章节 1：缓慢起飞的科幻现实与抽象的经济影响

📝 本节摘要：

本节讨论了当前 AI 发展的奇特状态：尽管技术进步如同科幻小说成真，但这种“缓慢起飞（slow takeoff）”让一切感觉异常普通。Ilya 和 Dwarkesh 探讨了尽管新闻中充斥着巨额投资公告，但普通人尚未在日常生活中感受到实质性的变化，这种影响目前仍停留在抽象层面。

[原文] [Dwarkesh]: you know what's crazy that all of this is real yeah meaning what don't you think so meaning what like all this AI stuff and all this area yeah that it's happen like isn't it straight out of science fiction yeah

[译文] [Dwarkesh]: 你知道什么很疯狂吗？这一切都是真实的。是啊，这意味着什么？你不这么认为吗？意味着像所有这些 AI 的东西以及这整个领域，是啊，它正在发生，这难道不是直接出自科幻小说吗？是啊。

[原文] [Dwarkesh]: another thing that's crazy is like how normal the slow takeoff feels the idea that we'd be investing 1% of GDP in AI like I feel like it would have felt like a bigger deal you know where right now it just feels like we get used to things pretty fast turns out

[译文] [Dwarkesh]: 另一件疯狂的事情是，这种“缓慢起飞（slow takeoff）”感觉是多么正常。我们将 GDP 的 1% 投资于 AI 这种想法，我觉得这本该感觉像是一件更重大的事，你知道吗？而现在感觉只是——事实证明我们适应事物的速度相当快。

[原文] [Ilya]: Yeah but also it's kind of like it's abstract like what does it mean what it means that you see it in the news that such and such company announced such and such dollar amount right that's that's all you see right it's not really felt in any other way so far yeah

[译文] [Ilya]: 是的，但也因为它有点抽象。比如这意味什么？这意味着你在新闻里看到某某公司宣布了某某金额的投资，对吧？这就是你看到的全部，对吧？目前在任何其他方面并没有真正感受到它。是啊。

[原文] [Dwarkesh]: should we actually begin here i think this is an interesting discussion sure i think your point about well from the average person's point of view nothing is that different will continue being true even into the singularity no I don't think so okay interesting so

[译文] [Dwarkesh]: 我们要不就从这里开始？我觉得这是一个有趣的讨论。当然。我认为你的观点是，从普通人的角度来看，没什么太大的不同。这种情况甚至会一直持续到奇点（Singularity）吗？不，我不这么认为。好的，有趣，那么……

章节 2：高分低能？模型评估与现实效用的脱节

📝 本节摘要：

这一章节深入探讨了一个令人困惑的现象：AI 模型在复杂的评估（evals）中表现优异，但其产生的实际经济影响却远滞后于此。Ilya 举了一个生动的“修复 Bug”的例子，指出模型常常在被要求修复错误时引入新错误，表现出一种奇怪的循环。他提出这可能是因为强化学习（RL）训练让模型变得过于狭隘和缺乏自我意识，或者是因为 RL 训练数据的选择比预训练（Pre-training）更加困难且容易由人为偏见导致“过度拟合”评估标准。

[原文] [Ilya]: the thing which I was referring to not feeling different is okay so such and such company announced some difficult to comprehend dollar amount of investment right I don't think anyone knows what to do with that but I think that the impact of AI is going to be felt ai is going to be diffused through the economy there are very strong economic forces for this and I think the impact is going to be felt very strongly

[译文] [Ilya]: 我指的“感觉没什么不同”是，好吧，某某公司宣布了一些难以理解的投资金额，对吧？我不认为有人知道该怎么应对这个。但我认为 AI 的影响将会被感受到，AI 将会渗透到整个经济中，这背后有非常强大的经济力量，我认为这种影响将会非常强烈地被感受到。

[原文] [Dwarkesh]: when do you expect that impact i think the models seem smarter than their economic impact would imply yeah this is one of the very confusing things about the models right now how to reconcile the fact that they are doing so well on evals mhm and you look at the evals and you go those are pretty hard evals right they're doing so well but the economic impact seems to be dramatically behind and it's almost like it's it's very difficult to make sense of how can the model on the one hand do these amazing things and then on the other hand like repeat itself twice in some situation in

[译文] [Dwarkesh]: 你预期这种影响何时到来？我觉得模型似乎比它们体现出的经济影响要更聪明。是啊，这是目前关于模型非常令人困惑的事情之一。如何调和这一事实：它们在评估（evals）上表现得如此之好——嗯哼——你看着那些评估会说，那些是相当难的评估，对吧？它们表现得这么好，但经济影响似乎极其滞后。这就好像，很难理解模型怎么能一方面做这些惊人的事情，而在另一方面，比如在某些情况下重复自己两次……

[原文] [Ilya]: a kind of a an example would be let's say you use VIP coding to do something and you go to some place and then you get a bug and then you tell the model can you please fix the bug yeah and the model says "Oh my god you're so right i have a bug let me go fix that." And it introduces a second bug and then you tell it you have you have this new the second bug and it tells you "Oh my god how could I've done it you're so right again." And brings back the first bug and you can alternate between those and it's like how is that possible it's like I'm not sure but it does suggest that the something strange is going on

[译文] [Ilya]: 一个例子可能是，假设你使用 VIP 编程（注：可能指某种AI辅助编程工具）做某事，你到了某个地方，然后遇到了一个 Bug，接着你告诉模型“你能修复这个 Bug 吗？”是啊。模型说“哦天哪，你是对的，我有一个 Bug，让我去修复它。”然后它引入了第二个 Bug。接着你告诉它你有这个新的第二个 Bug，它告诉你“哦天哪，我怎么会这样做，你又是对的。”然后它又把第一个 Bug 带回来了。你可以在这两个 Bug 之间来回循环，这就像是，这怎么可能？就像我也不确定，但这确实表明有一些奇怪的事情正在发生。

[原文] [Ilya]: i have two possible explanations so here this is the more kind of a whimsical explanation is that maybe ARL training makes the models a little bit too single-minded and narrowly focused a little bit too I don't know unaware even though it also makes them aware in some other ways and because of this they can't do basic things

[译文] [Ilya]: 我有两个可能的解释。这一个是比较异想天开的解释：也许强化学习（RL）训练让模型变得有点过于一根筋和视野狭窄，有点太——我不知道——缺乏意识，尽管它也在其他方面让它们变得有意识。正因为如此，它们无法做基本的事情。

[原文] [Ilya]: but there is another explanation which is back when people were doing pre-training the question of what data to train on was answered because the that answer was everything when you do pre-training you need all the data so you don't have to think is it going to be this data or that data

[译文] [Ilya]: 但还有另一个解释，就是回到人们做预训练（pre-training）的时候，关于“用什么数据训练”的问题已经有了答案，因为那个答案是“所有东西”。当你做预训练时，你需要所有的数据，所以你不必思考是要用这个数据还是那个数据。

[原文] [Ilya]: but when people do RL training they do need to think they say okay we want to have this kind of RL training for this thing and that kind of RL training for that thing and from what I hear all the companies have teams that just produce new RL environments and just add it to the training mix and then the question is well what are those there are so many degrees of freedom there is such a huge variety of real environments you could produce

[译文] [Ilya]: 但是当人们做 RL 训练时，他们确实需要思考。他们会说，好吧，我们想要针对这个东西进行这种 RL 训练，针对那个东西进行那种 RL 训练。据我所知，所有公司都有专门的团队生产新的 RL 环境，并将其加入到训练组合中。那么问题来了，那些是什么？这里有太多的自由度（degrees of freedom），你能制造出的真实环境种类如此繁多。

[原文] [Ilya]: and one of the one thing you could do and I think that's something that is done inadvertently is that people take inspiration from the evals you say "Hey I would love our model to do really well when we release it i want the evos to look great what would be RL training that could help on this task right?" I think that is something that happens and I think it could explain a lot of what's going on if you combine this with generalization of the models actually being inadequate

[译文] [Ilya]: 其中你能做的一件事——而且我认为这是在不经意间发生的——就是人们从评估（evals）中获取灵感。你会说“嘿，我希望我们的模型在发布时表现得非常好，我希望评估结果看起来很棒，什么样的 RL 训练能在这种任务上有所帮助呢？对吧？”我认为这种事确实在发生。如果你把这与模型泛化能力实际上的不足结合起来，我认为这可以解释很多正在发生的事情。

章节 3：“竞技程序员”陷阱：过度拟合与泛化能力的缺失

📝 本节摘要：

为了解释模型为何在特定任务上超强但在通用任务上笨拙，Ilya 提出了一个精彩的类比：就像两名学生，一名为了竞赛练习了 10,000 小时（死记硬背所有解题技巧），另一名只练习了 100 小时但具有真正的领悟力（"it factor"）。目前的模型更像前者，被过度训练以在特定“竞赛”（评估）中获胜，从而牺牲了在未见过的现实场景中的泛化能力。

[原文] [Ilya]: I have an anal a human analogy which might be helpful so even the case let's take the case of competitive programming since you mentioned that and suppose you have two students one of them work decided they want to be the best competitive programmer so they will practice 10,000 hours for that domain they will solve all the problems memorize all the proof techniques and be very very you know be very skilled at quickly and correctly implementing all the algorithms and by doing by doing so they became the best one of the best

[译文] [Ilya]: 我有一个人类的比喻可能很有帮助。就拿竞技编程为例，既然你提到了这个。假设你有两个学生，其中一个决定他们想成为最好的竞技程序员，所以他们会在那个领域练习 10,000 小时。他们会解决所有问题，背诵所有的证明技巧，并且非常非常——你知道——非常熟练于快速且正确地实现所有算法。通过这样做，他们成为了最好的，最好的之一。

[原文] [Ilya]: student number two thought oh competitive programming is school maybe they practiced for 100 hours much much less and they also did really well which one do you think is going to do better in their career later on

[译文] [Ilya]: 二号学生觉得，哦，竞技编程就是学校作业，也许他们只练习了 100 小时，少得多，但他们也做得非常好。你认为哪一个在以后的职业生涯中会做得更好？

[原文] [Dwarkesh]: the second right

[译文] [Dwarkesh]: 第二个，对吧。

[原文] [Ilya]: and I think that's basically what's going on the models are much more like the first student but even more because then we say okay so the model should be good at competitive programming let's get every single competitive programming problem ever and then let's do some data augmentation so we have even more competitive programming problems yes and we train on that and so now you got this great competitive programmer

[译文] [Ilya]: 我认为这基本上就是正在发生的事情。模型更像是第一个学生，甚至程度更甚。因为我们会说，好吧，模型应该擅长竞技编程，让我们把史上所有的竞技编程问题都找来，然后做一些数据增强，这样我们就有了更多的竞技编程问题。是的，然后我们在那上面训练。所以现在你得到了这个很棒的竞技程序员。

[原文] [Ilya]: and with this analogy I think it's more intuitive i think it's more intuitive with this analogy that Yeah okay so if it's so well trained okay it's like all the different algorithms and all the different proof techniques are like right at it at its fingertips and it's more intuitive that with this level of preparation it not would not necessarily generalize to other things

[译文] [Ilya]: 有了这个比喻，我认为这更直观了。我认为通过这个比喻更直观的是：是的，好吧，如果它被训练得这么好，所有的不同算法和所有的不同证明技巧就像都在它的指尖一样。但也更直观的是，有了这种程度的准备，它并不一定能泛化到其他事情上。

[原文] [Dwarkesh]: but then what is the um analogy for what the second student is doing before they do the 100 hours of fine-tuning i think it's like they have it i think it's the it factor right and like I know like when I was in undergrad I remember there was there was a student like this that studied with me so I know I know it exists

[译文] [Dwarkesh]: 但是，第二个学生在做那 100 小时的微调之前，他们在做什么？这个比喻对应的是什么？我觉得就像是他们拥有那个东西，我觉得就是那种“天赋（it factor）”，对吧？就像我知道，当我在读本科的时候，我记得有一个跟我一起学习的学生就是这样，所以我知道，我知道这种人是存在的。

章节 4：预训练的本质与人类学习的奥秘

📝 本节摘要：

对话转向探讨“预训练（Pre-training）”的本质。Dwarkesh 试图寻找预训练的人类对应物（如童年成长期或生物进化）。Ilya 承认有一些相似之处，但指出了一个巨大的差异：数据量。预训练使用海量数据，而人类仅凭极少的数据就能获得更深层的理解和极其稳健的直觉。他引入了神经科学的案例（如情绪中枢受损的病人），暗示人类的学习不仅仅是数据积累，可能还涉及某种内置的、由进化赋予的“价值函数（value function）”。

[原文] [Dwarkesh]: yeah i think it's interesting to distinguish it from whatever pre-training does so one way to understand what you just said about we don't have to choose the data in pre-training is to say actually it's not dissimilar to the 10,000 hours of practice it's just that you get that 10,000 hours of practice for free because it's already somewhere in the pre-training distribution

[译文] [Dwarkesh]: 是的，我认为把它与预训练所做的区分开来很有趣。理解你刚才说的“我们不需要在预训练中选择数据”的一种方式是，实际上这与那 10,000 小时的练习并没有什么不同，只是你免费获得了那 10,000 小时的练习，因为它已经存在于预训练的分布中了。

[原文] [Ilya]: but it's like maybe you're suggesting actually there's actually not that much generalization from pre-training there's just so much data in pre-training everybody's like it's not necessarily generalizing better than RL like the main the main strength of pre-training is that there is a so much of it and b you don't have to think hard about what data to put into pre-training and it's a very kind of natural data and it does include in it a lot of what people do yeah people's thoughts and a lot of the features of you know it's like the whole world as projected by people onto text and pre-training tries to capture that using a huge amount of data

[译文] [Ilya]: 但就像你可能是在暗示，实际上预训练并没有带来那么多的泛化，只是因为预训练中有太多的数据。大家都在说，它不一定比 RL 泛化得更好。就像预训练的主要优势在于：A）它有如此多的数据；B）你不必费劲去想该把什么数据放入预训练中。而且它是非常自然的数据，它确实包含了很多人们做的事情，是的，人们的思想和很多特征。你知道，这就像是整个世界被人们投射到了文本上，而预训练试图使用大量数据来捕捉这一点。

[原文] [Dwarkesh]: here's analogies that people have proposed for what the human analogy to pre-training is and I'm curious to get your thoughts on why they're potentially wrong one is to think about the first 18 or 15 or 13 years of a person's life when they aren't necessarily economically productive but they are doing something that is making them understand the world better and so forth and the other is to think about evolution as doing some kind of search for three billion years which then results in a human lifetime instance and then I'm I'm curious if you think either of these are actually analogous to pre-training or how how would you think about at least what lifetime human learning is like if not pre-training

[译文] [Dwarkesh]: 这里有一些人们提出的关于“预训练的人类对应物”的比喻，我很好奇你对它们为什么可能是错的的看法。一种是把它看作一个人生命中的前 18 年、15 年或 13 年，那时他们不一定具有经济生产力，但他们在做一些让他们更好地理解世界之类的事情。另一种是把进化看作是进行了 30 亿年的某种搜索，然后产生了一个人类生命的实例。我很好奇你是否认为这其中任何一个实际上可以类比预训练，或者你会如何思考——至少人类一生的学习像什么，如果不是像预训练的话？

[原文] [Ilya]: i think there are some similarities between both of these two pre-training and pre-training tries to play the role of both of these but I think there are some big differences as well the amount of pre-training data is very very staggering yes and somehow a a human being after even 15 years with a tiny fraction of that pre-training data they know much less yeah but whatever they do know they know much more deeply somehow and the mistakes like like already at that age you would not make mistakes that our eyes make yeah

[译文] [Ilya]: 我认为这与两者都有一些相似之处，预训练试图扮演这两者的角色。但我认为也存在一些巨大的差异。预训练的数据量是非常非常惊人的。是的，然而一个人即使在 15 年后，仅仅使用了那海量预训练数据的一小部分，他们知道的东西少得多，是啊，但不管他们知道什么，不知何故他们理解得要深刻得多。而且那些错误，就像在这个年纪，你已经不会犯我们的 AI 所犯的那种错误了。是啊。

[原文] [Ilya]: there is another thing you might say could it be something like evolution and the answer is maybe but in this case I think evolution might actually have an edge like there is this I remember reading about this case where some you know that one thing that neuroscientists do or rather one way in which neuroscientists can learn about the brain is by studying people with brain damage to different parts of the brain... there was one case that comes to mind that's relevant i read about this person who had some kind of brain damage that took out I think a stroke or an accident that took out his emotional processing so he stopped feeling any emotion...

[译文] [Ilya]: 还有另一件事你可能会说，这会不会像进化？答案是也许。但在这种情况下，我认为进化实际上可能具有优势。比如有这样一个——我记得读到过这个案例，你知道神经科学家做的一件事，或者说神经科学家了解大脑的一种方式，是研究大脑不同部位受损的人……我想到了一个相关的案例。我读到过一个人，他遭受了某种脑损伤，好像是一次中风或事故，破坏了他的情绪处理能力，所以他不再感受到任何情绪……

章节 5：情绪即算法——价值函数与决策效率

📝 本节摘要：

Ilya 通过神经科学案例提出，人类的情绪实际上是一种高效的“价值函数（Value Function）”。在强化学习中，价值函数能帮助智能体在最终结果出来前就判断当前步骤的好坏（如国际象棋丢子）。他认为，人类进化出的这种“硬编码”的情绪机制，极大地提高了我们的学习效率和决策鲁棒性，而这正是当前 AI 模型所欠缺的。

[原文] [Ilya]: And that's very does what what does it say about the role of our built-in emotions in making us like a viable agent essentially and I guess to connect to your question about pre-training it's like maybe pre- like maybe if you are good enough at like getting everything out of pre-training you can get you could get that as well but that's the kind of thing which seems Well it may or may not be possible to get that from pre-training what is that clearly not just directly emotion it seems like some almost value function like thing which is giving telling you which decision to be like what the end reward for any decision should be

[译文] [Ilya]: 这其实说明了什么？关于我们内置的情绪在让我们成为一个——本质上——可行的智能体（viable agent）方面扮演了什么角色？我想结合你关于预训练的问题来看，也许如果你足够擅长从预训练中挖掘一切，你也能得到那个东西。但这似乎是那种——好吧，是否能从预训练中得到它还不确定。那个东西显然不仅仅是直接的情绪，它似乎几乎像是一种“价值函数（value function）”之类的东西，它在告诉你哪个决定是怎样的，或者任何决定的最终回报应该是什么。

[原文] [Dwarkesh]: and you think that doesn't sort of implicitly come from I think it could I'm just saying it's not one it's not 100% obvious yeah but what what is that like what how do you think about emotions in what is the ML analogy for emotions it should be some kind of a value function thing yeah but I don't think there is a great ML analogy because right now value functions don't play a very prominent role in uh the things people do,

[译文] [Dwarkesh]: 你认为那不会隐式地来自于（预训练）吗？/ [Ilya]: 我认为有可能，我只是说这不是 100% 显而易见的。/ [Dwarkesh]: 是的，但那是什么？你怎么看待情绪？情绪的机器学习（ML）类比是什么？/ [Ilya]: 它应该就是某种价值函数。是的，但我认为目前还没有一个很好的 ML 类比，因为现在价值函数在人们做的事情中并没有扮演非常突出的角色。

[原文] [Ilya]: it might be worth defining for the audience what a value function is if if you want to do that i mean certainly I I'll be very happy to do that right so so when people do reinforcement learning the way reinforcement learning is done right now how does it do how do people train those agents so you have your neural net and you give it a problem and then you tell the model go solve it and the model takes maybe thousands hundreds of thousands of actions or thoughts or something and then it produces a solution the solution is created and then the score is used to provide a training signal for every single action in your trajectory mhm

[译文] [Ilya]: 也许值得为听众定义一下什么是价值函数，如果你想的话。我是说当然，我非常乐意这样做。对，所以当人们做强化学习（RL）时，目前的做法是怎样的？人们如何训练这些智能体？你有一个神经网络，给它一个问题，然后告诉模型去解决它。模型可能会采取数千、数十万个动作或思维步骤之类的，然后它生成一个解决方案。解决方案生成后，（最终的）得分被用来为你轨迹中的每一个动作提供训练信号。嗯哼。

[原文] [Ilya]: so that means that if you are doing something that goes for a long time if you're training a task that takes a long time to solve you will do no learning at all until you solve the until you came up with a proposed solution that's how reinforcement learning is done naively that's how 01 R1 ostensibly are done the value function says something like okay look maybe I could sometimes not always could tell you if you're doing well or badly,

[译文] [Ilya]: 这意味着如果你在做一件持续很长时间的事情，如果你在训练一个需要很长时间才能解决的任务，在你想出一个提议的解决方案之前，你完全不会进行任何学习。这就是目前朴素的强化学习做法，表面上看 o1、R1（注：指OpenAI o1或DeepSeek R1等推理模型）就是这样做的。而价值函数则是说：好吧，听着，也许我有时——并非总是——能告诉你，你做得是好还是坏。

[原文] [Ilya]: the notion of a value function is more useful in some domains than others so for example when you play chess and you lose a piece you know I messed up you don't need to play the whole game to know that what I just did was bad and therefore whatever um whatever preceded it was also bad so the value function lets you short circuit the weight until the very end

[译文] [Ilya]: 价值函数的概念在某些领域比在其他领域更有用。例如，当你下国际象棋时丢了一个子，你知道“我搞砸了”。你不需要下完整盘棋才知道我刚才做的很糟糕，因此在此之前的任何步骤也是糟糕的。所以，价值函数让你能够“短路”（short circuit），不必一直等到最后（才能获得反馈）。

[原文] [Ilya]: what was I alluding to with the person whose emotional center got um damaged is more that maybe what it suggests is that the value function of humans is modulated by emotions in some important way that's hardcoded by evolution and maybe that is important for people to be effective in the world,

[译文] [Ilya]: 我提到那个情绪中枢受损的人，其实更多是在暗示：这也许表明人类的价值函数在某种重要方式上是受到情绪调节的，而这种调节是由进化硬编码的。也许这对人类在世界上有效行事至关重要。

章节 6：时代的转折——从“扩展（Scaling）”回归“研究（Research）”

📝 本节摘要：

在这一章节，Ilya 做出了一个重要的历史分期判断。他认为 2012-2020 是“研究时代”，2020-2025 是“扩展（Scaling）时代”。“Scaling”这个词极其强大，吸干了所有的关注度，大家只要把计算和数据堆上去就行。但随着预训练数据耗尽，以及单纯堆算力面临瓶颈，我们正在重新回到一个新的“研究时代”。这意味着简单的“配方”不再适用，需要寻找新的范式。

[原文] [Dwarkesh]: people have been talking about scaling data scaling parameters scaling compute is there a more general way to think about scaling what are the other scaling axes

[译文] [Dwarkesh]: 人们一直在谈论扩展数据、扩展参数、扩展算力。有没有一种更通用的方式来思考扩展？还有哪些其他的扩展轴？

[原文] [Ilya]: so the thing so so here here is a perspective here's a perspective I think might be might be true so the way ML used to work is that people would just think of it with stuff and try to and try to get interesting results that's what's been going on in the past then the scaling insight arrived right scaling laws GPT3 and suddenly everyone realized we should scale and it's just this this is an example of how language affects thought scaling is what just one word but it's such a powerful word because it informs people what to do they say okay let's let's try to scale things,

[译文] [Ilya]: 那么，这里有一个我认为可能是真的视角。过去机器学习（ML）的运作方式是，人们就是思考各种东西，尝试各种方法，试图得到有趣的结果。这就是过去发生的事情。然后“扩展（Scaling）”的洞见出现了，对吧？扩展定律（Scaling Laws）、GPT-3，突然间每个人都意识到我们应该扩展。这正好是语言如何影响思维的一个例子。“扩展”只是一个词，但它是一个如此强大的词，因为它告诉人们该做什么。他们说，好吧，让我们试着扩展一切。

[原文] [Ilya]: and so you say okay so what are we scaling and pre-training was a thing to scale it was a particular scaling recipe yes the big breakthrough of pre-training is the realization that this recipe is good so you say hey if you mix some compute with some data into a neural net of a certain size you will get results and you will know that it will be better if you just scale the recipe up

[译文] [Ilya]: 所以你会问，好吧，那我们在扩展什么？预训练（Pre-training）就是那个要扩展的东西，它是一个特定的扩展配方。是的，预训练的重大突破在于意识到这个配方是好的。所以你会说，嘿，如果你把一些算力和一些数据混合进一个特定大小的神经网络中，你会得到结果，而且你知道如果你只是按比例放大这个配方，结果会更好。

[原文] [Ilya]: at some point though pre-training will run out of data the data is very clearly finite and so then okay what do you do next either you do some kind of a souped-up pre-training different recipe from the one we've done before or you're doing a RL or maybe something else but now that comput is big computer is now very big in some sense we are back to the age of research

[译文] [Ilya]: 但在某个时刻，预训练的数据会用完，数据显然是非常有限的。那么，好吧，接下来你做什么？要么你做某种加强版的预训练——一种不同于我们之前做过的配方；要么你做强化学习（RL）；或者也许是别的什么。但既然现在的算力很大——算力现在非常大——在某种意义上，我们又回到了“研究时代”。

[原文] [Ilya]: so maybe here's another way to put it up until 2020 from 2015 from 20 2012 to 2020 it was the age of research now from 2020 to 2025 it was the age of scaling or maybe plus minus let's add the arrow bars to those years because people say this is amazing you got to scale more keep scaling the one word scaling but now the scale is so big like is is it is the belief really that oh it's so big but if you had 100x more everything would be so different like it would be different for sure but like is the belief that if you just 100x the scale everything would be transformed i don't think that's true so it's back to the age of research again just with big computers,

[译文] [Ilya]: 也许可以这样说：直到 2020 年——从 2015 年，或者从 2012 年到 2020 年——那是“研究时代”。然后从 2020 年到 2025 年，是“扩展时代”，或者大概这个范围，让我们给这些年份加点误差条。因为人们说这太神奇了，你得扩展更多，继续扩展，“扩展”这一个词。但现在规模已经这么大了，难道信念真的是“哦，它这么大，但如果你再有 100 倍，一切就会变得截然不同”？肯定会有所不同，但那种认为只要将规模扩大 100 倍一切就会发生本质蜕变的信念——我不认为那是真的。所以，我们再次回到了“研究时代”，只是伴随着巨大的计算机。

章节 7：神秘的“更好算法”——为何人类学得比 AI 快

📝 本节摘要：

在这一节中，Ilya 反驳了“人类学习效率高仅仅是因为进化赋予了先验知识”的观点。他指出，进化可以解释人类在视觉和运动方面的天赋，但无法解释人类为何在语言、数学和编程这些现代技能上也能展现出惊人的样本效率（sample efficiency）。这暗示人类大脑中运行着一种根本上优于当前 AI 的机器学习算法。

[原文] [Ilya]: the thing which I think is the most fundamental is that these models somehow just generalize dramatically worse than people yes and it's super obvious that's that seems like a very fundamental thing

[译文] [Ilya]: 我认为最根本的事情是，这些模型不知何故在泛化能力上比人类差得惊人。是的，这超级明显，这似乎是一件非常根本的事情。

[原文] [Dwarkesh]: there's one which is about sample efficiency which is why should it take so much more data for these models to learn than humans... you know you could actually wonder one one possible explanation for the human sample efficiency that needs to be considered is evolution and evolution has given us a small amount of the mo the most useful information possible and for things like vision hearing and locomotion I think there's a pretty strong case that evolution actually has given us a lot,

[译文] [Dwarkesh]: 有一个是关于样本效率（sample efficiency）的，就是为什么这些模型学习需要比人类多得多的数据……你知道，你实际上可能会想，对于人类样本效率的一个需要考虑的可能解释是进化。进化给了我们少量但尽可能最有用的信息。对于像视觉、听觉和运动这些东西，我认为有很强的理由相信进化实际上给了我们很多。

[原文] [Ilya]: but in language and math and coding probably not it still seems better than models i mean obviously models are better than the average human at language and math and coding but are they better at the average human at learning oh yeah oh yeah absolutely what I meant to say is that language math and coding and especially math and coding suggests that whatever it is that makes people good at learning is probably not so much a complicated prior but something more some fundamental thing,

[译文] [Ilya]: 但是在语言、数学和编程方面，可能就不是（进化给的）了。但它（人类的学习能力）似乎仍然比模型好。我是说，显然模型在语言、数学和编程的表现上比普通人好，但它们在“学习”这方面比普通人好吗？（Dwarkesh: 哦是的。）哦是的，绝对的（指人类更强）。我想说的是，语言、数学和编程——尤其是数学和编程——表明，无论是什么让人类擅长学习，可能与其说是一个复杂的先验知识（prior），不如说是某种更——某种更根本的东西。

[原文] [Ilya]: consider a skill that people exhibit some kind of great reliability or you know um if the skill is one that was very useful to our ancestors for many millions of years hundreds of millions of years you could say you could argue that maybe humans are good at it because of evolution because we have a prior an evolutionary prior that's encoded in some very nonobvious way that somehow makes us so good at it

[译文] [Ilya]: 考虑一项人类表现出极高可靠性的技能。如果是那种对我们的祖先在数百万、数亿年里非常有用的技能，你可以说——你可以争辩说——也许人类擅长它是由于进化，因为我们有一个先验，一个以某种非常不明显的方式编码的进化先验，不知何故使我们如此擅长它。

[原文] [Ilya]: but if people exhibit great ability reliability robustness ability to learn in a domain that really did not exist until recently then this is more an indication that people might have just better machine learning period,

[译文] [Ilya]: 但是，如果人们在一个直到最近才存在的领域中表现出了巨大的能力、可靠性、鲁棒性和学习能力，那么这更多地表明：人类可能只是拥有更好的机器学习（算法），就这样（period）。

[原文] [Ilya]: you know that is a great question to ask and it's a question I have a lot of opinions about but unfortunately we live in a world where not not all machine learning ideas are discussed freely and this is this is one of them so there's probably a way to do it i think it can be done the fact that people are like that I think it's a proof that it can be done

[译文] [Ilya]: 你知道这是一个非常棒的问题，关于这个问题我有很多看法，但不幸的是，我们生活在一个并非所有机器学习想法都可以自由讨论的世界里，而这就是其中之一。所以，很可能有一种方法可以做到这一点。我认为这是可以做到的。人类是那样的这一事实，我认为就是它可以被做到的证明。

章节 8：算力账本——为什么 SSI 不需要谷歌级别的资源

📝 本节摘要：

面对外界对 SSI 算力储备的质疑，Ilya 进行了一次“算力审计”。他指出，大厂虽然融资巨大，但绝大部分算力被用于产品推理（Inference）和维持庞大的工程/销售团队。相比之下，SSI 的 30 亿美元资金专注于纯粹的研究（Research）。他认为，验证新范式并不需要绝对最大规模的算力，就像 Transformer 和 AlexNet 最初都是在有限资源下诞生的一样。

[原文] [Dwarkesh]: and if you at SSI have 50 different ideas how will you know which one is the next transformer and which one is you know brittle without having the kinds of compute that other frontier labs have

[译文] [Dwarkesh]: 如果你们 SSI 有 50 个不同的想法，在没有其他前沿实验室那种算力的情况下，你怎么知道哪一个是下一个 Transformer，哪一个又是脆弱不堪的呢？

[原文] [Ilya]: so I can I can comment on that which is the short comment is that you know you mentioned SSI specifically for us the amount of compute that SSI has for research is really not that small and I want to explain why like a simple math can explain why the amount of compute that we have is actually a lot more comparable for research than one might think

[译文] [Ilya]: 我可以对此发表评论，简短的评论是，你知道你特别提到了 SSI，对于我们来说，SSI 拥有的用于研究的算力实际上并不小。我想解释一下为什么，就像一个简单的数学计算可以解释，为什么我们拥有的用于研究的算力实际上比人们想象的要更有可比性。

[原文] [Ilya]: I'll explain okay so SSI has raised $3 billion which is like not small by it's like a lot by any absolute sense but you could say but look at the other companies raising much more but a lot of what they're a lot of their compute goes for inference like these big numbers these big loans it's earmarked for inference that's number one

[译文] [Ilya]: 我来解释一下。SSI 筹集了 30 亿美元，这无论从绝对意义上来说都不算小，算是一大笔钱。但你可以说，看看其他公司筹集的资金要多得多。但他们的大部分——他们的大部分算力都用于推理（inference）。比如那些大数字、那些大额贷款，那是指定用于推理的。这是第一点。

[原文] [Ilya]: number two you need if you want to have a product on which you do inference you need to have a big staff of engineers of salespeople a lot of the research needs to be dedicated for producing all kinds of product related features so then when you look at what's actually left for research the difference becomes a lot smaller

[译文] [Ilya]: 第二点，如果你想拥有一个进行推理的产品，你需要庞大的工程师团队、销售人员团队，大量的研究工作需要致力于生产各种与产品相关的功能。所以，当你看看实际上剩下用于（核心）研究的资源时，差距就变得小得多了。

[原文] [Ilya]: now the other thing is is that if you are doing something different do you really need the absolute maximum scale to prove it i don't think it's true at all i think that in our case we have sufficient compute to prove to convince ourselves and anyone else that what we're doing is correct

[译文] [Ilya]: 还有一件事是，如果你在做一些不同的事情，你真的需要绝对最大规模来证明它吗？我完全不认为如此。我认为在我们的案例中，我们有足够的算力来证明——来说服我们自己和其他任何人——我们正在做的事情是正确的。

章节 9：直通车与安全——为何不发布“中间产品”

📝 本节摘要：

Ilya 解释了 SSI 为何选择“直通车（Straight Shot）”策略（即不发布中间产品，直接研发超级智能），但也承认这是一个权衡。支持方观点是避免卷入商业“老鼠赛跑（rat race）”带来的短期利益妥协；反方观点是，分阶段发布（Incremental Deployment）有助于社会适应和发现安全漏洞。他最终承认，即使是直通车策略，在最后阶段也会包含某种形式的渐进式发布。

[原文] [Dwarkesh]: so why is your default plan to straightshot super intelligence because it sounds like you know openai anthropic all these other companies they're explicit thinking is look we have weaker and weaker intelligences that the public can get used to and prepare for and why is it potentially better to build a super intelligence directly

[译文] [Dwarkesh]: 那么为什么你的默认计划是直通（straightshot）超级智能？因为听起来像 OpenAI、Anthropic 所有这些其他公司，他们明确的想法是，看，我们有越来越弱的（注：指相对最终目标较弱，但在逐渐变强）智能，公众可以适应并为此做好准备。为什么直接构建超级智能可能更好？

[原文] [Ilya]: so I'll make the case for and against the case for is that you are so one of the challenges that people face when they're in the market is that they have to participate in the rat race and the rat race is quite difficult in that it exposes you to to to difficult trade-offs which you need to make

[译文] [Ilya]: 我会分别阐述支持和反对的理由。支持的理由是，当人们身处市场中时，面临的挑战之一是必须参与“老鼠赛跑（rat race，指激烈的商业竞争）”。这场竞争非常艰难，因为它会让你面临必须做出的艰难权衡。

[原文] [Ilya]: and there is it is it is nice to say we'll insulate ourselves from all this and just focus on the research and come out only when we are ready and not before but the counterpoint is valid too and those those are opposing forces the counterpoint is hey it is useful for the world to see powerful AI it is useful for the world to see powerful AI because that's the only way you can communicate it

[译文] [Ilya]: 能够说“我们将把自己与这一切隔绝开来，只专注于研究，只有当我们准备好了才出来，而不是在此之前”，这很好。但反方观点也是有效的，这两者是相互对立的力量。反方观点是：嘿，让世界看到强大的 AI 是有用的。让世界看到强大的 AI 是有用的，因为那是你能够传达它的唯一方式。

[原文] [Ilya]: I can't think of another discipline in human engineering and research where the end artifact was made safer mostly through just thinking about how to make it safe as opposed to why are airplane crashes per mile so much lower today than they were decades ago... it's mostly because these systems were deployed to the world you noticed failures those failures were corrected and the systems became more robust

[译文] [Ilya]: 我想不出人类工程和研究中有哪个学科，其最终成品的安全性主要是通过单纯思考如何让它变安全而实现的。这就像为什么今天的每英里飞机坠毁率比几十年前低得多……这主要是因为这些系统被部署到了世界上，你注意到了故障，这些故障被修正了，系统变得更加鲁棒。

[原文] [Ilya]: well I think I think on this point even in the straight shot scenario you would still do a gradual release of it is how I would imagine it the the gra gradualism would be an inherent inherent component of any plan it's just a question of what is the first thing that you get out of the door

[译文] [Ilya]: 嗯，我认为在这一点上，即使在直通车方案中，你仍然会进行渐进式发布，这是我的设想。“渐进主义（gradualism）”将是任何计划中固有的、不可或缺的组成部分。问题仅仅在于，你推出的第一件东西是什么。

章节 10：修正 AGI 定义——“超级智能 15 岁少年”

📝 本节摘要：

Ilya 提出了一个颠覆性的观点：传统的 AGI 定义（像预训练模型那样“知道”所有事情）是错误的。人类并不是因为生来就知道怎么做所有工作而成为通用智能的，而是因为具备持续学习（Continual Learning）的能力。他将理想的超级智能比作一个“超级聪明的 15 岁少年”——他可能什么都不懂，但去医学院学一学就能成名医，去学编程就能成大神。这才是真正的超级智能。

[原文] [Ilya]: I maintain F first word AGI second word pre-training let me explain so the word the term AGI why does this term exist it's a very particular term why does it exist there's a reason the reason that the term AGI exists is in my opinion not so much because it's like a very important essential descriptor of in of of some end state of intelligence But because it is a reaction to a different term that existed and the term is narrow AI

[译文] [Ilya]: 我坚持两个词：第一个是 AGI，第二个是预训练（Pre-training）。让我解释一下。AGI 这个词，为什么这个术语会存在？这是一个非常特定的术语。它存在是有原因的。在我看来，AGI 这个术语之所以存在，与其说是因为它是某种智能终极状态的重要本质描述，不如说它是对另一个已存在术语的反应，那个术语就是“狭义 AI（Narrow AI）”。

[原文] [Ilya]: general AI pre-training gives AGI but the thing that happened with AGI and pre-training is that in some sense they overshot the target because by the kind if you think about the term AGI you will realize and especially in the context of pre-training you will realize that a human being is not an AGI because a human being yes there is definitely a foundation of skills a human being a human being lacks a huge amount of knowledge instead we rely on continual learning

[译文] [Ilya]: 通用 AI、预训练带来了 AGI。但在某种意义上，AGI 和预训练发生的事情是它们“用力过猛（overshot the target）”了。因为如果你思考 AGI 这个术语，特别是在预训练的背景下，你会意识到人类并不是 AGI。因为人类——是的，肯定有技能基础——但人类缺乏大量的知识。相反，我们依赖于持续学习（continual learning）。

[原文] [Ilya]: where on the curve of continual learning is it going to be I produce like um a super intelligent 15year-old that's very eager to go and you say okay I'm going to they don't know very much at all the great student very eager you go and be a programmer you go and be a doctor go and learn so you could imagine that the deployment itself will involve some kind of a learning trial and error period it's a process as opposed to you drop the finished thing

[译文] [Ilya]: 它（AI）将处于持续学习曲线的哪个位置？如果我制造了一个像超级聪明的 15 岁少年那样的东西，非常渴望去尝试，你会说，好吧，他们根本懂的不多，是个很棒的学生，非常渴望。你去当程序员，你去当医生，去学习。所以你可以想象，部署本身将涉及某种学习、试错的时期。这是一个过程，而不是你直接扔出一个成品。

[原文] [Dwarkesh]: you're proposing instead a mind which can learn to do any single every single job yes and that is super intelligence

[译文] [Dwarkesh]: 你提出的反而是：一个能够学会做任何一项、每一项工作的心智。/ [Ilya]: 是的，那才是超级智能。

章节 11：偏执是好事？当 AI 真正展现力量时

📝 本节摘要：

Ilya 预言，目前 AI 安全领域的松懈是因为 AI 还没让人感到真正的“力量（Power）”。当前的错误让人觉得它笨，但未来 AI 会强大到让人恐惧。他预测，一旦这种力量变得显而易见，所有 AI 公司都会发生心态转变，变得极度“偏执（paranoid）”和谨慎。这是实现安全的一个重要（虽然被动）的心理机制。

[原文] [Ilya]: indeed the whole problem what is the problem of AI and AGI the whole problem is the power the whole problem is the power when the power is really big what's going to happen

[译文] [Ilya]: 确实，AI 和 AGI 的整个问题是什么？整个问题就是力量（power）。整个问题就在于力量。当力量真的很大时，会发生什么？

[原文] [Ilya]: i do maintain here is something which I predict will happen that's a prediction i maintain that as AI becomes more powerful then people will change their behaviors and we will see all kinds of unprecedented things which are not happening right now

[译文] [Ilya]: 我坚持这一点——这是我预测会发生的事情，这是一个预测——我坚持认为，随着 AI 变得越来越强大，人们会改变他们的行为，我们将看到各种目前尚未发生的、前所未有的事情。

[原文] [Ilya]: i do think that at some point the AI will start to feel powerful actually and I think when that happens we will see a big change in the way all AI companies approach safety they'll become much more paranoid i think I I say this as a predict as a as a as a prediction that we will see happen we'll see if I'm right but I think this is something that will happen because they will see the AI becoming more powerful

[译文] [Ilya]: 我确实认为，在某个时刻，AI 将开始真正让人感觉到强大。我认为当那一刻发生时，我们将看到所有 AI 公司对待安全的方式发生巨大变化。他们会变得更加偏执（paranoid）。我是作为一个预测来说这番话的——我们将看到它发生。我们要看看我是否正确，但我认为这将会发生，因为他们会亲眼看到 AI 变得越来越强大。

章节 12：终极平衡：关爱感知生命与 Neuralink++

📝 本节摘要：

在探讨长期的安全与共存时，Ilya 提出了两个概念：

1. 短期/中期对齐：让 AI 具有“关爱感知生命（Care for sentient life）”的价值观。这比单纯“关爱人类”更稳健，因为未来大部分感知生命可能是 AI 自己。

2. 长期平衡（Long-term equilibrium）：面对不想听的答案，Ilya 坦承，为了让人类在与超级智能的共存中不沦为旁观者（宠物），唯一的长远解法可能是人类通过 Neuralink++ 等技术与 AI 融合，成为超级智能的一部分。

[原文] [Ilya]: it's like the AI that's robustly aligned to care about sentient life specifically i think in particular it will be there's a case to be made that it will be easier to build an AI that cares about sentient life than an AI that cares about human life alone because the AI itself will be sentient

[译文] [Ilya]: 这就像是 AI 被稳健地对齐到去关心感知生命（sentient life）。特别地，我认为有一个理由是，构建一个关心感知生命的 AI 可能比构建一个只关心人类生命的 AI 更容易，因为 AI 本身也将是有感知的。

[原文] [Ilya]: even if you got an AI to care about sentient beings and it's not actually clear to me that that's what you should try to do if you solved alignment it would still be the case that most sentient beings will be AIS there will be trillions eventually quadrillions of AI humans will be a very small fraction of sentient beings so it's not clear to me if the goal is some kind of human control over this future civilization that this is the best criterion

[译文] [Ilya]: 即使你让 AI 关心感知生物——我其实并不确定这就是你该尝试去做的——如果你解决了对齐问题，情况仍然会是：绝大多数感知生物将是 AI。最终会有数万亿、数千万亿的 AI。人类将只是感知生物中非常小的一部分。所以，如果目标是人类对这个未来文明拥有某种控制权，我不确定这是否是最佳标准。

[原文] [Ilya]: but so I'm going to preface by saying I don't like this solution but it is a solution And the solution is if people become part AI with some kind of neural link++ because what will happen as a result is that now the AI understands something and we understand it too like because now the understanding is transmitted wholesale so now if the AI is in some situation now it's like you are involved in the situation yourself fully and I think this is the answer to the equilibrium

[译文] [Ilya]: 但——我要先声明我不喜欢这个解决方案，但这确实是一个解决方案。这个解决方案是：如果人类通过某种 Neuralink++ 成为 AI 的一部分。因为这样做的结果是，现在 AI 理解了某些东西，我们也理解了它。因为现在的理解是全盘传输的。所以如果 AI 处于某种情况中，现在就像是你自己完全参与到了这种情况中。我认为这是（人机）平衡的答案。

章节 13：研究的品味：美、简洁与对丑陋的零容忍

📝 本节摘要：

访谈最后，Dwarkesh 询问 Ilya 为什么他总能做出正确的重大判断（如 AlexNet, GPT-3）。Ilya 揭示了他的核心心法：审美（Aesthetic）。他寻找的是“美、简洁、优雅”，并坚信“丑陋”的东西绝无容身之地。这种自上而下（Top-down）的信念，结合对生物大脑机制的正确直觉，支撑他在实验数据尚未成功时坚持下去。

[原文] [Dwarkesh]: final question what is research taste you're obviously the person in the world who is considered to have the best taste in doing research in AI... what is it that how do you characterize how you come up with these ideas

[译文] [Dwarkesh]: 最后一个问题，什么是研究品味（Research Taste）？你显然是世界上被认为在 AI 研究方面拥有最佳品味的人……你是如何描述你想出这些主意的过程的？

[原文] [Ilya]: one thing that um guides me personally is an aesthetic of how AI should be by thinking about how people are but thinking correctly like it's very easy to think about how people are incorrectly but what does it mean to think about people correctly...

[译文] [Ilya]: 指引我个人的一件事是关于 AI 应该是什么样子的审美（aesthetic），这是通过思考人类是怎样的，但是是正确地思考。这就像，错误地思考人类是怎样的很容易，但正确地思考人类意味着什么……

[原文] [Ilya]: and looking for almost beauty beauty simplicity ugliness there's no room for ugliness it's just beauty simplicity elegance correct inspiration from the brain and all of those things need to be present at the same time and the more they are present the more confident you can be in a top- down belief

[译文] [Ilya]: 并且几乎是在寻找美。美、简洁。丑陋（ugliness）——这里没有丑陋的容身之地。只有美、简洁、优雅、来自大脑的正确灵感。所有这些东西需要同时存在。当它们存在得越多，你就越能对一种自上而下的信念（top-down belief）充满信心。

[原文] [Ilya]: and then the top down belief is the thing that sustains you when the experiments contradict you because if you just trust the data all the time well sometimes you can be doing a correct thing but there's a bug but you don't know that there is a bug how can you tell that there is a bug how do you know if you should keep debugging or you conclude it's the wrong direction well it's the top down

[译文] [Ilya]: 然后，这种自上而下的信念是当实验结果与你相悖时支撑你的东西。因为如果你总是只相信数据，好吧，有时你可能在做正确的事情，但有一个 Bug，而你不知道有一个 Bug。你怎么能分辨出有一个 Bug？你怎么知道你是应该继续调试，还是应该得出结论说方向错了？嗯，就是靠这种自上而下的信念。

章节 14：隐秘的知识——被“禁忌”的机器学习思想

📝 本节摘要：

当 Dwarkesh 追问人类为何能进行高效的无监督学习（如青少年学开车）时，Ilya 再次提到了“价值函数”的作用。但随后他抛出了一个极具神秘感的观点：他认为存在一种特定的机器学习原理能解释这一切，但在当前的竞争环境下，某些 ML 思想无法被自由讨论。这暗示了 SSI 可能掌握着某种尚未公开的核心技术直觉。

[原文] [Ilya]: so so so um this is where you know one of the things that you've been asking about is how can you know the teenage driver kind of self-correct and learn from their experience without an external teacher and the answer is well they have their value function right they have a general sense which is also by the way extremely robust in people like whatever it is the human value function whatever the human value function is with a few exceptions around addiction it's actually very very robust

[译文] [Ilya]: 那么，这就是——你知道你一直问的事情之一是，那个青少年司机通过什么方式自我修正并从经验中学习，而不需要外部老师？答案是：嗯，他们有他们的价值函数（value function），对吧？他们有一种普遍的感觉，顺便说一句，这在人类身上是非常鲁棒的。不管人类的价值函数是什么——除了在成瘾等少数例外情况下——它实际上是非常非常鲁棒的。

[原文] [Ilya]: and so for something like a teenager that's learning to drive they start to drive and they already have a sense of how they're driving immediately how badly they're unconfident and then they see okay and they and then of course the the learning speed of any teenager is so fast after 10 hours you're good to go

[译文] [Ilya]: 所以对于像学开车的青少年来说，他们开始驾驶，他们立刻就能感觉到自己开得怎么样，开得有多糟糕，他们有多不自信。然后他们观察，好的……当然，任何青少年的学习速度都是如此之快，10 个小时后，你就可以上路了。

[原文] [Dwarkesh]: yeah it seems like humans have some solution but I'm curious about like well how are they doing it and like why is it so hard to like how do we need to reconceptualize the way we're training models to make something like this possible

[译文] [Dwarkesh]: 是的，人类似乎有某种解决方案，但我很好奇他们是怎么做到的？以及为什么这（对模型来说）这么难？我们需要如何重新概念化我们训练模型的方式，以使这种事情成为可能？

[原文] [Ilya]: you know that is a great question to ask and it's a question I have a lot of opinions about but unfortunately we live in a world where not not all machine learning ideas are discussed freely and this is this is one of them so there's probably a way to do it i think it can be done the fact that people are like that I think it's a proof that it can be done

[译文] [Ilya]: 你知道这是一个非常棒的问题，关于这个问题我有很多看法。但不幸的是，我们生活在一个并非所有机器学习想法都可以自由讨论的世界里，而这就是其中之一。所以很可能有一种方法可以做到这一点，我认为这是可以做到的。人类是那样的这一事实，我认为就是它可以被做到的证明。

章节 15：想法的通货膨胀——重估“研究”的价值

📝 本节摘要：

Dwarkesh 询问 2012-2020 年间的“研究氛围”是怎样的。Ilya 指出，“扩展（Scaling）”吸干了房间里所有的空气，导致大家停止思考新想法，因为“想法廉价，执行至上”的硅谷教条盛行。但他反驳说，现在实际上是想法稀缺的时代。他回顾历史，指出 AlexNet 只用了 2 张 GPU，Transformer 只用了 8 到 64 张 GPU。这证明了验证突破性想法并不一定需要天文数字般的算力，从而为 SSI 的研究策略辩护。

[原文] [Ilya]: so one consequence of um the age of scaling is that there was this um scaling sucked out all the air in the room yeah and so because scaling sucked out all the air in the room everyone started to do the same thing we got to the point where uh we are in a world where there are more companies than ideas by quite a bit actually

[译文] [Ilya]: “扩展时代”的一个后果是，扩展吸干了房间里所有的空气。是的，因为扩展吸干了所有的空气，每个人都开始做同样的事情。实际上我们已经到了这样一个地步：世界上的公司比想法要多得多。

[原文] [Ilya]: on that you know there is this Silicon Valley saying that says that ideas are cheap execution is everything and people say that a lot yeah and there is truth to that but then I saw I saw someone say on Twitter um something like if ideas are are so cheap how come no one's having any ideas and I think it's true too i think like if you think about um research progress in terms of bottlenecks there are several bottlenecks

[译文] [Ilya]: 关于这一点，你知道硅谷有句俗语说“想法是廉价的，执行就是一切”，人们经常这么说。是的，这话有道理。但我看到有人在 Twitter 上说——大概是——“如果想法这么廉价，怎么没见谁有什么想法呢？”我认为这也是真的。如果你从瓶颈的角度思考研究进展，存在几个瓶颈。

[原文] [Ilya]: so compute is large enough such that it's like not obvious that you need that much more compute to prove some idea like I'll give you an analogy alexet was built on two GPUs that was the total amount of comput use for it the transformer was built on 8 to 64 GPUs no single transformer paper experiment used more than 64 GPUs of 2017 which would be like what two GPUs of today

[译文] [Ilya]: 现在的算力已经足够大了，所以并不明显你需要多得多的算力来证明某个想法。我给你一个类比：AlexNet 是建立在 2 张 GPU 上的，那是它使用的总算力。Transformer 是建立在 8 到 64 张 GPU 上的。Transformer 论文中没有一个实验使用的算力超过了 2017 年的 64 张 GPU——这大概相当于今天的 2 张 GPU 吧？

[原文] [Ilya]: so there definitely for for research you need like definitely some amount of compute but it's far from obvious that you need the absolutely largest amount of compute ever for research

[译文] [Ilya]: 所以，对于研究来说，你肯定需要一定量的算力，但远非显而易见的是，做研究需要史上绝对最大规模的算力。

章节 16：智能的生态位——竞争偏爱专业化

📝 本节摘要：

在展望未来 AI 产业格局时，Ilya 提出了一个反直觉的观点：即使未来有能够通过持续学习掌握所有技能的 AI，市场竞争仍会导致专业化（Specialization）而非大一统。因为“积累的学习”本身就是护城河——如果一家公司已经在生物学 AI 上投入了巨量算力和经验，另一家公司即使拥有通用模型，也不愿从头开始重复这一过程。这就是“生态位（Niches）”形成的原因。

[原文] [Dwarkesh]: by default you would expect the company that has the model company that has that model to be getting all these gains because they have the model that is learning how to do all has the skills and knowledge that it's building up in the world what is the reason to think that the benefits of that would be widely distributed and not just end up at whatever model company gets this continuous learning loop going first

[译文] [Dwarkesh]: 默认情况下，你会预期拥有那个模型的公司——拥有那个正在学习如何做所有事情、正在世界上建立技能和知识的模型的公司——会获得所有这些收益。有什么理由认为这些利益会被广泛分配，而不是仅仅落入无论哪家最先启动这个持续学习循环的模型公司手中？

[原文] [Ilya]: i think what's going to happen is that the way competition like competition loves specialization and you see it in the market you see it in evolution as well so you're going to have lots of different niches and you're going to have lots of different companies who are occupying different niches

[译文] [Ilya]: 我认为将会发生的是——竞争的方式——竞争偏爱专业化（specialization）。你在市场中能看到这一点，在进化中也能看到。所以你将会有许多不同的利基市场（niches），你将会有许多占据不同利基市场的不同公司。

[原文] [Ilya]: in this kind of world where you might say yeah like one AI company is really quite a bit better at some area of really complicated economic activity and a different company is better at another area and the third company is really good at litigation

[译文] [Ilya]: 在这种世界里，你可能会说，是的，比如一家 AI 公司在某些非常复杂的经济活动领域要好得多，而另一家公司在另一个领域更好，第三家公司非常擅长诉讼。

[原文] [Ilya]: but you have accumulated learning you have a investment you spent a lot of compute to become really really really good really phenomenal at this thing and someone else spent a huge amount of comput and a huge amount of experience to get really really good at some other thing right you apply a lot of human learning to get there but now like you you are at this high point where someone else would say look like I don't want to start learning what you've learned to do

[译文] [Ilya]: 因为你有积累的学习，你有投资。你花费了大量算力才变得非常非常非常擅长——在这个事情上真正非凡。而其他人花费了大量算力和大量经验在其他事情上变得非常擅长。对吧，你应用了大量人类学习才达到那个高度，但现在你处于这个高点，其他人会说：“看，我不想从头开始学习你已经学会做的事情。”

章节 17：自我博弈的陷阱与多样性的起源

📝 本节摘要：

既然大模型都基于相似的数据预训练，它们为何会有差异？Ilya 指出，预训练让大家变得一样，而强化学习（RL）和后训练（Post-training）是差异化开始的地方。对于“自我博弈（Self-play）”，他持保留态度，认为传统的自我博弈只能培养狭隘的技能（如谈判）。但他看好一种新形式的对抗：辩论（Debate）与“AI 法官”，这种为了取胜而试图变得“与众不同”的动力，将是 AI 产生真正多样性的源泉。

[原文] [Dwarkesh]: human teams have more diversity than teams of AIs might have but how do we elicit meaningful diversity among AI so I think just raising the temperature just results in gibberish i think you want something more like different scientists have different different prejudices or different ideas how do you get that kind of diversity among AI agents

[译文] [Dwarkesh]: 人类团队比 AI 团队可能拥有更多的多样性。但我们如何引发出 AI 之间有意义的多样性？我觉得仅仅提高温度（Temperature）只会导致胡言乱语。你想要的是更像——不同的科学家有不同的偏见或不同的想法。你如何在 AI 智能体之间获得那种多样性？

[原文] [Ilya]: so the reason there has been no diversity I believe is because of pre-training all the pre-trained models are the same pretty much because the pre-train on the same data now RL and post training is where some differentiation starts to emerge because different people come up with different RL training

[译文] [Ilya]: 我相信没有多样性的原因是因为预训练。所有的预训练模型几乎都是一样的，因为它们在相同的数据上进行预训练。现在，强化学习（RL）和后训练（post-training）是开始出现分化的地方，因为不同的人提出了不同的 RL 训练方法。

[原文] [Ilya]: the thing is that selfplay at least the way it was done in the past when you have agents which are somehow compete with each other it's only good for developing a certain set of skills it is too narrow it's only good for like negotiation uh conflict certain social skills strategizing that kind of stuff

[译文] [Ilya]: 问题是，自我博弈（self-play）——至少在过去的做法中，当你有智能体以某种方式相互竞争时——它只对开发特定的一套技能有好处。它太狭窄了。它只对诸如谈判、冲突、某些社交技能、制定策略这类东西有好处。

[原文] [Ilya]: now actually I think that selfplay did find a home but just in a different form in a different form so things like debate prove a verifier you have some kind of an LLM as a judge which is also incentivized to find mistakes in your work you could say this is not exactly selfplay but this is you know a related adversarial setup

[译文] [Ilya]: 现在，实际上我认为自我博弈确实找到了归宿，只是以一种不同的形式。所以像辩论（debate）、证明-验证者（prover-verifier），你用某种大语言模型（LLM）作为法官，它也被激励去发现你工作中的错误。你可以说这不完全是自我博弈，但这是一种相关的对抗性设置。

[原文] [Ilya]: really selfplay is an example of um is a special case of more general like um competition between between agents right the response the natural response to competition is to try to be different

[译文] [Ilya]: 实际上，自我博弈是智能体之间更广泛竞争的一个特例，对吧？对竞争的反应——自然的反应——就是试图变得与众不同。

章节 18：审美的正确性——从神经元到局部学习规则

📝 本节摘要：

Dwarkesh 询问 Ilya 为何能连续在 AlexNet、GPT-3 等重大突破中保持敏锐的直觉。Ilya 揭示了他的核心方法论：审美（Aesthetic）。这种审美并非单纯的艺术感，而是基于对人类大脑运作机制的“正确思考”。他举例说明，“人工神经元”和“分布式表示”之所以是好想法，是因为它们符合大脑的物理限制（如局部连接），这种对生物机制的正确抽象构成了简洁而优雅的理论基础。

[原文] [Dwarkesh]: final question what is research taste you're obviously the person in the world who is considered to have the best taste in doing research in AI you were uh the co-author on many of the biggest the biggest things that have happened in the history of deep learning from Alex net to GPT3 to so on what is it that how do you characterize how you come up with these ideas

[译文] [Dwarkesh]: 最后一个问题，什么是研究品味（research taste）？你显然是世界上被认为在 AI 研究方面拥有最佳品味的人。你是深度学习历史上许多最重大事件的合著者，从 AlexNet 到 GPT-3 等等。你是如何描述你想出这些主意的过程的？

[原文] [Ilya]: I can answer so I can comment on this for myself I think different people do it differently but one thing that um guides me personally is an aesthetic of how AI should be by thinking about how people are but thinking correctly like it's very easy to think about how people are incorrectly but what does it mean to think about people correctly

[译文] [Ilya]: 我可以回答，我可以就我自己评论这一点。我认为不同的人做法不同，但指引我个人的一件事是关于 AI 应该是什么样子的审美（aesthetic），这是通过思考人类是怎样的，但是是正确地思考。这就像，错误地思考人类是怎样的很容易，但正确地思考人类意味着什么……,

[原文] [Ilya]: so I'll give you some examples the idea of the artificial neuron is directly inspired by the brain and it's a great idea why because you say sure the brain has all these different organs has the faults but the faults probably don't matter M why do we think that the neurons matter because there's many of them it kind of feels right

[译文] [Ilya]: 我给你举几个例子。人工神经元（artificial neuron）的想法是直接受大脑启发的，这是一个很棒的想法。为什么？因为你会说，当然大脑有所有这些不同的器官，有各种缺陷，但这些缺陷可能并不重要。为什么我们认为神经元很重要？因为它们数量庞大，这感觉是对的。

[原文] [Ilya]: so you want the neuron you want some kind of local learning rule that will change the connections you want some local learning rule rule that will change the connections between the neurons right it feels plausible that the brain does it the idea of the distributed representation the idea that the brain you know the brain responds to experience or neural network should learn from experience not response the brain learns from experience the neural natural level of experience and you kind of ask yourself is some is something fundamental or not fundamental how things should be and I think that's been guiding me a fair bit kind of thinking from multiple angles

[译文] [Ilya]: 所以你需要神经元，你需要某种局部学习规则（local learning rule）来改变连接——你需要某种局部学习规则来改变神经元之间的连接，对吧？大脑这样做是感觉合理的。还有分布式表示（distributed representation）的想法，以及大脑对经验做出反应，或者说神经网络应该从经验中学习——大脑从经验中学习，神经网络（也应该）在自然的经验层面上学习。你会问自己，某种东西是不是根本性的？事物应该是怎样的？我认为这在很大程度上一直指引着我，从多个角度进行思考。,

章节 19：自上而下的信念——在丑陋与Bug中寻找真理

📝 本节摘要：

在访谈的尾声，Ilya 阐述了“美、简洁、优雅”在科学研究中的实用价值。他提出了一个关键概念：自上而下的信念（Top-down belief）。在科研过程中，实验数据往往会与理论相悖（例如因为代码 Bug）。如果你只相信数据，就会放弃正确的方向。只有拥有基于“美”的坚定信念，你才能断定“这必然是正确的，肯定有个 Bug”，从而坚持下去直到成功。

[原文] [Ilya]: and looking for almost beauty beauty simplicity ugliness there's no room for ugliness it's just beauty simplicity elegance correct inspiration from the brain and all of those things need to be present at the same time and the more they are present the more confident you can be in a top- down belief

[译文] [Ilya]: 并且几乎是在寻找美。美、简洁。丑陋（ugliness）——这里没有丑陋的容身之地。只有美、简洁、优雅、来自大脑的正确灵感。所有这些东西需要同时存在。当它们存在得越多，你就越能对一种自上而下的信念（top-down belief）充满信心。

[原文] [Ilya]: and then the top down belief is the thing that sustains you when the experiments contradict you because if you just trust the data all the time well sometimes you can be doing a correct thing but there's a bug but you don't know that there is a bug how can you tell that there is a bug how do you know if you should keep debugging or you conclude it's the wrong direction well it's the top down

[译文] [Ilya]: 然后，这种自上而下的信念是当实验结果与你相悖时支撑你的东西。因为如果你总是只相信数据，好吧，有时你可能在做正确的事情，但有一个 Bug，而你不知道有一个 Bug。你怎么能分辨出有一个 Bug？你怎么知道你是应该继续调试，还是应该得出结论说方向错了？嗯，就是靠这种自上而下的信念。

[原文] [Ilya]: well how should you can say the things have to be this way something like this has to work therefore we got to keep going that's the top down and it's based on this like multifaceted beauty and inspiration by the brain

[译文] [Ilya]: 你可以说“事情必须是这样的”，“像这样的东西必须行得通”，因此我们必须继续前进。这就是自上而下的信念，它是基于这种多层面的美感和来自大脑的灵感。,

[原文] [Dwarkesh]: all right we'll leave it there thank you so much thank you so much all right appreciate it that was great yeah I enjoyed it yes me too

[译文] [Dwarkesh]: 好的，我们就到这里。非常感谢，非常感谢。好的，很感激，这太棒了。是啊，我很享受这次谈话。/ [Ilya]: 是的，我也是。