Name: 理性主義的極限：從蘇格拉底到神經網絡 | Dan Shipper：完整訪談 (The limits of rationalism: From Socrates to neural networks | Dan Shipper: Full Interview)
Uploaded: 2025-11-21T18:19:36.000Z
Duration: 57 min 47 s

to 不定詞

I get to talk to people all the time about how they use AI in their work and in their lives, and

我經常跟人們聊他們如何在工作和生活中運用 AI，

以及它如何改變了他們這個人。

基礎數量詞

There's many different ways of knowing things and many different ways of understanding things.

我們有很多種認識事物的方式，也有很多種理解事物的方式。

What both of those ways of seeing the world are trying to do is reduce the world into a set of really clean universal laws that apply in any situation.

這兩種看待世界的方式都在試圖將世界簡化成一套非常清晰、適用於任何情況的普世法則。

如果 X 為真，那麼 Y 就會發生。

And what language models see instead is a dense web of causal relationships between different parts of the world that all come together in unique, very context specific ways to produce what comes next.

而語言模型看到的是一個由世界不同部分之間因果關係組成的密集網絡，這些關係以獨特、非常具體的情境方式匯集在一起，產生接下來的結果。

And what's really interesting about neural networks is the way that they think or the way that they operate is a lot like human intuition.

類神經網路真正有趣的地方在於，它們思考或運作的方式，很像人類的直覺。

Human intuition is also trained by thousands and thousands and thousands of hours of direct experience.

人類的直覺也是透過成千上萬小時的直接經驗所培養出來的。

The reason I love that is because I hope that it makes more visible to us the value and importance of intuitive thought.

我之所以熱愛這一點，是因為我希望它能讓我們更清楚地看到直覺思考的價值和重要性。

I'm the co founder and CEO of EVERY and I'm the host of the AI and I podcast.

我是 EVERY 的共同創辦人兼 CEO，也是 AI and I podcast 的主持人。

Chapter one, the Limits of Rationalism from Socrates to Neural Networks I think rationalism is one of the most important ideas in the last 2000 years.

第一章，從蘇格拉底到類神經網路的理性主義極限，我認為理性主義是過去兩千年來最重要的概念之一。

Rationalism is really the idea that if we can be explicit about what we know, if we can really reduce what we know down into a set of theories, a set of rules for how the world works, that is true knowledge about the world and that is distinct from everything else that kind of messes with our heads, messes with how we do, how we, how we operate in society.

理性主義其實就是這個想法：如果我們能清楚說明我們所知道的，如果我們能把我們所知道的濃縮成一套理論、一套關於世界如何運作的規則，那就是關於世界的真實知識，而且這跟那些會擾亂我們思緒、影響我們行為、影響我們在社會上運作的一切事物都有所區別。

And you may not have heard that word, or maybe you have, but

你可能沒聽過這個詞，或者你聽過，但

it is built into the way that you see the world.

它已經內建在你觀察世界的方式裡了。

For example, the way computers work, or the way vaccines work, or the way that we predict the weather, or the way that we try to make decisions when we're thinking about.

舉例來說，電腦的運作方式、疫苗的作用方式、我們預測天氣的方式，或是我們在思考時試圖做決策的方式。

I don't want to be too emotional about this.

I want to get really precise about my thinking on this issue.

我希望能精確地闡述我對這個議題的想法。

Even the way that we do therapy, a lot of therapy is about rationalizing or rationalizing through what you think and what you feel.

甚至連我們進行心理治療的方式，很多治療都是在試圖理性化或透過理性來理解你的想法和感受。

All that stuff comes from an extensive lineage of ideas that started in ancient Greece, really blossomed during the Enlightenment, and

這些東西都源自於一個源遠流長的觀念傳承，它始於古希臘，在啟蒙時期蓬勃發展，

now is like the bedrock of our culture and the way that we think about the world.

如今已成為我們文化的基石，以及我們看待世界的方式。

I think the father of rationalism is Socrates, the philosopher.

我認為理性主義之父是哲學家蘇格拉底。

Socrates is one of the first people to really examine the question of what we know and how, what is true and what's not true to be able to describe what we know.

蘇格拉底是第一批真正探究「我們知道什麼、如何知道、什麼是真的、什麼不是真的」這些問題的人，以便闡述我們所知道的。

And how we know it to make that clear and explicit so that only people that knew the truth, that knew how the world really works, were the ones that were steering the state.

以及我們如何知道它，讓它清晰明確，這樣一來，只有那些知道真相、知道世界真正運作方式的人，才能引導國家。

That really became the birth of philosophy is this idea that if you inquire deeply into what is usually kind of like the inexplicit intuitions that we have about the world, you can identify a set of rules or a theory about what the world is like and what's true and what's not that you can lay out explicitly and that you can use to decide the difference between true and false.

這確實是哲學的誕生，這個想法是，如果你深入探究我們通常對世界所抱持的那些不明確的直覺，你就能找出一些規則或理論，說明世界是什麼樣子的，什麼是真的、什麼不是，你可以明確地闡述出來，並用來分辨真假。

I think that you can trace the birth of rationalism to this dialogue Protagoras.

我認為你可以追溯理性主義的誕生到這段對話《普羅泰戈拉》。

And in the dialogue, it's a debate between Socrates on the one hand and Protagoras.

在這段對話中，是蘇格拉底和普羅泰戈拉之間的辯論。

And Protagoras is what we call a sophist.

而普羅泰戈拉是我們所謂的詭辯家。

And it's where the term sophistry, which means like kind of, you know, someone who says really compelling things but is actually full of shit.

這也是「詭辯」這個詞的由來，意思是，你知道的，某個說話很有說服力，但其實都是胡說八道的人。

What Protagoras and Socrates are debating is can excellence be taught?

普羅泰戈拉和蘇格拉底辯論的重點是，卓越是否能被教導？

And excellence, the word is often translated in English as virtue, but

而「卓越」這個詞，英文常翻譯成「virtue」，但

I think a more appropriate translation is excellence.

我認為更貼切的翻譯是「excellence」。

And in ancient Greece, like that kind of excellence was, was really prized.

在古希臘，這種卓越是被非常重視的。

It's sort of like, it's sort of like a general ability to.

差不多就像是，差不多就像是一種普遍的能力，能夠

Be good at important things in life and in society.

在人生和社會的重要事務上表現出色。

And they approach it from very different angles.

他們從非常不同的角度來探討這個問題。

Protagoras believes that everyone has the capacity, every human has the capacity to be excellent.

普羅泰戈拉認為每個人都有這個能力，每個人類都有變得卓越的潛力。

And he tells this big myth about how we as humans gained the capacity to be excellent.

他講了一個很棒的神話，關於我們人類是如何獲得這種卓越能力的。

And Socrates is saying, no, no, no, I don't want any of that.

而蘇格拉底則說，不行不行不行，我不要那些。

I want you to say explicitly what it is and what it's not and what are the components of it.

我希望你明確地說出它是什麼，不是什麼，以及它的組成是什麼。

And that's a really big moment, at least the way that Plato writes it.

這是一個非常重要的時刻，至少在柏拉圖的寫法裡是這樣。

Socrates kind of like takes apart Protagoras and it's pretty clear by the end that Protagoras doesn't know, doesn't have any way to define in a non contradictory way what excellence is, what it means to be good.

蘇格拉底有點像拆解了普羅泰戈拉，到最後很清楚普羅泰戈拉不知道，也無法用一種不矛盾的方式定義何謂卓越，何謂善良。

And the implication is that then he doesn't know it.

And that sort of set Western society on this path of trying to find really clear definitions and theories for the things that we talk about and to identify knowledge, the ability to know something, or whether or not you know something with whether or not you can really clearly define it.

這也讓西方社會走上了試圖為我們討論的事物尋找清晰定義和理論的道路，並將知識、知道某事的能力，或是你是否知道某事，與你是否能清晰定義它連結在一起。

And that idea became incredibly important in the scientific enlightenment thinkers on the philosophy side, like Descartes and on the science side, like Newton and Galileo, took this idea and used it as a new method to understand and explain the world.

這個想法在科學啟蒙思想家（如笛卡兒）和科學家（如牛頓和伽利略）身上變得極其重要，他們採用這個想法作為理解和解釋世界的新方法。

So what it became is can we use mathematics to explain and predict different things in the world?

所以，它變成了一個問題：我們能否利用數學來解釋和預測世界上的各種事物？

And from Socrates, the Galileo to Newton, they continually reinforced this idea that in order to truly know something, you have to be able to describe it explicitly, you have to have a theory about it, you have to be able to describe it mathematically.

從蘇格拉底到伽利略再到牛頓，他們不斷地強化這個觀念：為了真正瞭解某件事，你必須能夠明確地描述它，你必須對它有一個理論，你必須能夠用數學來描述它。

Ideally, the world around us is shaped by this framework.

理想情況下，我們周遭的世界是由這個框架塑造的。

So everything from smartphones to computers to cars to rockets to cameras to electricity, every appliance in your house, vaccines, everything in our world is shaped with this idea or this way of seeing the world.

所以，從智慧型手機、電腦、汽車、火箭、相機、電力，你家裡的各種家電、疫苗，我們這個世界的一切，都是受到這個想法或這個看待世界的方式所塑造的。

And you can find this too in the rest of culture.

你也可以在其他文化領域找到這個現象。

Anytime you see a book or a movie or a blog post or whatever, talking about the five laws of power or the five laws of negotiation, all that stuff is ways that physics and rationalism in general has sort of seeped into the everyday, everyday way that we think about the world.

任何時候，當你看到一本書、一部電影、一篇部落格文章，或是任何在談論五大權力法則或五大談判法則的東西，這些都是物理學和理性主義總體上滲透到我們日常思考世界的方式。

And to be clear, it's been super successful.

要說清楚一點，這確實非常成功。

But in areas of the world like psychology or economics or neuroscience, it has been really hard to make progress in the same way that physics has made progress.

但在像心理學、經濟學或神經科學這些領域，要取得像物理學那樣的進展，一直都非常困難。

I think if you look, for example, at the social sciences, a lot of the way that the social sciences are structured is inspired by physics.

我認為，如果你看看社會科學，很多社會科學的結構方式都是從物理學獲得靈感的。

What we're trying to do is take very complex higher level phenomena, so like maybe psychology or economics or any other branch of social science, and

我們試圖做的是，將非常複雜的高層次現象，像是心理學、經濟學，或是任何其他社會科學的分支，

we're trying to reduce it down to a set of definitions and a theory and a set of rules for how things in that domain work.

並試圖將它們簡化為一套定義、一個理論，以及一套關於該領域事物運作規則的集合。

And what's really interesting is if you look at those fields.

而真正有趣的是，如果你看看這些領域。

So like psychology, for example, it's in the middle of a gigantic replication crisis.

所以像是心理學，它正經歷一個巨大的再現危機。

Even though we spent 100 years doing psychology research, the body of knowledge that we've been able to build there, in terms of its universal applicability, our ability to find universal laws in the same way that Newton found universal laws seems pretty suspect.

儘管我們做了 100 年的心理學研究，但我們在那裡建立的知識體系，就其普遍適用性而言，我們找到像牛頓發現的普適定律的能力，似乎相當可疑。

And we feel like we can't stop doing it because we have no better alternative.

而且我們覺得我們不能停止，因為我們沒有更好的替代方案。

Another really interesting, an important part of the world that this way of looking at things didn't work for in many ways is AI.

另一個真正有趣、重要且這個觀看方式在很多方面都行不通的領域是 AI。

So this is usually the part of an explanation where I try to define it.

所以這通常是我試圖定義它的解釋部分。

And what's really interesting is there's no universal, agreed upon definition for this.

而真正有趣的是，沒有一個普遍、大家同意的定義。

We've struggled to come up with a universal definition for what it is to know something or a universal definition for what anxiety is.

我們努力想出一個關於「知道某事」的普適定義，或是一個關於「焦慮」的普適定義。

For example, in psychology is another really good example.

例如，心理學是另一個很好的例子。

There are a lot of ways to kind of gesture at what AI is, but

really it's like obviously, Maybe not obviously, AI stands for artificial intelligence.

And the AI's project is to build a computer that can think and learn in the same way that humans learn.

And because of the way that computers work, for a very long time, that was a really hard problem.

AI started as a field in the 50s at Dartmouth.

And you can actually look at the original paper, they were very optimistic.

有很多方法可以大致說明 AI 是什麼，但

They were like, maybe a summer's worth of work and we'll have nailed this.

其實，這就像，也許不是那麼明顯，AI 代表人工智慧。

And the way that they defined it is to be able to reduce down human intelligence into a system of symbols that they could combine together based on explicit rules that would mimic human intelligence.

而 AI 的目標是建造一臺能像人類一樣思考和學習的電腦。

And so there's a really clear through line from Socrates original project to the Enlightenment to the original approach that AI theorists took called symbolic AI.

由於電腦的運作方式，很長一段時間以來，這是一個非常困難的問題。

The idea that you could embody thinking in essentially like logic, logical symbols, and

AI 在 50 年代於達特茅斯學院發跡。

transformations between logical symbols, which is, it's very similar to just basic philosophy.

轉換邏輯符號，這，這跟基本哲學很像。

And there are actually a lot of early successes.

而且早期確實有很多成功的例子。

For example, the two founding fathers of AI, Herbert Simon and Alan Newell, built this machine that they called the general problem solver.

舉例來說，AI 的兩位創始者，Herbert Simon 和 Alan Newell，他們打造了一臺叫做「通用問題求解器」的機器。

And what's really interesting is it wasn't even built as a computer because computers were extremely expensive back then.

有趣的是，這臺機器一開始並不是用電腦做的，因為當時電腦非常昂貴。

They originally codified the general problem solver on paper and then executed it themselves by hand.

他們原本是把通用問題求解器寫在紙上，然後親手執行。

Actually, I think one of them had their family do it with them to try to, to try to simulate how a computer would work to solve complex problems.

我認為他們其中一人甚至讓家人一起幫忙，試圖模擬電腦如何解決複雜問題。

And the General problem Solver, they tried to reduce down complex real world situations into simple logical logic problems that look a little bit like games.

而通用問題求解器，他們試圖將複雜的真實世界情況，簡化成簡單的邏輯問題，有點像遊戲。

And then they tried to see if they could build a computer that would solve some of those games.

然後他們試圖看看能不能打造一臺電腦來解決其中一些遊戲。

And they were actually quite successful at first.

而且一開始他們確實相當成功。

What they found was it worked really well for simple problems.

他們發現，這個方法對簡單的問題非常有效。

But as problems got more and more complex, the search space of possible solutions got really, really, really, really big.

但是隨著問題越來越複雜，可能的解決方案的搜尋空間也變得非常非常非常龐大。

And so by representing the problem in that way, the systems that they built started to fail as soon as they moved away from toy problems to more complex ones.

所以透過這樣的方式來表示問題，當他們從簡單的「玩具問題」轉向更複雜的問題時，他們建立的系統就開始失效了。

I think a really interesting and simple example of this is thinking about how you might decide whether an email in your inbox is spam or whether it's important.

我認為一個非常有趣且簡單的例子是，想想你該如何判斷收件匣裡的一封電子郵件是垃圾郵件還是重要郵件。

And you might say something like, if it mentions that I won the lottery, it's spam, right?

你可能會說，像是如果它提到我中了樂透，那就是垃圾郵件，對吧？

And so that sort of like if then rule is a lot like the kinds of rules that early symbolic AI theorists were trying to come up with to help you solve any problem is to codify.

所以這種「如果...那麼...」的規則，就很像早期符號 AI 理論家試圖想出來幫助你解決任何問題的規則，也就是將規則編碼。

Like, if XYZ is true, then here are the implications.

像是，如果 XYZ 為真，那麼這裡有它的推論。

What happens is, if you look at that really closely, there are always lots of little exceptions.

結果是，如果你仔細看，總是有很多小小的例外。

So an example might be if it says emergency, maybe you want to put that at the top of your inbox.

所以一個例子可能是，如果它寫著緊急，也許你想把這封郵件放在收件匣最上面。

But very quickly you'll have spammers, obviously, being like, just put emergency in the subject line and they'll shoot to the top.

但很快地，你會發現寄件人顯然會說，只要在主旨裡寫上緊急，它們就會衝到最上面。

So then you have to kind of like, create another rule, which is, it's emergency, but

所以你必須有點像，再創立一個規則，那就是，它是緊急的，但是

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理性主義的極限：從蘇格拉底到神經網絡 | Dan Shipper：完整訪談 (The limits of rationalism: From Socrates to neural networks | Dan Shipper: Full Interview)

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