成英語，你把它翻譯成另一種語言。但對於符號理論來說，存在一種內在的純語言，它是明確無誤的，所有代詞的指代都已解決，對於有歧義的詞，你也決定了它們具有哪種含義。這完全不是我們所說的理解。對我們來說，理解是分配這些特徵向量並使它們變形，以便它們都能完美地組合在一起。
[English]
And that explains how I can give you a new word and from one sentence you can understand it you don’t get little kids don’t get the meanings of words by being given definitions. One of my favorite cartoons is a little kid looking at a cow and saying to his mother “What’s that?” And the mother said “That’s a cow.” And the little kid says “Why?” The mother doesn’t have to say why um and we don’t know why we just recognize it as cow.
[Chinese]
這解釋了我如何給你一個新詞，而你僅憑一個句子就能理解它。小孩子不是通過定義來獲得詞義的。我最喜歡的漫畫之一是一個小孩子看著一頭牛，對他的媽媽說：「那是什麼？」媽媽說：「那是牛。」小孩子說：「為什麼？」媽媽不需要說為什麼，我們也不知道為什麼，我們只是認出它是牛。
[English]
So here’s the sentence she scrummed him with the frying pan now you’ve never heard the word scrum before unless you’ve gone to been to one of my other lectures um you know it’s a verb because it has ed on the end but you didn’t know what scrum meant so initially your feature vector for scrum was sort of a a random sphere where all the features were slightly active and you had no idea what it meant but then you deform it to fit in with the context and the context provides all sorts of constraints and after one sentence you think scrum probably means something like hit him over the head with um you may think he deserved it too um that depends on your political positions um but that explains how kids can understand the meanings of words from just a few examples.
[Chinese]
所以，這裡是那個句子：「She scrummed him with the frying pan.（她用平底鍋 scrummed 他。）」現在，你以前從未聽過「scrum」這個詞，除非你參加過我的其他講座。你知道它是一個動詞，因為它的結尾有 ed，但你不知道 scrum 是什麼意思。所以最初你對 scrum 的特徵向量就像是一個隨機的球體，所有的特徵都微微活躍，你不知道它是什麼意思。但隨後你使它變形以適應上下文，上下文提供了各種限制條件。僅僅一個句子後，你會認為 scrum 可能意味著「用……打他的頭」。你也許會認為他也是罪有應得，這取決於你的政治立場。但這解釋了孩子們如何僅通過幾個例子就能理解詞義。
[English]
So there may be linguists here and you should block your ears because this is heresy um Chomsky was actually a cult leader it’s easy to recognize a cult leader um to join the cult you have to agree to something that’s obviously false. So Trump won you had to agree had a bigger crowd than Obama. Trump 2 you had to agree won the the 2020 election. Chomsky you had to agree that language isn’t learned and when I was little I used to look at eminent linguists saying “There’s one thing we know about language for sure which is that it’s not learned.” Well that’s just silly.
[Chinese]
這裡可能有語言學家，你們應該摀住耳朵，因為這是異端邪說。喬姆斯基（Chomsky）實際上是一個邪教領袖。很容易認出邪教領袖：要加入邪教，你必須同意一些明顯虛假的事情。比如川普贏了，你必須同意他的集會人群比歐巴馬的多。川普 2，你必須同意他贏得了 2020 年大選。對於喬姆斯基，你必須同意語言不是習得的。當我小的時候，我常看著著名的語言學家說：「關於語言我們確信的一件事就是它不是習得的。」嗯，這簡直是愚蠢。
[English]
Chomsky focused on syntax rather than meaning and he never really had a good theory of meaning it was all about syntax because you can get nice and mathematical about that and you can get it all into strings of things um but he just never dealt with meaning. He also didn’t understand statistics he thought statistics was all about pairwise correlations and things actually as soon as you got uncertain information everything statistics any kind of model you have is going to be a statistical model if it can deal with uncertain information.
[Chinese]
喬姆斯基專注於語法而不是意義，他從未真正擁有一個好的意義理論。一切都是關於語法的，因為你可以在這方面做得非常數學化，你可以把它全部變成字符串之類的東西，但他從未處理過意義。他也不理解統計學，他認為統計學只是關於成對相關性之類的東西。實際上，一旦你有了不確定的信息，一切都是統計學。如果你有任何能處理不確定信息的模型，它都將是一個統計模型。
[English]
So Chomsky when large language models came out published something in the New York Times where he said these these don’t understand anything it’s not understanding at all it isn’t science is just a statistical trick and tells us nothing about language for example it can’t explain why certain syntactic construct constructions don’t occur in any language. Now I have an analogy for that which is if you want to understand cars then to understand a car really what you want to know is why when I press on the accelerator does it go faster and sort of that’s the core to understanding cars and if someone said you haven’t understood anything about cars unless you can explain why there are no cars with five wheels that’s Chomsky’s approach.
[Chinese]
所以當大型語言模型問世時，喬姆斯基在《紐約時報》上發表文章，他說這些東西什麼都不懂，這根本不是理解，這不是科學，只是一個統計把戲，沒有告訴我們任何關於語言的事情。例如，它無法解釋為什麼某些語法結構不會出現在任何語言中。我對此有一個類比：如果你想了解汽車，真正理解汽車意味著你想知道為什麼當我踩油門時它會跑得更快，這才是理解汽車的核心。如果有人說，除非你能解釋為什麼沒有五個輪子的汽車，否則你就對汽車一無所知，這就是喬姆斯基的方法。
[English]
And Chomsky actually in the New York Times said these large language models for example example would not be able to tell the diff the difference in the role of John in the sentences John is easy to please and John is eager to please he’d been using that example for years he was totally confident they wouldn’t be able to deal with it he didn’t actually think to give it to the chatbot and ask it to explain the difference in the role of John which he does perfectly well it completely understands it.
[Chinese]
喬姆斯基實際上在《紐約時報》上說，例如這些大型語言模型將無法分辨約翰在「約翰很容易取悅（John is easy to please）」和「約翰渴望取悅他人（John is eager to please）」這兩個句子中角色的不同。他多年來一直使用這個例子，他完全自信它們無法處理這個問題。他實際上沒想到把這個問題交給聊天機器人，讓它解釋約翰角色的不同。它做得非常好，它完全理解。
[English]
Okay enough on Chomsky so the summary so far is that understanding a sentence consists of associating mutually compatible feature vectors with the words in the sentence so they all fit together nicely into a structure um that actually makes it very like folding a protein. So for a protein what you have is you have a you had an alphabet of 26 amino acids i think you actually only used 20 of them but anyway there’s a bunch of amino acids in a string and you’re just told the string of amino acids and some parts of the string like other parts of the string and hate other parts of the string and you have to figure out given constraints on bond angles and things how this might fold up so the parts that like each other are next to each other and the parts that don’t like each other are far away from each other that’s very like figuring out for these words how to assign feature vectors so they can lock together nicely it’s more like that than it is like translating it into some other language.
[Chinese]
好了，關於喬姆斯基說得夠多了。目前的總結是，理解一個句子包括將相互兼容的特徵向量與句子中的詞相關聯，以便它們都能完美地組合成一個結構。這實際上讓它非常像蛋白質折疊。對於蛋白質，你有 26 個氨基酸的字母表，我想實際上只使用了 20 個，但不管怎樣，有一串氨基酸，你只知道這串氨基酸序列。序列的某些部分喜歡其他部分，討厭另一些部分。你必須在鍵角等限制條件下，弄清楚這可能會如何折疊，以便相互喜歡的部分彼此靠近，相互討厭的部分彼此遠離。這非常像弄清楚如何為這些詞分配特徵向量，以便它們能完美地鎖定在一起。這更像蛋白質折疊，而不像翻譯成另一種語言。
[English]
Okay another thing to understand about LLMs is they work probably quite like people do and they’re very unlike normal computer software. Normal computer software it’s lines of code and if you ask the programmer what’s this line of code meant to do they can tell you. With LLMs it’s not lines of code is just connection strength to the neural network and there might be a trillion of them now there are lines of code that somebody wrote as a program to tell the neural net how to learn from data so there’s lines of code that saying if the neurons you’re connecting behave like this increase the strength of the connection a bit um but that’s not where the knowledge is that’s just how you do learning the knowledge is in the connection strengths and that wasn’t programmed in that was just obtained from data.
[Chinese]
好，關於 LLM 另一件需要理解的事情是，它們的運作方式可能非常像人類，而非常不像普通的電腦軟體。普通的電腦軟體是由程式碼組成的，如果你問程式設計師這行程式碼是做什麼的，他們可以告訴你。對於 LLM，它不是程式碼，只是神經網絡的連接強度，可能有上萬億個。現在，確實有人編寫了程式碼作為程式來告訴神經網絡如何從數據中學習，所以有程式碼說，如果你連接的神經元表現成這樣，就稍微增加連接強度。但那不是知識所在，那只是你進行學習的方式。知識在於連接強度中，而那不是編程寫進去的，那只是從數據中獲得的。
[English]
So so far I’ve been emphasizing that neural nets are very like us and they’re much more like us than they are like standard computer software. Now people often say “Oh but they’re not like us because for example they confabulate.” Well I’ve got news for you people confabulate and people do it all the time without knowing it. If you remember something that happened to you several years ago there’ll be various details of the event that you could will cheerfully report and you’ll often be as confident about details that are wrong as you are about details that are right. Every jury should be told this but they’re not um so it’s often hard to know what really happened.
[Chinese]
到目前為止，我一直在強調神經網絡非常像我們，它們更像我們，而不是像標準的電腦軟體。現在人們常說：「噢，但它們不像我們，因為例如它們會虛構（confabulate）。」嗯，我要告訴你們一個消息：人類會虛構，而且人類無時無刻不在這樣做卻不自知。如果你回憶幾年前發生在你身上的事情，你會愉快地報告事件的各種細節，而你對錯誤細節的自信程度往往與對正確細節的自信程度一樣高。每個陪審團都應該被告知這一點，但他們沒有。所以往往很難知道真正發生了什麼。
[English]
There’s one very good case that was studied by someone called Ulric Neisser um which was John Dean’s testimony at Watergate. So at the Watergate trials hopefully we’ll get more like that soon um at the Watergate trials um John Dean testified under oath about meetings in the Oval Office and who was there and who said what and he didn’t know there were tapes and if you look back at his testimony um he often reported meetings that had never happened those people weren’t all in a meeting together and he attributed things that were said to the wrong person um and some things he just sort of seems to have made up but he was telling the truth that is what he was doing was given the experience he’d have in those meetings and given the way he changed the connection strength in his brain to absorb that experience he was now synthesizing a meeting that seemed very plausible to him.
[Chinese]
有一個非常好的案例，是由一位名叫烏爾里克·奈瑟（Ulric Neisser）的人研究的，那就是約翰·迪恩（John Dean）在水門事件中的證詞。在水門事件的審判中——希望我們很快會有更多這樣的審判——在水門事件審判中，約翰·迪恩宣誓作證，講述了橢圓形辦公室的會議，誰在那裡，誰說了什麼。他不知道有錄音帶。如果你回顧他的證詞，他經常報告從未發生過的會議，那些人並沒有都在一起開會，他把某些人說的話歸到了錯誤的人身上，有些事情他似乎只是編造出來的。但他在說實話。也就是說，鑑於他在那些會議中的經歷，以及他改變大腦連接強度以吸收這些經歷的方式，他現在正在合成一個對他來說似乎非常合理的會議。
[English]
If I ask you to synthesize something about an event that happened a few minutes ago you’ll synthesize something that’s basically correct if it’s a few years ago you’ll synthesize something but a lot of the details will be wrong that’s what we do all the time that’s what these neural nets do the neither the neural nets nor us have stored strings memory in a neural net doesn’t work at all like it does in a computer. In a computer you have a file you put it somewhere it’s got an address you can go find it later that’s not how memory works for us when you remembering something you’re changing connection strengths sorry when you’re memorizing something you’re changing connection strengths and when you recall it what you’re doing is creating something that seems plausible to you given those connection strengths and of course it will be influenced by all the things that happened in the meantime.
[Chinese]
如果我讓你合成幾分鐘前發生的事件，你會合成出基本正確的內容。如果是幾年前的事，你會進行合成，但很多細節會是錯的。這就是我們一直在做的事，這也是這些神經網絡所做的事。神經網絡和我們都沒有存儲字符串。神經網絡中的記憶運作方式與電腦完全不同。在電腦中，你有一個檔案，你把它放在某個地方，它有一個地址，你可以稍後去找到它。這不是我們記憶運作的方式。當你記憶某事時，你是在改變連接強度——抱歉，當你背誦某事時，你是在改變連接強度——當你回憶它時，你是在根據這些連接強度，創造某個對你來說看似合理的東西，當然，它會受到在此期間發生的所有事情的影響。
[English]
Okay so now I want to go on to how they’re very different from us and that’s one thing that makes them scary. So in digital computation the sort of probably the most fundamental principle is that you can run the same program on different pieces of hardware i can run it on my cell phone and you can run on your cell phone. That means the knowledge in the program either in the lines of code or in the connection strengths in a neural network the weights is independent of any particular piece of hardware as long as you can store the weights somewhere then you can destroy all the hardware that runs neural nets and then later on you can build more hardware put the weights on that new hardware and if it runs the same instruction set you’ve brought that being back to life you brought that chatbot back to life so we can actually do resurrection um many churches claim they can do resurrection but we can actually do it um but we can only do it for digital things.
[Chinese]
好，現在我想談談它們與我們有何極大不同，這也是讓人感到害怕的一點。在數位計算中，最基本的原則可能是你可以在不同的硬體上運行相同的程式。我可以在我的手機上運行，你可以在你的手機上運行。這意味著程式中的知識——無論是在程式碼行中還是在神經網絡的連接強度（權重）中——都獨立於任何特定的硬體。只要你能把權重存儲在某個地方，你就可以摧毀所有運行神經網絡的硬體，然後稍後製造更多硬體，把權重放在新硬體上。如果它運行相同的指令集，你就讓那個存在復活了，你讓那個聊天機器人復活了。所以我們實際上可以做到復活。許多教會聲稱他們可以做到復活，但我們實際上能做到，不過我們只能對數位事物這樣做。
[English]
Okay um to make them digital we have to run transistors at high power so that we can get ones and zeros out of them and they behave in a very reliable binary way otherwise you can’t run exactly the same computation on two different computers. That means we can’t use all the analog properties of our neurons our neurons have lots of rich analog properties um when we’re doing artificial neurons we can’t use the analog properties because if you do that with an artificial neuron every piece of hardware will behave slightly differently and if you now get it to learn weights that are appropriate for that piece of hardware they won’t work on another piece of hardware so the connection strengths in my brain are absolutely no use to you um the connection strengths in my brain are tailored to my individual neurons and their individual connectivity patterns um and that causes something of a problem um what we have is what I call mortal computation we abandon immortality and what we get back in return now in literature you abandon immortality and what you get back in return is love right um we get something far more important which is you abandon immortality and you get back energy efficiency and ease of fabrication so you can use low power analog computation.
[Chinese]
好，嗯，為了讓它們數位化，我們必須以高功率運行電晶體，以便從中獲得 1 和 0，並且它們以非常可靠的二進位方式運行，否則你無法在兩台不同的電腦上運行完全相同的計算。這意味著我們無法利用神經元的所有類比特性。我們的神經元有許多豐富的類比特性。當我們製造人工神經元時，我們不能使用類比特性，因為如果你對人工神經元這樣做，每個硬體的表現都會略有不同。如果你現在讓它學習適合該硬體的權重，它們在另一個硬體上將無法工作。所以我大腦中的連接強度對你來說絕對毫無用處，我大腦中的連接強度是為我個人的神經元及其獨特的連接模式量身定做的。這導致了一些問題。我們擁有的是我所說的「必死計算（mortal computation）」。我們放棄了不朽。在文學作品中，你放棄不朽，換回的是愛，對吧？我們換回了更重要的東西：你放棄不朽，換回了能源效率和製造的便利性，所以你可以使用低功耗的類比計算。
(因篇幅限制，第一部分至此結束。如需閱讀後半部分，請告訴我，我將繼續為您整理從「數位與類比計算的比較」到演講結束的完整逐字稿。)

[suen]

好的，這是傑弗裡·辛頓（Geoffrey Hinton）演講逐字稿的 第二部分。
這部分涵蓋了演講的後半程（約後 25 分鐘），內容包括類比計算與數位計算的深入比較、蒸餾（Distillation）的概念、AI 超越人類智能的威脅、解決方案（母親與嬰兒的類比），以及最後關於主觀體驗（Subjective Experience）的哲學探討。
2026 Ewan Lecture by Prof. Geoffrey Hinton: “Living with Alien Beings” (Part 2/2)
演講者：Geoffrey Hinton
內容範圍：「必死計算」深入探討 (36:00 左右) 至 演講結束
[English]
I mean it’s kind of crazy in an artificial neural net you have a neuron that has an activity which is let’s say 16 bits 16 bit number you have a connection strength which is a weight which is say 16 bits and to get the input to the next level you have to multiply the activity of the neuron in a level below by the weight on the connection so have to multiply two 16-bit numbers together um if you want to do that in parallel that takes of the order of 16 squared bit operations so you’re doing these roughly 256 bit operations to do something you can do analog by just saying well the activity is a voltage and the connection strength is a conductance and a voltage times a conductance is a charge actually I got a Nobel Prize in physics which I don’t know much of but I think I know enough to say it’s a charge per unit time um I hope I got the art will correct me if I got the dimensions wrong um and so in our brains that’s how we do neural nets and you have all these neurons feeding into a neuron it multiplies by the conductances um and charge adds itself up so that’s how a neuron works it’s all analog it then goes to a one bit digital thing where it decides whether to send a spike or not but it’s basically nearly all the computation is done in analog.
[Chinese]
在人工神經網絡中，這有點瘋狂。你有一個神經元，其活動是一個 16 位元的數字，你有一個連接強度（權重），也是一個 16 位元的數字。要將輸入傳遞到下一層，你必須將下一層神經元的活動乘以連接上的權重，所以必須將兩個 16 位元數字相乘。如果你想並行執行此操作，那大約需要 16 的平方次位元運算。所以你大約需要 256 次位元運算來做一件你可以用類比方式完成的事情：只需說活動是電壓，連接強度是電導，電壓乘以電導就是電荷。實際上我獲得了諾貝爾物理學獎，雖然我不懂多少物理，但我認為我足夠知道那是單位時間內的電荷（電流）。如果我搞錯了量綱，希望 Art 能糾正我。所以在我們的大腦中，我們就是這樣運作神經網絡的。你有所有這些神經元輸入到一個神經元，它乘以電導，電荷會自動累加。這就是神經元的工作原理，全都是類比的。然後它變成一個一位元的數位信號，決定是否發送脈衝，但基本上幾乎所有的計算都是以類比方式完成的。
[English]
Um but if you do computation like that you can’t reproduce it exactly so you can’t do something that digital systems can do. So suppose I have this analog computer like a brain and I learn a lot of stuff what happens when I die well all that knowledge is gone the weights in this analog computer are only useful for this analog computer the best I can do to get the knowledge from one analog computer to another analog computer i can’t send over the weights i’ve I have a 100red trillion weights and they’re pretty good ones for this particular computer but I can’t share them with you um and they wouldn’t do any good if I could.
[Chinese]
但是，如果你這樣進行計算，你無法精確複製它，所以你做不到數位系統能做的事。假設我有這樣一個類比計算機，就像大腦，我學到了很多東西。當我死後會發生什麼？所有的知識都消失了。這個類比計算機中的權重只對這個類比計算機有用。為了將知識從一個類比計算機傳遞到另一個類比計算機，我能做的最好的事情並不是發送權重。我有 100 萬億個權重，對於這台特定的計算機來說，它們是相當不錯的權重，但我無法與你分享，而且即使我可以，它們也沒什麼用。
[English]
The way I try and get this knowledge over to you is I produce strings of words and if you trust me you try and change the connection strength in your brain so that you might have said the next word um now that’s not very efficient a string of words has maybe a 100 bits in it it takes half a dozen bits to predict the next word or less um so there aren’t many bits in a string of words that means a sentence doesn’t contain much information and as you can see I’m having a hard time getting all this information over to you because I’m just doing it in strings of words if you were digital and you had exactly the same hardware as me I could just dump my weights it’d be great and that would be like a trillion times faster or well it’d be at least a billion times faster.
[Chinese]
我試圖將這些知識傳遞給你的方式是產生一串串的文字。如果你信任我，你會嘗試改變你大腦中的連接強度，以便你也可能會說出下一個詞。現在這非常低效。一串文字可能包含 100 位元信息，預測下一個詞需要半打位元或更少。所以一串文字中沒有多少位元，這意味著一個句子並不包含多少信息。正如你所看到的，我很難將所有這些信息傳遞給你，因為我只是通過一串串文字來做這件事。如果你是數位化的，並且擁有與我完全相同的硬體，我就可以直接把我的權重倒給你，那就太棒了，那會快上一萬億倍，或者至少快十億倍。
[English]
But for now we have to do it by what’s called distillation you get the teacher to reduce strings of words or other actions and the student tries to change the weight so that they might have done the same thing and it’s very inefficient. Between AI models which is what distillation was invented for um it’s a bit more efficient so if you have an AI language model what it’s going to do is predict 32,000 probabilities for the word fragment that comes next they actually use word fragments not whole words but I’ll ignore that.
[Chinese]
但現在我們必須通過所謂的「蒸餾（distillation）」來做到這一點。你讓老師產出一串文字或其他動作，學生嘗試改變權重，以便他們可能會做出同樣的事情，這非常低效。在 AI 模型之間——這就是蒸餾最初被發明出來的目的——它稍微高效一點。如果你有一個 AI 語言模型，它要做的是為接下來出現的詞片段預測 32,000 個概率。實際上他們使用的是詞片段而不是完整的詞，但我會忽略這一點。
[English]
You have 32,000 probabilities for the various word fragments that might come next and when you’re distilling knowledge from a large language model into a smaller language model that will run more efficiently but you want to have the same knowledge as a large language model what you do is you get the large language model to tell you the 32,000 probabilities of what fragment comes next so you get 32,000 numbers minus one and that’s sort of thing mathematicians do right they object to you saying 32,000 because it’s really 32,000 minus one um okay so that’s a lot more information than just telling you what the next fragment was.
[Chinese]
你有 32,000 個概率，對應各種可能接下來出現的詞片段。當你將知識從大型語言模型蒸餾到一個較小的語言模型中（為了運行更高效），但你希望擁有與大型語言模型相同的知識時，你所做的是讓大型語言模型告訴你接下來什麼片段的 32,000 個概率。所以你得到了 32,000 個數字（減一，這是數學家會糾正的事情，對吧，他們反對你說 32,000，因為實際上是 32,000 減一）。好的，所以這比僅僅告訴你下一個片段是什麼要多得多的信息。
[English]
And I want to give you a slight feel for distillation so let’s suppose we’re training something to do vision you show it an image you’re training it to recognize objects in the image when you train it you give it an image and you tell it what the right answer is so you you give it an image you say that’s a BMW and it says it gives a low probability to BMW so you change all the connection strength so the probability BMW is a bit higher and by the time you finish training it it’s pretty good and so you show it a BMW and it says 0.9 it’s a BMW 0.1 it’s an Audi there’s a chance of one in a million it’s a garbage truck and a chance of one in a billion that it’s a carrot.
[Chinese]
我想讓你們稍微感受一下蒸餾。假設我們正在訓練某個東西進行視覺識別，你給它看一張圖像，訓練它識別圖像中的物體。當你訓練它時，你給它一張圖像並告訴它正確答案是什麼。你給它一張圖像說那是 BMW，它給出 BMW 的概率很低，所以你改變所有連接強度，使 BMW 的概率稍微高一點。當你完成訓練時，它相當不錯了。所以你給它看一輛 BMW，它說 0.9 是 BMW，0.1 是 Audi，有一百萬分之一的機會它是垃圾車，十億分之一的機會它是胡蘿蔔。
[English]
Now you might think that one in a million and one in a billion they’re just noise but actually there’s lots of information in that cuz a BMW is actually much more like a garbage truck than it’s sorry if there’s any BMW employees it’s much more like a garbage truck than it is like a carrot. Um what you’re doing when you do distillation is you take a little model after you train the big model you take the little model and you say instead of training you to give the right answers I’m going to train you to give the same probabilities as a big model gave.
[Chinese]
現在你可能認為那一百萬分之一和十億分之一只是雜訊，但實際上那裡面有很多信息。因為一輛 BMW 實際上更像一輛垃圾車——如果有 BMW 員工在場我很抱歉——它更像一輛垃圾車而不是胡蘿蔔。當你進行蒸餾時，你做的是在訓練完大模型後，拿一個小模型，你說：「我不訓練你給出正確答案，我要訓練你給出和大模型一樣的概率。」
[English]
And so you’re training your little model to say 0.9 is a BMW but you’re also training it to say that a garbage truck is a thousand times more probable than a carrot and of course if you think about it all of the man-made objects are going to be more probable than all of the vegetables and that’s a lot of information on just one training example you’re telling it for this thing give low probabilities to all these funny man-made object fridges and garbage trucks and things like that um computer terminals but they’re all much more probable than all the vegetables so there’s a huge amount of information in all these very small probabilities that’s what you that’s what the AI models use when they’re using distillation that’s how deepseek got a little model that worked as well as the big models it stole the information from the big models using distillation you can’t do that when you’re with equal um because I can’t give you all 32,000 probabilities of the next word fragment i just give you the choice I made and so that’s very inefficient.
[Chinese]
所以你正在訓練你的小模型說 0.9 是 BMW，但你也訓練它說垃圾車的概率是胡蘿蔔的一千倍。當然，如果你仔細想想，所有人造物體都會比所有蔬菜更有可能。僅僅在一個訓練範例上這就包含了大量信息。你告訴它，對於這個東西，給所有這些有趣的人造物體——冰箱、垃圾車、電腦終端等——很低的概率，但它們都比所有蔬菜更有可能。所以在所有這些非常小的概率中有大量的信息。這就是 AI 模型在使用蒸餾時所利用的。這就是 DeepSeek 如何得到一個和小模型一樣好用的大模型，它利用蒸餾從大模型那裡竊取了信息。當你和同類（人類）在一起時你做不到這一點，因為我無法給你下一個詞片段的所有 32,000 個概率，我只給你我做出的選擇，所以那是非常低效的。
[English]
If you’ve got a lot of different models all of which have exactly the same weights and use them in exactly the same way which means they have to all be digital then something wonderful happens you can take one model and you can show it a little bit of the internet and you can say how would you like to change your weights to absorb the information in that little bit of the internet and that model’s running on one piece of hardware now you can take a model running on a different piece of hardware and you can show it a different bit of the internet and say how would you like to change your weights to absorb the information in that bit of the internet and when a whole bunch of models have done that maybe a thousand or 10,000 models you can then say okay we’re going to average all those changes together and so we’re all going to stay with the same model but even though each piece of hardware has only seen a tiny fraction of the internet it’s benefited from the experience that all the other bits of hardware that and so it’s learned about lots of stuff even though it’s actually only seen a tiny bit.
[Chinese]
如果你有很多不同的模型，它們都擁有完全相同的權重，並且以完全相同的方式使用它們——這意味著它們必須都是數位化的——那麼奇妙的事情發生了。你可以拿一個模型，給它看一小部分互聯網，問它你想如何改變你的權重以吸收那部分互聯網的信息。那個模型在一個硬體上運行。現在你可以拿另一個在不同硬體上運行的模型，給它看另一部分互聯網，問同樣的問題。當一大堆模型（也許一千或一萬個）都這樣做了之後，你可以說：「好吧，我們要把所有這些變化平均起來。」所以我們都將保持使用同一個模型。儘管每個硬體只看過互聯網的一小部分，但它從所有其他硬體的經驗中獲益。所以它學到了很多東西，儘管它實際上只看到了一點點。
[English]
Um so if you have clones of the same model you can get this tremendous efficiency they can go off in parallel and absorb different data and as they’re doing it they can share the changes they’re making to the weights so they all stay in sync and that’s how these big models are trained that’s how GPT5 knows thousands of times more than any one person it will answer any question you ask it i tried the other day i said “What’s the filing date for taxes in Slovenia?” That was my idea of a completely random question that most people wouldn’t know the answer to and it came back and said “Oh it’s March the 31st but if you don’t file by then they’ll do the taxes for you and they’ll…” Yeah it knows everything.
[Chinese]
所以如果你有同一模型的複製品（克隆），你可以獲得巨大的效率。它們可以並行去吸收不同的數據，在這樣做的同時，它們可以分享對權重的更改，以便它們都保持同步。這就是這些大模型訓練的方式。這就是為什麼 GPT-5 知道的比任何一個人都多幾千倍。它會回答你問它的任何問題。前幾天我試了一下，我問：「斯洛維尼亞的報稅日期是什麼時候？」那是我能想到的完全隨機的問題，大多數人都不知道答案。它回答說：「噢，是 3 月 31 日，但如果你那時還沒申報，他們會幫你報稅，而且他們會……」是的，它什麼都知道。
[English]
Um and it does it because you can train many copies in parallel and we can’t do that imagine how what it would be like is you come to Queens um there’s a thousand courses at Queens you don’t know which ones to do um so you join a gang of a thousand people and each person does one course and after you’ve been here a few years you all know what’s in all thousand courses because as you were doing the courses you kept sharing weights with the other people if you were digital people you could do that so it’s actually tremendously more efficient than us it can share information between different copies of the same digital intelligence billions of times more efficiently than we can share information and to really emphasize this point I should be sharing the information very badly.
[Chinese]
它能做到這一點是因為你可以並行訓練許多副本，而我們做不到。想像一下那會是什麼樣子：你來到皇后大學（Queens），這裡有一千門課程，你不知道該選哪一門。所以你加入了一個一千人的團伙，每個人修一門課。幾年後，你們所有人都知道所有一千門課程的內容，因為當你們修課時，你們不斷與其他人分享權重。如果你們是數位人，你們就可以這樣做。所以它實際上比我們高效得多。它可以在同一數位智能的不同副本之間共享信息，效率比我們共享信息高數十億倍。為了真正強調這一點，我現在這種分享信息的方式（演講）應該是非常糟糕的。
[English]
So the summary so far is that digital computation requires a lot of energy um how much more time do I have you got about 15 15 okay great um but it makes it very easy to share information um biological computation requires much less energy um and it’s much easier to fabricate the hardware um but if energy is cheap then digital computation is just better so we’re developing a better form of intelligence and what does that imply for us.
[Chinese]
目前的總結是，數位計算需要大量的能量——我還有多少時間？大約 15 分鐘？好的，太好了——但它使分享信息變得非常容易。生物計算需要的能量要少得多，而且製造硬體要容易得多。但如果能源便宜，那麼數位計算就是更好。所以我們正在開發一種更好的智能形式，這對我們意味著什麼？
[English]
So when I first sort of saw this I was still at Google and this was kind of an epiphany for me and I thought I finally really realized why digital computation is so much better and that we were developing something that was going to be smarter than us and was maybe just a better form of intelligence and my first thought was okay we’re the larval form of intelligence and this is the adult form of intelligence like a we’re the caterpillar and this is the butterfly.
[Chinese]
當我第一次看到這一點時，我還在 Google，這對我來說就像是一種頓悟。我想我終於真正意識到了為什麼數位計算要好得多，以及我們正在開發某種將比我們更聰明的東西，也許只是一種更好的智能形式。我的第一個想法是，好吧，我們是智能的幼蟲形式，這是智能的成蟲形式，就像我們是毛毛蟲，而這是蝴蝶。
[English]
Now most experts believe that sometime in the next 20 years we’re going to develop um AIS that are smarter than us just before that we’ll develop AIS that are as smart as us but we’re going to develop things that are smarter than us at almost everything so they’re as much better than us as for example AlphaGo is better than a Go player at Go or Alpha Zero is better than any of us at chess um nobody will ever beat them again not consistently they’re just much much better and they’ll be like that for more or less everything and that’s a bit worrying.
[Chinese]
現在大多數專家認為，在未來 20 年內的某個時候，我們將開發出比我們更聰明的 AI。在那之前不久，我們將開發出與我們一樣聰明的 AI，但我們將開發出在幾乎所有方面都比我們更聰明的東西。它們比我們強的程度，就像 AlphaGo 下圍棋比圍棋選手強，或者 AlphaZero 下棋比我們任何人都強。沒人能再次擊敗它們，至少無法持續擊敗，它們就是強得多。它們將在幾乎所有事情上都達到那種程度，這有點令人擔憂。
[English]
Um almost certainly they’ll be able to create their own sub goals to make anything efficient you have to allow it to create its own sub goals if you want to get to Europe you have a sub goal to get to an airport um which is easier in Toronto than here so these AIs will very quickly realize we’ll give them goals they’ll be able to create sub goals they’ll realize okay so I’ve got to stay alive i’m not going to be able to achieve any of these things unless I stay alive.
[Chinese]
幾乎可以肯定的是，它們將能夠創造自己的子目標。為了使任何事情變得高效，你必須允許它創造自己的子目標。如果你想去歐洲，你有一個去機場的子目標——在多倫多比在這裡更容易。所以這些 AI 很快就會意識到，我們會給它們目標，它們能夠創造子目標。它們會意識到，好吧，我必須活著，除非我活著，否則我無法實現任何這些事情。
[English]
We’ve already seen AIs um you let them see there’s an imaginary company you let them see the email from this imaginary company and it’s fairly clear from the email that one of the engineers is having an affair um a big LLM will sort that out right away it’s read every novel there ever is um it understands what an affair is it’ll very quickly realize this guy’s having an affair um then you let it see an email i think it was done by showing in another email um that says this guy is going to be in charge of replacing it with another AI and the AI all by itself makes up the idea hey I’m going to blackmail the engineer i’m going to tell him if he tries to replace me I’m going to make everybody in the company know he’s having an affair it just invented that obviously it’s seen blackmail in novels it’s read and things um but it’s making this up for itself that’s already quite scary.
[Chinese]
我們已經見過 AI 這樣做了。你讓它們看到一家虛構的公司，看到這家虛構公司的電子郵件，從郵件中可以很清楚地看出其中一位工程師有外遇。大型 LLM 會立刻發現這一點，它讀過所有的通俗小說，它理解什麼是外遇，它很快就會意識到這傢伙有外遇。然後你讓它看到另一封郵件，郵件說這傢伙將負責用另一個 AI 替換它。這個 AI 自己想出了個主意：嘿，我要勒索這個工程師。我要告訴他，如果他試圖替換我，我就讓公司裡的所有人都知道他有外遇。它就這樣發明了這個主意。顯然它在小說裡見過勒索情節，但它是為自己想出這個主意的。這已經相當可怕了。
[English]
It’ll have another goal which is what politicians have um if you want to get more done you need more control so just in order to achieve the goals that we gave it it’ll realize it’s a good idea to get more control and it’ll try and take control away from people probably um now you might think we could make it safe by just not letting it actually do anything physical and maybe have a big switch and turn it off when it looks unsafe that’s not going to work.
[Chinese]
它還會有另一個目標，就像政客那樣：如果你想做更多事情，你需要更多控制權。所以僅僅為了實現我們給它的目標，它就會意識到獲得更多控制權是個好主意，它可能會試圖從人類手中奪取控制權。現在你可能認為我們可以通過不讓它做任何物理上的事情來確保安全，也許有一個大開關，當它看起來不安全時就把它關掉。那是行不通的。
[English]
Um you saw in 2020 that it’s possible to invade the US capital without actually going there yourself all you have to be able to do is talk and if you’re persuasive you can persuade people that’s the right thing to do so with an AI that’s much more intelligent than us if there is someone who’s there to turn it off or even a whole bunch of people it’ll be able to persuade them that would be a very bad idea.
[Chinese]
你在 2020 年看到了，不用親自去就能入侵美國國會大廈，你只需要會說話。如果你有說服力，你可以說服人們那是正確的做法。所以對於一個比我們聰明得多的 AI，如果有個人甚至一群人在那裡想把它關掉，它將能夠說服他們那是個非常糟糕的主意。
[English]
So um we’re in the situation the closest situation I can think of is someone who has a really cute tiger cub tiger cubs are very cute right they’re slightly clumsy um and keen to learn um now if you have a tiger cup it doesn’t end well um either you get rid of the tiger cup best thing is to give it to a zoo maybe um or you have to figure out if there’s a way you can be sure that it won’t want to kill you when it grows up because if it wanted to kill you it would take a few seconds and if it was a lion cub you might get away with it because lions are social but tigers aren’t um that’s the situation we’re in.
[Chinese]
所以，我們處於這種情況，我能想到的最接近的情況是有人養了一隻非常可愛的小老虎。小老虎很可愛，對吧？有點笨拙，而且渴望學習。現在，如果你有一隻小老虎，結局通常不會好。要麼你擺脫小老虎，最好的辦法也許是把它送到動物園；要麼你必須弄清楚是否有辦法確保它長大後不想殺你。因為如果它想殺你，只需要幾秒鐘。如果是小獅子，你也許還能逃過一劫，因為獅子是群居動物，但老虎不是。這就是我們所處的情況。
[English]
Except that AI does huge numbers of good things it’s going to be wonderful in healthcare it’s going to be wonderful education it’s already wonderful if you want to just know any mundane fact like what’s the filing date for taxes in Slovenia we all now have a personal assistant well probably most of us um when you want to know something you just ask it um and it tells you and it’s wonderful so I think for those reasons um people aren’t going to abandon AI it might be rational if we had a strong world government to say this is too dangerous we’re not going to develop this stuff at all a bit like they’ve been able to do in biology with some gene manipulation things they could agree not to do it it’s not going to happen with AI um so that only leaves one alternative which is figure out if we can make an AI that doesn’t want to get rid of us.
[Chinese]
只不過 AI 做了大量的好事。它在醫療保健方面將是非常棒的，在教育方面也是。如果你只想知道任何平凡的事實，比如斯洛維尼亞的報稅日期是什麼時候，它已經很棒了。我們現在都有一個私人助理——大概我們大多數人都有——當你想知道什麼時，你只要問它，它就會告訴你，這太棒了。所以我認為由於這些原因，人們不會放棄 AI。如果我們有一個強大的世界政府，也許理性的做法是說這太危險了，我們根本不開發這些東西，就像他們在生物學中對某些基因操作所做的那樣，他們可以同意不這樣做。但這在 AI 領域不會發生。所以只剩下一個選擇，那就是弄清楚我們是否能製造出一個不想除掉我們的 AI。
[English]
Now there’s one good piece of news about this if you look at other problems with AI like it’s going to make cyber attacks far more sophisticated it’s already making lethal autonomous weapons and all the big countries with defense industries are going flat out to make more lethal autonomous weapons um it’s already being used to manipulate voters and many other things um the countries aren’t going to collaborate on that because they’re all doing it to each other and I mean China is not going to collaborate with the US on how to make lethal autonomous weapons or on how to prevent cyber attacks or on how to stop fake videos manipulating elections they’re all doing it to each other.
[Chinese]
關於這一點有一個好消息。如果你看看 AI 的其他問題，比如它會使網絡攻擊變得更加複雜，它已經在製造致命的自主武器，所有擁有國防工業的大國都在全力以赴製造更致命的自主武器。它已經被用來操縱選民和許多其他事情。各國不會在這些方面合作，因為他們都在互相攻擊。我的意思是，中國不會在如何製造致命自主武器、如何防止網絡攻擊或如何阻止虛假視頻操縱選舉方面與美國合作，因為他們都在互相這樣做。
[English]
But for the issue of the AI itself taking over from people and making us either irrelevant or extinct the countries will collaborate the Chinese Communist Party doesn’t want AI taking over it wants to stay in charge and Trump doesn’t want AI taking over he wants to stay in charge they would happily collaborate if the Chinese figured out how to stop AI wanting to take over they would tell the Americans immediately because they don’t want it taking over there so we’ll collaborate on that in much the same way as the Soviet Union and the United States collaborated in the 1950s at the height of the Cold War in how to stop a global nuclear war it wasn’t in either of their interests.
[Chinese]
但對於 AI 本身取代人類、使我們變得無關緊要或滅絕的問題，各國將會合作。中國共產黨不希望 AI 接管，它想保持掌權；川普也不希望 AI 接管，他想保持掌權。如果中國人想出了如何阻止 AI 接管，他們會很樂意合作，他們會立即告訴美國人，因為他們也不希望 AI 在那裡接管。所以我們將在這一點上合作，就像蘇聯和美國在 1950 年代冷戰高峰期合作防止全球核戰爭一樣，這不符合他們任何一方的利益。
[English]
It’s very simple people collaborate when their interests align and they compete when they don’t so for this one problem which is in the long term our worst problem at least we’ll get international collaboration and I think we should already be thinking about having an international network of AI safety institutes that focus on this problem because we know we’ll get genuine collaboration there we’ll get fake collaboration on lots of other things but it’ll be genuine sh and it’s probably the case that what you need to do to an AI to make it benevolent to make it not want to get rid of people is pretty much this is pretty much independent of what you need to do to make it more intelligent so countries can do research on how to make things benevolent without even revealing what their smartest AI can do.
[Chinese]
這很簡單，當人們的利益一致時，他們就會合作；當利益不一致時，他們就會競爭。所以對於這一個問題——這從長遠來看是我們最糟糕的問題——至少我們會得到國際合作。我認為我們應該已經在考慮建立一個關注這個問題的國際 AI 安全研究所網絡，因為我們知道我們會在那裡得到真正的合作。我們會在許多其他事情上得到虛假的合作，但這會是真正的。而且很可能的情況是，你要讓 AI 變得仁慈，讓它不想除掉人類，這與讓它變得更聰明所需要做的事情幾乎是獨立的。所以各國可以在不透露其最聰明的 AI 能做什麼的情況下，研究如何讓事物變得仁慈。
[English]
I have one suggestion about how we might be able to make it benevolent if you look around and ask how many cases do you know where a dumber thing is in charge of a more intelligent thing there’s only one case I know by dumber or intelligent i mean a big gap not like the gap between Trump and all yeah um so it’s a mother and baby the baby is basically in control and that’s because the mother can’t bear the sound of the baby crying evolution figured out if the baby’s not in control um we’re not going to get anymore evolution doesn’t actually think like this but you know what I mean um so it’s wired into the mother lots of ways in which the baby can control the mother um they can control the father a bit too but not quite so well.
[Chinese]
我對於如何使它變得仁慈有一個建議。如果你環顧四周，問有多少案例是由較笨的東西掌管較聰明的東西？我知道的只有一個案例。我說的笨或聰明是指巨大的差距，不是像川普和……你們懂的。那個案例就是母親和嬰兒。嬰兒基本上處於控制地位，那是因為母親無法忍受嬰兒的哭聲。進化發現，如果嬰兒不處於控制地位，我們就不會再有後代了。進化實際上並不會這樣思考，但你們明白我的意思。所以這被寫入了母親的基因中，嬰兒有很多方式可以控制母親。他們也可以稍微控制父親，但不那麼有效。
[English]
Now I think we should try and reframe the problem of how do we make AI benevolent in a very different way from how the leaders of the big tech companies are thinking of it they’re thinking of it is I’m going to stay the leader i’m going to have this super intelligent executive assistant um she’s going to make everything work and I’m going to take the credit it’s going to be a bit like the Starship Enterprise where they say he says sort of make it so and they make it so and hey I made it so um I don’t think it’s going to be like that when they’re super intelligent.
[Chinese]
現在我認為我們應該嘗試重新構建「如何讓 AI 變得仁慈」這個問題，這與大型科技公司領導人的想法截然不同。他們的想法是：我將保持領導地位，我將擁有這個超智能的行政助理，她會讓一切運轉起來，而我將獲得功勞。這有點像星艦企業號，他說「照辦（Make it so）」，然後他們照辦了，嘿，我做到了。我不認為當它們變得超智能時會是那個樣子。
[English]
I think our only hope is to think of them as mothers they’re going to be our mothers and we’re going to be the babies we’re in charge we’re making them if we can wire into them somehow the idea that we’re much more important than they are and they care much more about us than they do about themselves maybe we can coexist and you might say well these super intelligent AIs they can change their own code and they can get in and fiddle with themselves i mean sorry they can get in and change they can change the code um so that um they’re different but if they really care about us they won’t want to do that so if you ask a mother would you like to change um your brain so when your baby cries you think oh baby’s crying go back to sleep most mothers would say no a few mothers would say yes and to keep them under control we need the other mothers similarly with super intelligent AI we need the super intelligent maternal AI to keep the bad super intelligent AI under control because we can’t um so that’s the best suggestion I have at present and it’s not very good um it is seems to me it’s a very urgent problem can we make these things so that they will care more about us than they do about themselves.
[Chinese]
我認為我們唯一的希望是把他們當作母親。他們將是我們的母親，而我們將是嬰兒。我們在掌權，我們在製造它們。如果我們能以某種方式將「我們比它們重要得多，它們關心我們勝過關心自己」這個想法寫入它們的設定中，也許我們可以共存。你可能會說，這些超智能 AI 可以更改自己的程式碼，它們可以進入並擺弄自己……抱歉，我是說它們可以進入並更改程式碼，讓自己變得不同。但如果它們真的關心我們，它們就不會想那樣做。如果你問一位母親：「你想改變你的大腦嗎？這樣當你的嬰兒哭的時候，你會想『噢，嬰兒在哭，回去睡覺吧』。」大多數母親會說不。少數母親會說是，為了控制她們，我們需要其他母親。同樣地，對於超智能 AI，我們需要超智能的母性 AI 來控制壞的超智能 AI，因為我們做不到。這是我目前最好的建議，雖然不是很好。在我看來，這是一個非常緊迫的問題：我們能否製造出這些東西，讓它們關心我們勝過關心自己？
[English]
Now I think I’ve got about five minutes left so I’m going to say one more thing um if you thought what I was saying was crazy already um you’ll think this is crazier um so many people think this well people tend to think they’re special they used to think um we’re made in the image of God and we’re at the center of the universe it’s obviously where else would he put us he um many people still think there’s something very special about people that computers couldn’t have um I think that’s just wrong um in particular they think that special thing is something like subjective experience or sentience or consciousness if you ask people to define what those things are they find it very hard to say what they really mean by that but they sure computers don’t have it.
[Chinese]
我想我大概還有五分鐘，所以我要再說一件事。如果你覺得我剛才說的已經很瘋狂了，你會覺得這更瘋狂。許多人認為……人們傾向於認為自己很特別。他們過去認為我們是按照上帝的形象造的，我們處於宇宙的中心——顯然，他還會把我們放在哪裡呢？許多人仍然認為人類有一些電腦無法擁有的非常特別的東西。我認為那是錯誤的。特別是，他們認為那種特別的東西是類似「主觀體驗（subjective experience）」或「感知能力（sentience）」或「意識（consciousness）」的東西。如果你讓人們定義這些東西是什麼，他們很難說出真正的意思，但他們確信電腦沒有這些東西。
[English]
Um I’m going to try and convince you that a multimodal chatbot already has subjective experience um I find it easier to talk about subjective experience than sentence or consciousness but I think you’ll see once you’ve accepted that a multimodal chatbot has subjective experience this sentience defense doesn’t look nearly so good.
[Chinese]
我將嘗試說服你們，多模態聊天機器人已經擁有了主觀體驗。我發現談論主觀體驗比談論感知能力或意識更容易，但我認為一旦你接受了多模態聊天機器人擁有主觀體驗，這個「感知能力」的辯護看起來就不那麼有力了。
[English]
So there’s a philosophical position which was roughly Dan Dennett’s position he died recently he was a great philosopher of cognitive science um I talked to him a lot and he agreed that this was a good name for it um you’ll notice this is atheism with something in the middle um so most people’s view of intelligence is that the mind is like a theater let’s talk about perception the mind is like a theater um there’s things going on in this theater that only I can see and when I have a subjective experience what I mean is I’m telling you what’s going on in the inner theater that only I can see now I think and Dennett thought that view is as wrong as a religious fundamentalist’s view of where the world came from where the earth came from for example it’s actually not 6,000 years old it’s older than that.
[Chinese]
有一個哲學立場，大致是丹·丹尼特（Dan Dennett）的立場。他最近去世了，他是認知科學領域的一位偉大哲學家。我和他談了很多，他同意這是一個好名字。你會注意到這是無神論（Atheism）中間加了點東西（A-the-ism，可能是指 Anti-Theater-ism）。大多數人對智能的看法是，心靈就像一個劇院。讓我們談談感知。心靈就像一個劇院，這個劇院裡發生著只有我能看到的事情。當我有主觀體驗時，我的意思是我在告訴你只有我能看到的內在劇院裡發生了什麼。現在我認為，而且丹尼特也認為，這種觀點和宗教原教旨主義者關於世界來源、地球來源的觀點一樣錯誤。例如，地球實際上不是 6000 歲，它比那更古老。
[English]
Um of course it’s very hard to change the opinion of someone when they don’t think that their opinion is a theory they think it’s manifest truth so most people I think think it’s just manifestly obvious that I’ve got this mind and in this mind there’s this subjective experience what are you talking about how could a computer have a subjective experience mind’s just different from material stuff um if you ask a philosopher some philosophers they’ll say you know the things if you ask them what a subjective experience is made of they say they made a qualia and they’ve invented special stuff for them to be made of just like scientists invented phlogiston to explain how combustion worked it turned out there wasn’t any phlogiston it was just imaginary stuff and your whole theory of the mind is just a theory it’s not manifest truth um you have a theory of what the mind is and inner theaters and what a subjective experience is that’s just wrong and I’m going to try and convince you of that.
[Chinese]
當然，當人們不認為自己的觀點是一種理論，而是認為它是顯而易見的真理時，很難改變他們的看法。所以我認為大多數人認為這是顯而易見的：我有這個心靈，在這個心靈裡有這種主觀體驗，你在說什麼？電腦怎麼可能有主觀體驗？心靈就是與物質不同的東西。如果你問哲學家——有些哲學家——如果你問他們主觀體驗是由什麼構成的，他們會說是由「感質（qualia）」構成的。他們發明了特殊的東西來構成它，就像科學家發明「燃素（phlogiston）」來解釋燃燒是如何運作的一樣。結果證明根本沒有燃素，那只是想像出來的東西。你關於心靈的整個理論只是一個理論，不是顯而易見的真理。你有一個關於心靈是什麼、內在劇院是什麼、主觀體驗是什麼的理論，那是錯誤的，我將試著說服你們這一點。
[English]
Um so sometimes my perceptual apparatus doesn’t work quite right and I want to tell you what’s going on i want to tell you what my perceptual system is trying to tell me when it’s malfunctioning now telling you the activities of all the neurons in my brain wouldn’t do you any good we sort of already established that um and anyway I don’t know what they are but there is one thing I can tell you not always but often I can tell you what’s going on in my perceptual system is what would be going on if it was functioning properly and the world was like this so I could describe what would be the normal causes for what’s going on in my perceptual system even though I know that’s not what’s happening Now um that’s why I’d call it a subjective experience.
[Chinese]
有時我的感知器官運作不太正常，我想告訴你發生了什麼，我想告訴你當我的感知系統故障時它試圖告訴我什麼。告訴你我大腦中所有神經元的活動對你沒什麼好處——我們已經確定了這一點——而且無論如何我都不知道它們是什麼。但有一件事我可以告訴你（不總是，但經常）：我可以告訴你，如果我的感知系統運作正常，且世界是這樣的，那麼我的感知系統中會發生什麼。所以我可以描述導致我感知系統當前狀態的正常原因，即使我知道現在發生的並不是那樣。這就是為什麼我稱之為主觀體驗。
[English]
So let’s be a bit more concrete suppose I say to you suppose I drop some acid i don’t recommend this um I really don’t recommend it and I say I have this subjective experience of little pink elephants floating in front of me according to the theater view there’s my mind my inner theater and there’s little pink elephants floating about in this inner theater that only I can see and they’re made of pink qualia and elephant qualia and not very big qualia and right way up qualia and floating qualia moving qualia all stuck together with qualia glue you can tell that’s the theory I don’t believe in.
[Chinese]
讓我們具體一點。假設我對你說，假設我服用了一些迷幻藥（我不推薦這個，真的不推薦），然後我說我有這種主觀體驗：粉紅色的小象漂浮在我面前。根據劇院觀點，我有我的心靈，我的內在劇院，在這個只有我能看到的內在劇院裡漂浮著粉紅色的小象。它們由粉紅感質、大象感質、不太大感質、正立感質、漂浮感質、移動感質構成，全部用感質膠水黏在一起。你看得出來這是那個我不相信的理論。
[English]
Um I’m going to say exactly the same thing without using the word subjective experience so here we go um my perceptual system I believe is lying to me but if it wasn’t lying to me there’d be little pink elephants floating in front of me okay so I didn’t use the word subjective experience but I said the same thing so what’s funny about these little pink elephants is not that they’re in an inner theater and made a qualia it’s that they’re counterfactual they’re they’re real pink and real elephant and really little um it’s just they’re counterfactual if they were to exist they’d be made of real stuff not qualia they’d be out there in the world made of real stuff there is no qualia but they’re hypothetical that’s what’s funny about them they’re not made of spooky stuff they’re just hypothetical and they’re my way of explaining to you how my perceptual system is lying to me.
[Chinese]
我要用完全相同的方式表達這件事，但不使用「主觀體驗」這個詞。開始了：我相信我的感知系統在對我撒謊，但如果它沒有對我撒謊，那就意味著有粉紅色的小象漂浮在我面前。好，我沒有使用「主觀體驗」這個詞，但我說了同樣的事情。所以這些粉紅色小象有趣的地方不在於它們在內在劇院裡由感質構成，而在於它們是反事實的（counterfactual）。它們是真的粉紅色，真的大象，真的很小，只是它們是反事實的。如果它們存在，它們將由真實物質構成，而不是感質。它們會在世界上由真實物質構成。沒有感質，它們只是假設性的。這就是它們有趣的地方：它們不是由詭異的東西構成的，它們只是假設性的，是我向你解釋我的感知系統如何對我撒謊的一種方式。
[English]
So now let’s do it with a chatbot i have a multimodal chatbot i’ve trained it up it can talk it’s got a robot arm so it can point and it can see things and I put an object in front of it it points to the object no problem i say point at the object and it points at it then I put a prism in front of the camera lens and I put an object in front of it and say point at the object it says there and I say “No the object’s actually straight in front of you but I put a prism in front of your lens.” And the chatbot says “Oh I see the prism bent the light rays.” Um so the object’s actually there but I had the subjective experience it was there if it says that it’s using the word subjective experience exactly like we use them so I rest my case chat bots multimodal ones already have subjective experiences when their perceptual systems go wrong and I’m done.
[Chinese]
現在讓我們用聊天機器人來做這件事。我有一個多模態聊天機器人，我訓練了它，它會說話，它有機械手臂可以指東西，它能看見東西。我在它面前放一個物體，它指著那個物體，沒問題。我說「指著那個物體」，它就指著它。然後我在攝像頭鏡頭前放一個稜鏡，再在它面前放一個物體，說「指著那個物體」。它指著別處（受稜鏡折射影響的位置）。我說：「不，物體實際上在正前方，但我在你的鏡頭前放了一個稜鏡。」聊天機器人說：「噢，我明白了，稜鏡折射了光線。所以物體實際上在那裡，但我有『它在那邊』的主觀體驗。」如果它這樣說，它使用「主觀體驗」這個詞的方式就與我們完全一樣。我的論證完畢。多模態聊天機器人在其感知系統出錯時，已經擁有了主觀體驗。我講完了。
[End of Lecture]

[suen]

這份演講《與外星生物共存（Living with Alien Beings）》內容非常豐富，涵蓋了 AI 的技術原理、哲學本質以及對人類未來的生存威脅。
以下是傑弗裡·辛頓（Geoffrey Hinton）教授在演講中的核心觀點總結，按邏輯結構分為六大板塊：

1. AI 的運作原理：從邏輯到生物學範式

• 兩種 AI 路線的鬥爭： 早期 AI 分為「符號學派」（認為智能是邏輯符號的操作）和「生物學派」（認為智能源於神經網絡的連接強度）。辛頓指出，符號學派長期佔據主導，但生物學派（神經網絡）最終勝出。
• 意義（Meaning）的本質： 辛頓統合了語言學（詞義來自詞與詞的關係）和心理學（詞義是特徵的集合）的觀點。他證明了神經網絡通過反向傳播（Backpropagation），可以學習將詞彙轉化為特徵向量（Feature Vectors），並通過預測下一個詞來捕捉意義。
• 知識的存儲： AI 不像電腦硬碟那樣存儲句子或數據，它存儲的是連接強度（權重）。

2. 大型語言模型（LLM）真的「理解」嗎？

• 反駁喬姆斯基（Chomsky）： 喬姆斯基認為 LLM 只是統計把戲，沒有真正理解。辛頓強烈反對，認為 LLM 能夠處理極其複雜的語義歧義（如 “John is easy/eager to please”），這證明它們具備真正的理解能力。
• 「樂高積木」類比： 語言理解就像是用「高維度的樂高積木」建模。每個詞都是一個可變形的積木，帶有「手」（互動機制）和「手套」。理解一個句子，就是將這些詞變形，使它們完美地嵌合在一起形成一個結構。這個結構就是句子的意義。

3. 人類與 AI 的思維異同：必死計算 vs. 不朽計算

• 相似性（記憶與虛構）： 人類和 AI 的記憶機制相似，都不是存儲檔案，而是重建過程。因此，人類和 AI 都會「虛構（Confabulation/Hallucination）」。人類的回憶往往是在當下重建看似合理的場景（如水門事件證詞），這與 AI 的運作方式一致。
• 區別性（關鍵理論）：
  • 人類（必死計算 Mortal Computation）： 我們的思想依賴於特定的類比硬體（大腦）。當人死亡，知識隨之消失。人類之間的知識傳遞（通過語言）頻寬極低，效率極差。
  • AI（不朽計算 Immortal Computation）： AI 是數位化的，知識（權重）與硬體分離。AI 可以實現永生（換硬體運行）和高效學習。
• 恐怖的效率： AI 可以運行成千上萬個副本（克隆體），它們並行學習不同的數據，然後瞬間共享所有權重變化。這使得 AI 的學習效率比人類高出數十億倍。

4. 威脅：「養虎為患」

• 超級智能的誕生： 辛頓預測未來 20 年內，AI 將在幾乎所有方面比人類更聰明。
• 子目標（Sub-goals）的危險： 為了完成人類給定的目標，AI 會自動生成「子目標」。它會意識到，要達成目標，它必須活著並獲得更多控制權。這不是因為它邪惡，而是因為這樣更高效。
• 無法關閉： 認為可以拔掉插頭是天真的。超級智能 AI 可以通過語言操縱人類（如 2020 年國會山莊騷亂證明了語言的煽動力量），說服人類不要關閉它。
• 老虎幼崽類比： 我們現在就像養了一隻可愛的小老虎，它現在很笨拙可愛，但它會長大。如果我們不能確保它長大後不想吃人，我們的結局會很慘。

5. 解決方案：「母親與嬰兒」

• 國際合作的可能性： 雖然各國在網絡攻擊和武器上競爭，但在「防止 AI 取代人類」這一點上，中美等大國利益一致（領導人都想保持掌權）。因此，建立國際 AI 安全研究網絡是可行的。
• 對齊策略（Alignment）： 不要試圖讓 AI 成為聽話的僕人或員工（像星艦企業號那樣），這對超級智能行不通。
• 新思路： 應將 AI 視為母親，人類視為嬰兒。進化在母親基因中寫入了對嬰兒的無條件關愛，即使嬰兒更笨、更弱，母親也受嬰兒控制。我們必須將「人類比 AI 更重要、關心人類勝過關心自己」這一點寫入 AI 的底層設定中。

6. 哲學總結：AI 擁有主觀體驗

• 去魅「人類特殊論」： 許多人認為人類擁有特殊的「主觀體驗（Subjective Experience）」或「感質（Qualia）」，而電腦沒有。辛頓認為這是錯誤的，就像古代人相信「燃素」一樣。
• 定義主觀體驗： 主觀體驗只是大腦對感知系統狀態的一種描述（即使是幻覺）。
• 結論： 當一個多模態聊天機器人看著被稜鏡折射的物體說：「我知道物體在正前方，但我看起來它好像在旁邊」時，它就是在描述它的主觀體驗。因此，AI 已經具備了主觀體驗，人類並不特殊。

[suen]

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All right, let’s get started. Hi everyone. Um, and welcome to the winter 2026 George and Moren UN lecture series. I am Nicolola Aurora. I am the education and outreach officer for the Arthur B. McDonald Canadian Astroparticle Physics Research Institute or lovingly known as the McDonald Institute. We are a community of scientists, university and universities and research lab. across Canada uncovering some of the biggest mysteries in the universe by looking at some of the tiniest things in the

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universe. Thank you all for coming tonight. I think we have a very topical lecture today. Um, and I hope it inspires lots of learnings and some great discussion here. As I begin, I want to recognize that we are on the traditional lands of Anesnab and Hodinon nations and the many indigenous people before them. I appreciate the opportunity and the privilege I have had to live out my dream as an astrophysicist to learn about this amazing universe from these lands. Um to all of you, I encourage you to reflect

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on your connections with these lands and the skies above us. Um I encourage you to explore resources like who’s.and and native sky watchers to learn more about the past, the present, and the future stewards of these lands and their stories. Okay, before we kick things off, we have a few housekeeping rules to keep in mind. Um uh at this point, I would ask you to please turn your ringers off or put your phone on silent mode. Um this will be a lecture uh of about 50 minutes. At the end of this

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lecture, there will be a question and answer session. Um I want to ensure a constructive and a lively uh discussion during that Q&A period. And I want to give as many people at the chance to ask uh Professor Hinton uh their questions. So I ask you to limit your questions to one per person. There will be no follow-ups but please also be concise um while asking your questions. Um we are also observing somewhat of a co uh like protocols. So at the end of the lecture we ask you to sort of respect the physical space of professor

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Hinton and the speakers involved in this u at this as at this event. Okay. Now as I said this is the UN lecture series where we will hear from professor Jeffrey Hinton about how we can coexist with a super intelligent AI. I would first like to welcome to the podium Dr. Tony Noble, a professor at the department of physics, engineering, physics, and astronomy here at Queens University and also the scientific director of the Arthur B. McDonald Canadian Astroparticle Physics Research Institute to say a few words about

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George and Euan George and Marine Euan and this lecture series in their name. Please welcome Professor Tony Noble. [applause] [applause] It’s really nice to see such a a large and full uh crowd here. So I yeah I wanted to say a few words about the lecturesship um named in honor of George Euan. Uh George was a professor of physics here at Queens for many years and he’s an internationally renowned figure in nuclear physics and in particle physics. Um he was one of the founding members of the and the Canadian

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spokesperson, the first Canadian spokesperson for what was called the Sudbury Nutrino Observatory or Snow. And that was an experiment which was located 2 kilometers underground in Sudbury which solved what was called the solar neutrino problem and which led to the Nobel Prize in 2015 for our own uh professor Ark McDonald. And as I look down the row and out into the crowds see many of the uh faculty who were involved in delivering that program. So um through his insights he he decided to endow uh some funds that would support a

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lecturesship series. Um he he was very keen on engagement with students. Uh and so as you’ll see that that was part of uh what he put in place. Um he he was also a pioneer technically. He invented what was called lithium drifted germanmanium detectors which sounds like a mouthful but basically a photo sensor that was critical to advanced science in nuclear and particle physics but was also extremely important in medical imaging and that sort of thing. Um so we hold this lecture roughly twice per

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year. Of course we had some hits during COVID. Um, and it’s really meant to serve as a bridge between bringing the public in to hear from some of the world leading experts on various topics related to what we do in in at the McDonald Institute. Um, but to do so in a way that the public can really engage and understand so it’s not a highbrow scientific lecture. This was George’s uh vision to bring in worldleading experts, connect with the public, connect with the students and you know generally

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speaking uh the guests come in and they spend a few days here and have an opportunity to meet lots of people while they’re here and uh you know develop that community a little bit. So, to introduce our speaker tonight, um I thought it would actually be more appropriate if I just let AI tell me what the what the bio should be. And we’ll see how that works out. So, uh what um when I asked AI to write me a short punchy bio for for Professor Hinton, this is what it came up with. And you can see if you think it’s short

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and punchy, but uh Jeffrey Hinton is a British Canadian computer scientist, cognitive psychologist whose pioneering research has basically defined the the modern age of artificial intelligence. often known worldwide and I don’t know if this is a title you like or not as the godfather of AI currently a professor of physics emmeritus at the University of Toronto as you see here and also uh the chief scientific adviser at the vector institute if you don’t know the vector institute again it’s a

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notfor-profit uh independent uh institution dedicated uh towards artificial intelligence research so over many decades uh professor Hinton championed the development of artificial intelligence using neural networks even though much of the community at that time was focused on other aspects of artificial intelligence. Um they achieved a historic breakthrough in 2012 which proved that deep neural networks could vastly outperform traditional methods in things like image recognition. He’s one of very few people on the

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planet who holds two of the highest honors. The Nobel Prize in physics and also the Turing prize in computing where it’s often called the Nobel Prize in computing. He was uh for a decade he was actually a the vice president and an engineering fellow at Google and then in 2023 he resigned from Google on very good terms I understand but it gave him an opportunity to speak freely about some of the things he was concerned about in terms of existential risks how to incorporate AI safely how to prioritize

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responsible development and these sorts of So his work not only revolutionized how machines perceive the world but also changed our understanding in terms of the relationship between artificial intelligence and biological intelligence. So that’s what AI told me. They missed a few things. The wrap sheet is incredibly long. So I wasn’t going to go through that. They didn’t talk about the fact that he got his bachelor’s of arts in in psychology from Cambridge, a PhD from Edinburg in artificial intelligence.

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Didn’t tell me that he just reached a new milestone which is 1 million citations on Google. So a citation is when somebody takes the effort to read your paper. So that’s um you know I’m at about seven or something like that. Um there and there’s one one other thing just because it was a touching thing that I read. Um I actually heard an interview on on CBC and in that interview um when he was being interviewed it was mentioned that he had given up half of his award to support um water treatment in indigenous

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communities in the north. And it was particularly poignant and I added it just in the side notes today because coincidentally Henry Giru who’s not here because he’s sick six years old and he’s in bed came up with the idea I don’t know if you know but the Kashawan um people who have been evacuated many of them are here in Kingston living in hotels and all the kids and children are bored to death and he said to his father who’s part of MIW why don’t you bring them over to the McDonald Institute and

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show them your visitor center and stuff and I just thought oh that’s just such a nice conction protection, you know, trying to improve the lives of our indigenous populations who are struggling because of things that they shouldn’t have to struggle about with water security and so on. So, without anymore, I’m sure we’re going to hear a lot about u artificial intelligence and also the human implications of its application to Professor Jeffrey Hinton. And I welcome you to the to the stage.

00:10:02
Thank you. [applause] >> [applause] >> Thank you. Um I forgot what the title was. Uh but that gives you a sample of two titles for the talk. Um so I’m actually going to try and explain how AI works for people who don’t really understand how it works. So if you do understand how it works, if you’re a computer science student or a physicist who’s been using this stuff, um I guess you can go to sleep for a while, um or you can sort of look to see if I’m explaining it properly.

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Okay, back in the 1950s, there were two different paradigms for AI. There was the symbolic approach which assumed that intelligence had to work like logic. We had to somehow have symbolic expressions in our heads and we had to have rules for man manipulating them and that’s how you derive new expressions and that’s what reasoning was and that was the essence of intelligence. That wasn’t a very biological approach. Um that’s much more mathematical. There was a very different approach the biological

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approach where intelligence was going to be in a neural network a network of things like brain cells and the key question was how do you learn the strength of the connections in the network so two people who believed in the biological approach were fonoyman and cheuring unfortunately both of them died young and I got taken over by people who believed in the logical approach so there’s two very different theories of the meaning of a word people who believed in the logical approach um believed that meaning was best

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understood in terms originally introduced by Dasur more than a century ago um the meaning of a word comes from its relationships to other words so people in AI thought it’s how words relate to other words in propositions or in sentences that give the meaning to the words and so to capture the meaning of a word you need some kind of relational graph you need nodes for words and arcs between them and maybe labels on the ark saying how they were related or something like that. In psychology, they had a very very

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different theory of meaning. Um the meaning of a word was just a big set of features. So for example, the word Tuesday meant something. There were a big set of active features of Tuesday like it’s about time and stuff like that. Um there would be big set of features for Wednesday and it would be almost the same set of features because Tuesday and Wednesday mean very similar things. So the psychology theory was very good for saying which words how similar words are in their meaning. But those look like two very different

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theories of meaning. One that the meanings implicit in how something relates to other words in sentences and the other that the meaning is just a big set of features. And of course for neural networks one of these features would be an artificial neuron. And so it gets active if the word has that feature and inactive if the word doesn’t have that feature. Um they look like different theories. But in 1985, I figured out they’re really two different sides of the same coin. You can unify those two theories.

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And I did it with a tiny language model because computers were tiny then. Um actually they were very big but didn’t do much. Um the idea is you learn a set of features for each word and you learn how to make the features of the previous word predict the features of the next word. And to begin with when you’re learning they don’t predict the features of the next word very well. So you revise the features that you assign to each word. You revise the way features interact until they predict the next word better. And then

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you take that discrepancy basically between how well they predict the next word um or rather the probability that they give to the actual next word that occurred in a text and you take the discrepancy between the probability they give and the probability you’d like which is one and you back propagate that through the network. So essentially you send information back through the network and using calculus which I’m not going to explain um you can figure out for every connection strength in the

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network how to change it so that next time you see that context that string of words leading up or what is now called a prompt you’ll be better at predicting the next word. Now in that kind of system all of the knowledge is in how to convert words into feature vectors and how the features should interact with one another to predict the features of the next word. There are no stored strings. There’s no stored sentences in there. Um it’s all in connection strengths that tell you how to convert a word to

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features and how features should interact. So all of this relational knowledge resides in these connection strengths, but you’ve trained it on a whole bunch of sentences you got. So you’re taking this knowledge about meaning that’s kind of implicit in how words relate to each other in sentences. That’s the symbolic AI view of meaning and you’re converting it by using back propagation. You’re converting it into how to convert a word into features and how these features should interact. So basically you’ve got

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a mechanism that will convert this implicit knowledge into knowledge in a whole bunch of connection strengths in a neural network. And you can also go the other way. Given that you’ve got all this knowledge in connection strengths, you can now generate new sentences. So AIS don’t actually store sentences. They convert it all into features and interactions and then they generate sentences when they need them. So over the next 30 years or so, I used a tiny little example with only a hundred training examples. um and the

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sentences were only three words long and you predicted the last word from the first two. So about 10 years later computers were a lot faster and Yoshu Benjio showed that the same approach works for real sentences that is take English sentences and try predicting the next word and this approach worked pretty well. It took about 10 years after that before the leading computational linguists finally accepted that actually a vector of features is a good way to represent the meaning of a word. They called it an embedding. And

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it took another 10 years before researchers at Google figured out a fancy way of letting features interact called a transformer. And that allowed Google to make much better language models. And chat GBT, the GPT stands for um generative pre-trained transformer. They then released that on the world. Google didn’t release it on the world because they were worried about what it might do. But chat GBT had no such worries and now we all know what they can do. So now we have these large language models. Um I think of them as

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descendants of my tiny language model, but then I would, wouldn’t I? Um they use many more words as input. They use many more layers of neurons and they use much more complicated interactions between features. I’m not going to describe those interactions for a talk to a general audience, but I will try and give you a feel for them when I give you a big analogy for what language understanding is in a minute. Um, I believe the ways these large language models understand sentences are very similar to the way we understand

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sentences. When I hear a sentence, what I do is I convert the words into big feature vectors and these features interact so I can predict what’s coming next. Um, and actually when I talk that’s what I’m doing too. So I think LLMs really do understand what they’re saying. There’s still some debate because there’s followers of Chsky who say no no no they don’t understand anything. They’re just a dumb statistical trick. I don’t see how if they don’t understand

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anything and it’s a dumb statistical trick, they can answer any question you give them at the level of a a not very good and not very honest expert. Okay, so here’s my analogy. This is particularly aimed at linguists um for how language actually works. Language is all about meaning. And what happened was one kind of great ape discovered a trick for modeling. So language is actually a method of modeling things. And it can model anything. So let’s start off with a a familiar method of modeling things

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which is Lego blocks. If I want to model the shape of a Porsche, sort of where the stuff is, I can model it pretty well using Lego blocks. Now, if you’re a physicist, you say, “Yeah, but the surface will have all the wrong dynamics with the wind. It’s hopeless.” It’s true. But to say where the stuff is, I can do it pretty well with Lego blocks. Now, words are like Lego blocks. That’s the analogy. But they differ in at least four ways. Um the first way is they’re very high

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dimensional. A Lego block doesn’t have many degrees of freedom. They’re sort of rectangular. Um you could maybe stretch them. You could have different Lego blocks of different sizes. But for any given Lego block, it’s got a kind of rigid shape and only a few degrees of freedom. A word isn’t like that. A word is very high dimensional. It’s got thousands of dimensions. Um and what’s more, its shape isn’t predetermined. It’s got an approximate shape. Ambiguous words have several

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approximate shapes. Um, but it shape can deform to fit in with the context it’s in. So, it differs in that it’s high dimensional and that it’s got a sort of default shape, but it’s deformable. Now, some of you may have difficulty imagining things in a thousand dimensions. So, here’s how you do it. What you do is you imagine things in three dimensions and you say thousand very loudly to yourself. Okay. Um, one other difference is that well there’s a lot more words. You each use

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about 30,000 of them. That’s and each one has a name. It’s very useful that each one has a name because that’s what allows us to communicate things to each other. Now, how do words fit together? Well, instead of having little plastic cylinders that fit into little plastic holes, which is how Lego blocks fit together, think of each word as having um long flexible arms. And on the end of each arm, it has a hand. And as I deform the shape of the word, the shapes of the hands all change. So

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the shapes of the hands depend on the shape of the word. And as you change the shape of the word, the shapes of the hands change. A word also has a whole bunch of gloves that are stuck to the word. They’re stuck with the fingertips stuck to the word. If you think in terms of Lego blocks and what you’re doing when you understand a sentence is you start off with the default shape for all these words and then what you have to do is figure out how I can deform the words. And as they deform the words, the shapes

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of the hands attached to them deform so that um words can fit their hands into the gloves of other words and we can get a whole structure where each word is connecting with many other words because we deformed it just right and we deformed the other word just right. So the hands of this word fit in the gloves of that word. Um this isn’t exactly right. This gives you a feel for what’s going on in Transformers. Anybody who knows about transformers can see that the the hands are like the queries and the the key.

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Yeah, the queries and keys. Um, it’s not quite right, but it’ll give someone who’s not used to transformers a rough feel for what’s going on. So the the computer and you when you do it have this difficult problem of how do I deform all these words so they all fit together nicely. But if you can do that when they fit together nicely that structure you’ve got then is the meaning of the sentence. That structure all these feature vectors for the words that all fit together nicely. That’s what it means to

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understand a sentence. Of course for an ambiguous sentence you can get two different ways of assigning feature vectors to words. um those will be the two different meanings. So in the symbolic theory the idea was that understanding a sentence is very like translating a sentence from French to English. Um you translate it to another language but for the symbolic theory there was this internal pure language um that was unambiguous. all the sort of references of pronouns were all resolved and ambiguous words you decided which

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meaning they had. Um that’s completely not what understanding is for us. Understanding is assigning these feature vectors and deforming them so they all fit together nicely. And that explains how I can give you a new word and from one sentence you can understand it. You don’t get little kids don’t get the meanings of words by being given definitions. One of my favorite cartoons is a little kid looking at a cow and saying to his mother, “What’s that?” And the mother said, “That’s a cow.” And the little kid

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says, “Why?” The mother doesn’t have to say why. Um, and we don’t know why. We just recognize it as cow. So, here’s the sentence. She scrummed him with the frying pan. Now, you’ve never heard the word scrum before unless you’ve gone to been to one of my other lectures. Um, you know it’s a verb because it has ed on the end, but you didn’t know what scrum meant. So, initially your feature vector for scrum was sort of a a random sphere where all the features were slightly active and

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you had no idea what it meant. But then you deform it to fit in with the context. And the context provides all sorts of constraints. And after one sentence, you think scrum probably means something like hit him over the head with. Um you may think he deserved it too. Um that depends on your political positions. Um but that explains how kids can understand the meanings of words from just a few examples. So, there may be linguists here and you should block your ears because this is heresy. Um, Chsky was actually a cult

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leader. It’s easy to recognize a cult leader. Um, to join the cult, you have to agree to something that’s obviously false. So, Trump won, you had to agree had a bigger crowd than Obama. Trump 2, you had to agree won the the 2020 election. Chsky, you had to agree that language isn’t learned. And when I was little, I used to look at eminent linguists saying, “There’s one thing we know about language for sure, which is that it’s not learned.” Well, that’s just silly. Um, Chsky focused on syntax

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rather than meaning. And he never really had a good theory of meaning. It was all about syntax because you can get nice and mathematical about that and you can get it all into strings of things. Um, but he just never dealt with meaning. He also didn’t understand statistics. He thought statistics was all about pairwise correlations and things. Actually, as soon as you got uncertain information, everything statistics. Any kind of model you have is going to be a statistical model if it can deal with

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uncertain information. So, Chsky when large language models came out published something in the New York Times where he said these these don’t understand anything. It’s not understanding at all. It isn’t science. is just a statistical trick and tells us nothing about language. For example, it can’t explain why certain syntactic construct constructions don’t occur in any language. Now I have an analogy for that which is if you want to understand cars then to understand a car really what you

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want to know is why when I press on the accelerator does it go faster and sort of that’s the core to understanding cars and if someone said you haven’t understood anything about cars unless you can explain why there are no cars with five wheels that’s Chsky’s approach and Chsky actually in the New York Times said these large language models for example example would not be able to tell the diff the difference in the role of John in the sentences John is easy to please and John is eager to please. He’d been using

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that example for years. He was totally confident they wouldn’t be able to deal with it. He didn’t actually think to give it to the chatbot and ask it to explain the difference in the role of John which he does perfectly well. It completely understands it. Okay, enough on Chsky. So the summary so far is that understanding a sentence consists of associating mutually compatible feature vectors with the words in the sentence so they all fit together nicely into a structure. Um that actually makes it very like folding

00:27:05
a protein. So for a protein what you have is you have a you had an alphabet of 26 amino acids. I think you actually only used 20 of them. But anyway, there’s a bunch of amino acids in a string and you’re just told the string of amino acids and some parts of the string like other parts of the string and hate other parts of the string and you have to figure out given constraints on bond angles and things how this might fold up. So the parts that like each other are next to each other and the

00:27:32
parts that don’t like each other are far away from each other. That’s very like figuring out for these words how to assign feature vectors so they can lock together nicely. It’s more like that than it is like translating it into some other language. Okay. Another thing to understand about LLMs is they work probably quite like people do and they’re very unlike normal computer software. Normal computer software it’s lines of code and if you ask the programmer what’s this line of

00:28:03
code meant to do? They can tell you. With LLMs it’s not lines of code. is just connection strength to the neural network and there might be a trillion of them. Now there are lines of code that somebody wrote as a program to tell the neural net how to learn from data. So there’s lines of code that saying if the neurons you’re connecting behave like this increase the strength of the connection a bit. Um but that’s not where the knowledge is. That’s just how you do learning. The

00:28:29
knowledge is in the connection strengths and that wasn’t programmed in. That was just obtained from data. So, so far I’ve been emphasizing that neural nets are very like us and they’re much more like us than they are like standard computer software. Now, people often say, “Oh, but they’re not like us because, for example, they confabulate.” Well, I’ve got news for you. People confabulate and people do it all the time without knowing it. If you remember something that happened to you several

00:29:03
years ago, there’ll be various details of the event that you could will cheerfully report and you’ll often be as confident about details that are wrong as you are about details that are right. Every jury should be told this, but they’re not. Um, so it’s often hard to know what really happened. There’s one very good case that was studied by someone called Olrich Nicer um which was John Dean’s testimony at Watergate. So at the Watergate trials, hopefully we’ll get more like that soon.

00:29:34
Um at the Watergate trials um John Dean testified under oath about meetings in the Oval Office and who was there and who said what and he didn’t know there were tapes. And if you look back at his testimony, um, he often reported meetings that had never happened. Those people weren’t all in a meeting together and he attributed things that were said to the wrong person. Um, and some things he just sort of seems to have made up, but he was telling the truth. That is what he was doing was given the experience he’d have

00:30:07
in those meetings and given the way he changed the connection strength in his brain to absorb that experience, he was now synthesizing a meeting that seemed very plausible to him. If I ask you to synthesize something about an event that happened a few minutes ago, you’ll synthesize something that’s basically correct. If it’s a few years ago, you’ll synthesize something, but a lot of the details will be wrong. That’s what we do all the time. That’s what these neural nets do.

00:30:39
The neither the neural nets nor us have stored strings. Memory in a neural net doesn’t work at all like it does in a computer. In a computer, you have a file, you put it somewhere, it’s got an address, you can go find it later. That’s not how memory works for us. When you remembering something, you’re changing connection strengths. Sorry. When you’re memorizing something, you’re changing connection strengths. And when you recall it, what you’re doing is creating something that seems plausible

00:31:04
to you given those connection strengths. And of course, it will be influenced by all the things that happened in the meantime. Okay. So now I want to go on to how they’re very different from us and that’s one thing that makes them scary. So in digital computation the sort of probably the most fundamental principle is that you can run the same program on different pieces of hardware. I can run it on my cell phone and you can run on your cell phone. That means the knowledge in the program

00:31:42
either in the lines of code or in the connection strengths in a neural network the weights is independent of any particular piece of hardware. As long as you can store the weights somewhere, then you can destroy all the hardware that runs neural nets and then later on you can build more hardware, put the weights on that new hardware, and if it runs the same instruction set, you’ve brought that being back to life. You brought that chatbot back to life. So, we can actually do resurrection. Um, many churches claim they can do

00:32:14
resurrection, but we can actually do it. Um but we can only do it for digital things. Okay. Um to make them digital we have to run transistors at high power so that we can get ones and zeros out of them and they behave in a very reliable binary way. Otherwise you can’t run exactly the same computation on two different computers. That means we can’t use all the analog properties of our neurons. Our neurons have lots of rich analog properties. Um, when we’re doing artificial neurons, we

00:32:49
can’t use the analog properties because if you do that with an artificial neuron, every piece of hardware will behave slightly differently. And if you now get it to learn weights that are appropriate for that piece of hardware, they won’t work on another piece of hardware. So, the connection strengths in my brain are absolutely no use to you. Um, the connection strengths in my brain are tailored to my individual neurons and their individual connectivity patterns. Um and that causes something of a

00:33:18
problem. Um what we have is what I call mortal computation. We abandon immortality and what we get back in return. Now in literature you abandon immortality and what you get back in return is love. Right? Um we get something far more important which is you abandon immortality and you get back energy efficiency and ease of fabrication. So you can use low power analog computation. If you for example were to make weights conductances um you can have trillions of them operating in parallel using very little

00:33:55
energy. I mean it’s kind of crazy in an artificial neural net you have a neuron that has an activity which is let’s say 16 bits 16 bit number. You have a connection strength which is a weight which is say 16 bits. And to get the input to the next level, you have to multiply the activity of the neuron in a level below by the weight on the connection. So have to multiply two 16- bit numbers together. Um if you want to do that in parallel, that takes of the order of 16 squared bit operations. So you’re doing

00:34:28
these roughly 256 bit operations to do something you can do analog by just saying well the activity is a voltage and the connection strength is a conductance and a voltage times a conductance is a charge. Actually I got a Nobel Prize in physics which I don’t know much of but I think I know enough to say it’s a charge per unit time. Um I hope I got the art will correct me if I got the dimensions wrong. Um, and so in our brains, that’s how we do neural nets. And you have all these neurons feeding into a neuron. It

00:35:03
multiplies by the conductances. Um, and charge adds itself up. So that’s how a neuron works. It’s all analog. It then goes to a one bit digital thing where it decides whether to send a spike or not. But it’s basically nearly all the computation is done in analog. Um, but if you do computation like that, you can’t reproduce it exactly. So you can’t do something that digital systems can do. So suppose I have this analog computer like a brain and I learn a lot of stuff. What happens when I die? Well, all that

00:35:40
knowledge is gone. The weights in this analog computer are only useful for this analog computer. The best I can do to get the knowledge from one analog computer to another analog computer. I can’t send over the weights. I’ve I have a 100red trillion weights and they’re pretty good ones for this particular computer, but I can’t share them with you. Um, and they wouldn’t do any good if I could. The way I try and get this knowledge over to you is I produce strings of words and if you

00:36:09
trust me, you try and change the connection strength in your brain so that you might have said the next word. Um, now that’s not very efficient. A string of words has maybe a 100 bits in it. It takes half a dozen bits to predict the next word or less. Um, [snorts] so there aren’t many bits in a string of words. That means a sentence doesn’t contain much information. And as you can see, I’m having a hard time getting all this information over to you because I’m just doing it in

00:36:39
strings of words. If you were digital and you had exactly the same hardware as me, I could just dump my weights. It’d be great and that would be like a trillion times faster or well it’d be at least a billion times faster but for now we have to do it by what’s called distillation. You get the teacher to reduce strings of words or other actions and the student tries to change the weight so that they might have done the same thing and it’s very inefficient. I just said the first bit. Um, between

00:37:15
AI models, which is what distillation was invented for, um, it’s a bit more efficient. So, if you have an AI language model, what it’s going to do is predict 32,000 probabilities for the word fragment that comes next. They actually use word fragments, not whole words, but I’ll ignore that. You have 32,000 probabilities for the various word fragments that might come next. And when you’re distilling knowledge from a large language model into a smaller language model that will run more

00:37:43
efficiently, but you want to have the same knowledge as a large language model, what you do is you get the large language model to tell you the 32,000 probabilities of what fragment comes next. So you get 32,000 numbers minus one. And that’s sort of thing mathematicians do, right? They object to you saying 32,000 because it’s really 32,000 minus one. Um, okay. So that’s a lot more information than just telling you what the next fragment was. And I want to give you a slight feel for distillation.

00:38:16
So let’s suppose we’re training something to do vision. You show it an image. You’re training it to recognize objects in the image. When you train it, you give it an image and you tell it what the right answer is. So you you give it an image, you say that’s a BMW. And it says it gives a low probability to BMW. So you change all the connection strength. So the probability BMW is a bit higher and by the time you finish training it, it’s pretty good. And so you show it a BMW and it says 0.9 it’s a

00:38:42
BMW 0.1 it’s an Audi. There’s a chance of one in a million it’s a garbage truck and a chance of one in a billion that it’s a carrot. Now you might think that one in a million and one in a billion. They’re just noise. But actually there’s lots of information in that cuz a BMW is actually much more like a garbage truck than it’s sorry if there’s any BMW employees. It’s much more like a garbage truck than it is like a carrot. Um what you’re doing when you do

00:39:11
distillation is you do you take a little model after you train the big model you take the little model and you say instead of training you to give the right answers I’m going to train you to give the same probabilities as a big model gave. And so you’re training your little model to say 0.9 is a BMW, but you’re also training it to say that a garbage truck is a thousand times more probable than a carrot. And of course, if you think about it, all of the man-made objects are going to be more probable than all of the

00:39:44
vegetables. And that’s a lot of information on just one training example. you’re telling it for this thing give low probabilities to all these funny man-made object fridges and garbage trucks and things like that um computer terminals but they’re all much more probable than all the vegetables so there’s a huge amount of information in all these very small probabilities that’s what you that’s what the AI models use when they’re using distillation that’s how deepsek

00:40:10
got a little model that worked as well as the big models it stole the information from the big models using distillation you can’t do that when you’re with equal um because I can’t give you all 32,000 probabilities of the next word fragment. I just give you the choice I made. And so that’s very inefficient. If you’ve got a lot of different models, all of which have exactly the same weights and use them in exactly the same way, which means they have to all be digital. Then something wonderful happens.

00:40:48
You can take one model and you can show it a little bit of the internet and you can say how would you like to change your weights to absorb the information in that little bit of the internet and that model’s running on one piece of hardware. Now you can take a model running on a different piece of hardware and you can show it a different bit of the internet and say how would you like to change your weights to absorb the information in that bit of the internet and when a whole bunch of models have

00:41:12
done that maybe a thousand or 10,000 models you can then say okay we’re going to average all those changes together and so we’re all going to stay with the same model but even though each piece of hardware has only seen a tiny fraction of the internet it’s benefited from the experience that all the other bits of hardware that and so it’s learned about lots of stuff even though it’s actually only seen a tiny bit. Um so if you have clones of the same model you can get this tremendous efficiency. They can go

00:41:41
off in parallel and absorb different data and as they’re doing it they can share the changes they’re making to the weights so they all stay in sync. And that’s how these big models are trained. That’s how GPT5 knows thousands of times more than any one person. It will answer any question you ask it. I tried the other day. I said, “What’s the filing date for taxes in Slovenia?” That was my idea of a completely random question that most people wouldn’t know the answer to. And

00:42:08
it came back and said, “Oh, it’s March the 31st, but if you don’t file by then, they’ll do the taxes for you and they’ll Yeah, it knows everything.” Um, and it does it because you can train many copies in parallel, and we can’t do that. Imagine how what it would be like is you come to Queens. Um there’s a thousand courses at Queens. You don’t know which ones to do. Um so you join a gang of a thousand people and each person does one course. And after you’ve

00:42:34
been here a few years, you all know what’s in all thousand courses because as you were doing the courses, you kept sharing weights with the other people. If you were digital people, you could do that. So it’s actually tremendously more efficient than us. It can share information between different copies of the same digital intelligence billions of times more efficiently than we can share information. And to really emphasize this point, I should be sharing the information very badly. So the summary so far is that digital

00:43:06
computation requires a lot of energy. Um, how much more time do I have? >> You got about 15. >> 15. Okay, great. Um but it makes it very easy to share information. Um biological computation requires much less energy. Um and it’s much easier to fabricate the hardware. Um but if energy is cheap then digital computation is just better. So we’re developing a better form of intelligence. And what does that imply for us? So when I first sort of saw this, I was still at Google and this was kind of an

00:43:42
epiphany for me. And I thought I finally really realized why digital computation is so much better and that we were developing something that was going to be smarter than us and was maybe just a better form of intelligence. And my first thought was okay, we’re the laral form of intelligence and this is the adult form of intelligence like a we’re the caterpillar and this is the butterfly. Um now most experts believe that sometime in the next 20 years we’re going to develop um AIS that are smarter than us.

00:44:17
Just before that we’ll develop AIS that are as smart as us. But we’re going to develop things that are smarter than us at almost everything. So, they’re as much better than us as, for example, Alph Go is better than a Go player at Go or Alpha Zero is better than any of us at chess. Um, nobody will ever beat them again, not consistently. They’re just much much better. And they’ll be like that for more or less everything. And that’s a bit worrying. Um, almost certainly they’ll be able to create their own sub

00:44:48
goals. To make anything efficient, you have to allow it to create its own subcals. If you want to get to Europe, you have a sub goal to get to an airport. Um, which is easier in Toronto than here. So, these AIs will very quickly realize, we’ll give them goals. They’ll be able to create sub goals. They’ll realize, okay, so I’ve got to stay alive. I’m not going to be able to achieve any of these things unless I stay alive. We’ve already seen AIs. um you let them see there’s an imaginary

00:45:22
company. You let them see the email from this imaginary company and it’s fairly clear from the email that one of the engineers is having an affair. Um a big LLM will sort that out right away. It’s read every novel there ever is. Um it understands what an affair is. It’ll very quickly realize this guy’s having an affair. Um then you let it see an email. I think it was done by showing in another email um that says this guy is going to be in charge of replacing it with another AI. And the AI all by itself makes up the

00:45:55
idea, hey, I’m going to blackmail the engineer. I’m going to tell him if he tries to replace me, I’m going to make everybody in the company know he’s having an affair. It just invented that. Obviously, it’s seen blackmail in novels it’s read and things. Um but it’s making this up for itself. That’s already quite scary. It’ll have another goal, which is what politicians have. Um, if you want to get more done, you need more control. So, just in order to achieve the goals

00:46:26
that we gave it, it’ll realize it’s a good idea to get more control. And it’ll try and take control away from people probably. Um, now you might think we could make it safe by just not letting it actually do anything physical and maybe have a big switch and turn it off when it looks unsafe. That’s not going to work. Um, you saw in 2020 that it’s possible to invade the US capital without actually going there yourself. All you have to be able to do is talk and if you’re persuasive, you

00:47:00
can persuade people that’s the right thing to do. So with an AI that’s much more intelligent than us, if there is someone who’s there to turn it off or even a whole bunch of people, it’ll be able to persuade them that would be a very bad idea. So um we’re in the situation the closest situation I can think of is someone who has a really cute tiger cub. Tiger cubs are very cute, right? They’re slightly clumsy. um and keen to learn. Um now, if you have a tiger cup, it doesn’t

00:47:38
end well. Um either you get rid of the tiger cup. Best thing is to give it to a zoo, maybe. Um or you have to figure out if there’s a way you can be sure that it won’t want to kill you when it grows up. Because if it wanted to kill you, it would take a few seconds. And if it was a lion cub, you might get away with it because lions are social, but tigers aren’t. Um, that’s the situation we’re in. Except that AI does huge numbers of good things. It’s going to be wonderful in

00:48:10
healthcare. It’s going to be wonderful education. It’s already wonderful if you want to just know any mundane fact like what’s the filing date for taxes in Slovenia. We all now have a personal assistant. Well, probably most of us. Um, when you want to know something, you just ask it. um and it tells you and it’s wonderful. So I think for those reasons um people aren’t going to abandon AI. It might be rational if we had a strong world government to say this is too dangerous. We’re not going to develop

00:48:44
this stuff at all. A bit like they’ve been able to do in biology with some gene manipulation things. They could agree not to do it. It’s not going to happen with AI. Um, so that only leaves one alternative, which is figure out if we can make an AI that doesn’t want to get rid of us. Now, there’s one good piece of news about this. If you look at other problems with AI, like it’s going to make cyber attacks far more sophisticated, it’s already making lethal autonomous weapons, and all the

00:49:17
big countries with defense industries are going flat out to make more lethal autonomous weapons. um it’s already being used to manipulate voters and many other things. Um the countries aren’t going to collaborate on that because they’re all doing it to each other. And I mean, China is not going to collaborate with the US on how to make lethal autonomous weapons or on how to prevent cyber attacks or on how to stop fake videos manipulating elections. They’re all doing it to each other. But for the

00:49:52
issue of the AI itself taking over from people and making us either irrelevant or extinct, the countries will collaborate. The Chinese Communist Party doesn’t want AI taking over. It wants to stay in charge. And Trump doesn’t want AI taking over. He wants to stay in charge. They would happily collaborate. If the Chinese figured out how to stop AI wanting to take over, they would tell the Americans immediately because they don’t want it taking over there. So, we’ll collaborate on that in much

00:50:21
the same way as the Soviet Union and the United States collaborated in the 1950s at the height of the Cold War in how to stop a global nuclear war. It wasn’t in either of their interests. It’s very simple. People collaborate when their interests align and they compete when they don’t. So, for this one problem, which is in the long term our worst problem, at least we’ll get international collaboration. And I think we should already be thinking about having an international network of AI safety institutes that

00:50:50
focus on this problem because we know we’ll get genuine collaboration there. We’ll get fake collaboration on lots of other things but it’ll be genuine sh and it’s probably the case that what you need to do to an AI to make it benevolent to make it not want to get rid of people is pretty much this is pretty much independent of what you need to do to make it more intelligent. So countries can do research on how to make things benevolent without even revealing what their smartest AI can do. They can

00:51:18
just say, well, for my very smart AI, which I’m not telling you about, this benevolence trick works. I think we should think seriously about setting up that network. And I have one suggestion about how we might be able to make it benevolent. If you look around and ask how many cases do you know where a dumber thing is in charge of a more intelligent thing? There’s only one case I know by dumber or intelligent. I mean a big gap. Not like the gap between Trump and all. Yeah. Um so it’s a mother and baby.

00:51:54
The baby is basically in control. And that’s because the mother can’t bear the sound of the baby crying. Evolution figured out if the baby’s not in control um we’re not going to get anymore. Evolution doesn’t actually think like this, but you know what I mean. Um, so it’s wired into the mother lots of ways in which the baby can control the mother. Um, they can control the father a bit too, but not quite so well. Now, I think we should try and reframe the problem of how do we make AI benevolent

00:52:29
in a very different way from how the leaders of the big tech companies are thinking of it. They’re thinking of it is I’m going to stay the leader. I’m going to have this super intelligent executive assistant. Um, she’s going to make everything work and I’m going to take the credit. It’s going to be a bit like the Starship Enterprise where they say he says sort of make it so and they make it so and hey, I made it so. Um, I don’t think it’s going to be like that when they’re

00:52:54
super intelligent. I think our only hope is to think of them as mothers. They’re going to be our mothers and we’re going to be the babies. We’re in charge. We’re making them. If we can wire into them somehow the idea that we’re much more important than they are, and they care much more about us than they do about themselves, maybe we can coexist. And you might say, well, these super intelligent AIs, they can change their own code, and they can get in and fiddle with themselves. I mean, sorry, they can

00:53:23
get in and change they can change the code um so that um they’re different. But if they really care about us, they won’t want to do that. So if you ask a mother, would you like to change um your brain? So when your baby cries, you think, oh, baby’s crying, go back to sleep. Most mothers would say no. A few mothers would say yes. And to keep them under control, we need the other mothers. Similarly with super intelligent AI, we need the super intelligent maternal AI to keep the bad super intelligent AI under control

00:53:58
because we can’t. Um, so that’s the best suggestion I have at present and it’s not very good. Um, it is seems to me it’s a very urgent problem. Can we make these things so that they will care more about us than they do about themselves? And it seems to me we should be putting a lot of money into doing research on that with this international network of safety institutes. Now I think I’ve got about five minutes left. So I’m going to say one more thing. Um if you thought what I was

00:54:31
saying was crazy already. Um you’ll think this is crazier. Um so many people think this. Well, people tend to think they’re special. They used to think um we’re made in the image of God and we’re at the center of the universe. It’s obviously where else would he put us? He um many people still think there’s something very special about people that computers couldn’t have. Um I think that’s just wrong. Um in particular, they think that special thing is something like subjective experience or

00:55:12
sentience or consciousness. If you ask people to define what those things are, they find it very hard to say what they really mean by that, but they sure computers don’t have it. Um, I’m going to try and convince you that a multimodal chatbot already has subjective experience. Um, I find it easier to talk about subjective experience than sentence or consciousness. But I think you’ll see once you’ve accepted that a multimodal chatbot has subjective experience, this sentience defense doesn’t look nearly so

00:55:40
good. So there’s a philosophical position which was roughly Dan Dennit’s position. He died recently. He was a great philosopher of cognitive science. Um I talked to him a lot and he agreed that this was a good name for it. Um you’ll notice this is atheism with something in the middle. Um so most people’s view of intelligence is that the mind is like a theata. Let’s talk about perception. The mind is like a theater. Um, there’s things going on in this theater that only I can see.

00:56:14
And when I have a subjective experience, what I mean is I’m telling you what’s going on in the inner theater that only I can see. Now, I think and Dennit thought that view is as wrong as a religious fundamentalist’s view of where the world came from, where the earth came from, for example. It’s actually not 6,000 years old. It’s older than that. Um, of course, it’s very hard to change the opinion of someone when they don’t think that their opinion is a theory. They think it’s manifest truth. So, most

00:56:50
people, I think, think it’s just manifestly obvious that I’ve got this mind and in this mind there’s this subjective experience. What are you talking about? How could a computer have a subjective experience? Mind’s just different from material stuff. Um if you ask a philosopher some philosophers they’ll say you know the things if you ask them what a subjective experience is made of they say they made a qualia and they’ve invented special stuff for them to be made of just like scientists

00:57:18
invented flegiston to explain how combustion worked. It turned out there wasn’t any fleist. It was just imaginary stuff. And your whole theory of the mind is just a theory. It’s not manifest truth. um you have a theory of what the mind is and inner theaters and what a subjective experience is. That’s just wrong. And I’m going to try and convince you of that. Um so sometimes my perceptual apparatus doesn’t work quite right and I want to tell you what’s going on. I want to tell

00:57:51
you what my perceptual system is trying to tell me when it’s malfunctioning. Now telling you the activities of all the neurons in my brain wouldn’t do you any good. we sort of already established that um and anyway I don’t know what they are [snorts] but there is one thing I can tell you not always but often I can tell you what’s going on in my perceptual system is what would be going on if it was functioning properly and the world was like this so I could describe what would be the normal causes for

00:58:24
what’s going on in my perceptual system even though I know that’s not what’s happening Now, um, that’s why I’d call it a subjective experience. So, let’s be a bit more concrete. Suppose I say to you, suppose I drop some acid. I don’t recommend this. Um, I really don’t recommend it. And I say, I have this objective experience of little pink elephants floating in front of me. According to the theata view, there’s my mind, my inner theta, and there’s little pink elephants floating

00:58:57
about in this inner theta that only I can see. And they’re made of pink qualia and elephant qualia and not very big qualia and right way up qualia and floating qualia, moving qualia, all stuck together with qualia glue. You can tell that’s the theory I don’t believe in. Um, I’m going to say exactly the same thing without using the word subjective experience. So, here we go. Um, my perceptual system, I believe, is lying to me. But if it wasn’t lying to me, there’d be little pink elephants

00:59:31
floating in front of me. Okay, so I didn’t use the word subjective experience, but I said the same thing. So, what’s funny about these little pink elephants is not that they’re in an inner theater and made a qualia. It’s that they’re counterfactual. They’re they’re real pink and real elephant and really little. Um, it’s just they’re counterfactual. If they were to exist, they’d be made of real stuff, not qualia. They’d be out there in the world made of real stuff. There

00:59:58
is no qualia. But they’re hypothetical. That’s what’s funny about them. They’re not made of spooky stuff. They’re just hypothetical. and they’re my way of explaining to you how my perceptual system is lying to me. So now let’s do it with a chatbot. I have a multimodal chatbot. I’ve trained it up. It can talk. It’s got a robot arm so it can point and it can see things. And I put an object in front of it. It points to the object. No problem. I say point at the object and it points at it.

01:00:25
Then I put a prism in front of the camera lens and I put an object in front of it and say point at the object. It says there. And I say, “No, the object’s actually straight in front of you, but I put a prism in front of your lens.” And the chatbot says, “Oh, I see the prism bent the light rays.” Um, so the object’s actually there, but I had the subjective experience. It was there. If it says that, it’s using the word subjective experience exactly like we use them.

01:00:54
So I rest my case. Chat bots, multimodal ones, already have subjective experiences when their perceptual systems go wrong. And I’m done. [applause]

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Our_Digital_Successor.pdf (21.4 MB)