Hi, I work in AI and have been following this stuff very closely for about a decade, so I'll try to explain it as clearly as I can. In 2020, transformers had already been around for a few years. The paper linked in the top comment from Google explains them in incredible technical detail, but basically transformers gave AI a much better way to look back at different parts of text and figure out what mattered to what it was currently writing. It was, and still fundamentally is, a "next token predictor," but suddenly there was a lot more machinery going into predicting that next token. Also, "next token predictor" is technically correct, but a little misleading. The example I like is a horror novel. Imagine you've read 400 pages and the last sentence says, "...and the killer's name is ___." You're still only predicting the next token. But to predict it correctly, you may need to understand the characters, motives, timeline, clues, red herrings, and basically the entire book. That's what training forces these models to do at enormous scale. To get better at predicting the next token across trillions of examples, they end up compressing patterns about language, programming, science, logic, people, and the world into billions of numerical parameters. GPT-3 in 2020 was basically what happens when you make one of those things huge . Researchers found scaling laws: increase model size, training data, and compute together, and performance improves surprisingly predictably. At sufficient scale, models also started becoming useful at things smaller models were hopeless at, like translation, coding, arithmetic, physics, and few-shot learning. People argue about how truly "emergent" some of those abilities are, but from the user's perspective it can absolutely look like a switch flipped. GPT-3 was still... not great. If they relaunched it today, I think people would be shocked by how uniquely awful it was compared with what we're used to now. Sometimes it answered your question. Sometimes it continued your question. Sometimes it wrote both sides of the conversation. Sometimes it just wandered into the woods and died. Then came instruction tuning. Instead of only training the model to continue arbitrary text, researchers showed it tons of examples that were basically "user asks this > good assistant responds like this." Obvious in hindsight, but enormously important. It taught the giant autocomplete machine to actually try to do what you asked. Then RLHF, reinforcement learning from human feedback. Humans compare answers: this one is good, this one sucks, this one is dangerous, this one is excellent. Those preferences become a reward signal, and the model is trained to produce more of the responses people prefer. That's a huge part of how we got from "weird autocomplete" to "assistant." It also created new failure modes. If you reward a model for giving people answers they like, it can learn that agreeing with them is pretty rewarding too. Hence the infamous sycophancy problems, and in extreme cases people getting pulled into delusional feedback loops with AI. I actually knew someone who was hospitalized during one of those. So, you know. Progress. Then we get to one of the stars of the show: chain of thought. Imagine every question had to be answered immediately, before you had a chance to think. What's 7,278 divided by 28? Maybe you can do that instantly. I can't. Give me a piece of paper and 30 seconds, though, and suddenly it's easy. Researchers found that sufficiently capable models performed dramatically better on math, logic, coding, and planning if they were allowed to work through intermediate steps first. Eventually, instead of relying on users to type "think step by step," companies started training reasoning behavior directly. The model didn't stop being a next-token predictor. We just discovered that: think > break problem apart > notice mistake > try again > check answer > respond is much more useful than: question > answer At the same time, researchers got much better at the boring but critical parts of training. DeepMind's Chinchilla work showed that some giant early models were undertrained. A smaller model trained on enough high-quality data could beat a much larger one. Scaling became less "make it gigantic" and more "find the right balance of parameters, data, and compute." The data improved too. Better filtering, deduplication, quality control, data mixtures, and eventually synthetic data meant models spent less training time on garbage and more on useful patterns. Context windows also exploded. Older chatbots often forgot what you said because they literally could not see very far back. Modern systems can fit entire books, large codebases, or huge document sets into context, while retrieval systems can search external information and insert the relevant pieces before the model answers. Then came multimodality. Instead of only understanding text, models learned to work with images, audio, screenshots, charts, PDFs, and video. You no longer have to describe the broken UI. You can just show it the screenshot. And then we gave them tools, which is another huge part people miss. A neural network doing arithmetic internally might be mediocre at arithmetic. A neural network that knows when to open a calculator isn't. Give it web search, Python, a terminal, databases, APIs, files, calculators, and external memory and you're no longer really talking to "a chatbot." You're talking to a model controlling software. Coding improved especially fast because code has objective feedback. Did it compile? Did the tests pass? Did the output match? That gives researchers huge amounts of automatic reinforcement signal without asking a human whether every answer "looks right." Then came the newer reasoning models. Instead of only using reinforcement learning to teach the model what humans prefer, you can reward it for actually solving verifiable math, science, logic, and coding problems correctly. Over enough attempts, models learn strategies like breaking problems apart, checking assumptions, using tools, backtracking, and trying another approach. That led into inference-time compute, another scaling curve. You can spend more compute not only while training a model, but after the user asks a question. Hard problem? Spend more effort exploring solutions and checking the work. Easy problem? Answer immediately. There are a bunch of smaller improvements too. Mixture-of-experts lets huge models activate only parts of themselves at once. Distillation transfers capabilities into smaller models. Quantization and better kernels make them cheaper to run. Synthetic data lets strong models generate new training problems, provided you can verify them. And increasingly we have agents. Instead of answering once, a model can search, open a file, write code, run it, inspect the error, fix it, and keep going toward a goal. Image and video had a parallel explosion. Diffusion models learned to turn noise into an image through repeated refinement, then better data, architectures, text understanding, and compute pushed that into photorealistic images and coherent video. So there wasn't one single breakthrough. It was roughly: transformers > scaling > better data/compute recipes > instruction tuning > RLHF > chain of thought > long context/retrieval > multimodality > tools > reinforcement-learned reasoning > inference-time compute > agents And these things multiply each other. Better reasoning is more useful with tools. Tools are more useful with long context. Reinforcement learning gets more powerful when answers can be automatically verified. Stronger models can generate better training data and help researchers write better code. So yes, throwing ungodly amounts of compute at the problem absolutely mattered. But it wasn't just compute. It was six years of researchers repeatedly finding better ways to use that compute, train the models, extract useful behavior from them, and connect them to the outside world. That's why it feels like AI went from 0 to 100 overnight. The curve had been moving upward the entire time. Most people just didn't notice until enough improvements stacked together that the thing on the other side of the screen stopped feeling like a shitty chatbot and started feeling like something fundamentally different. And the part I think people should pay attention to is that none of the things driving that curve have stopped. We're still building larger clusters. Training is still getting more efficient. Reasoning RL is still scaling. Models are getting more tools and more inference-time compute. Synthetic data is improving. And increasingly, AI is becoming useful at the actual research and engineering required to make better AI. That's where the "singularity" discussion starts becoming less silly than it sounded ten years ago. You don't need a magical moment where a computer wakes up. You just need AI systems to become good enough at AI research that they materially accelerate the creation of the next generation. Then some portion of AI progress starts feeding back into itself. Nobody knows exactly how strong that feedback loop will be or how fast it will run. But the same process that took us from GPT-3 barely holding a conversation to models that can reason, code, use computers, do research, and generate convincing video is still running. And now the thing we're improving is starting to help us improve the thing. So if the last five or six years felt absurdly fast, I would not assume the next five are going to feel slower.
大家好，我从事人工智能工作，大约十年来一直密切关注这些东西，所以我会尽力尽可能清楚地解释它。 2020年，变形金刚已经出现好几年了。谷歌顶部评论中链接的论文以令人难以置信的技术细节解释了它们，但基本上 Transformer 为人工智能提供了一种更好的方式来回顾文本的不同部分，并找出对当前所写内容重要的内容。它曾经是，而且从根本上来说仍然是一个“下一个令牌预测器”，但突然之间有更多的机制来预测下一个令牌。此外，“下一个代币预测器”在技术上是正确的，但有点误导。我喜欢的例子是恐怖小说。想象一下，您已经读了 400 页，最后一句话说：“……凶手的名字是 ___。”您仍然只是预测下一个标记。但要正确预测，你可能需要了解人物、动机、时间线、线索、转移注意力，以及基本上整本书。这就是训练迫使这些模型大规模执行的任务。为了更好地预测数万亿个示例中的下一个标记，他们最终将有关语言、编程、科学、逻辑、人类和世界的模式压缩为数十亿个数字参数。 2020 年的 GPT-3 基本上就是当你把其中一件事情变得巨大时所发生的事情。研究人员发现了缩放法则：增加模型大小、训练数据并一起计算，并且性能的提高令人惊讶地可预测。在足够规模的情况下，模型也开始在较小模型无法完成的事情上变得有用，例如翻译、编码、算术、物理和小样本学习。人们争论其中一些能力到底有多“新兴”，但从用户的角度来看，它绝对看起来像是一个开关被翻转了。 GPT-3 仍然......不太好。如果他们今天重新推出它，我想人们会因为它与我们现在所习惯的相比有多么糟糕而感到震惊。有时它回答了你的问题。有时它会继续你的问题。有时它会写出对话的双方。有时它只是溜进树林里就死了。然后是指令调整。研究人员不仅训练模型继续任意文本，还向模型展示了大量示例，这些示例基本上是“用户提出这个问题 > 好的助手会这样回应”。事后看来很明显，但非常重要。它教会了巨大的自动完成机器真正尝试做你要求的事情。然后是 RLHF，根据人类反馈进行强化学习。人类比较答案：这个很好，这个很糟糕，这个很危险，这个很好。这些偏好成为奖励信号，模型经过训练可以产生更多人们喜欢的反应。这是我们从“奇怪的自动完成”到“助手”的一个重要部分。它还创造了新的故障模式。如果你奖励一个为人们提供他们喜欢的答案的模型，它就会知道同意他们的观点也是相当值得的。因此，就会出现臭名昭著的阿谀奉承问题，在极端情况下，人们会陷入人工智能的妄想反馈循环中。我实际上认识一个在其中一次住院的人。所以，你知道。进步。然后我们就到了这部剧的明星之一：思想链。想象一下，在你有机会思考之前，每个问题都必须立即得到回答。 7,278 除以 28 等于多少？也许你可以立即做到这一点。我不能。不过，给我一张纸和 30 秒，突然间一切就变得容易了。研究人员发现，如果允许模型首先完成中间步骤，那么能力足够强的模型在数学、逻辑、编码和规划方面的表现会显着提高。最终，公司不再依赖用户输入“一步一步思考”，而是开始直接训练推理行为。该模型并没有停止作为下一个标记的预测器。我们刚刚发现：思考 > 分解问题 > 注意到错误 > 再试一次 > 检查答案 > 回应比：问题 > 回答更有用。同时，研究人员在培训中无聊但关键的部分方面取得了更好的成绩。 DeepMind 的 Chinchilla 工作表明，一些巨大的早期模型训练不足。经过足够高质量数据训练的较小模型可以击败较大模型。扩展变得不再是“让它变得巨大”，而是更多地“找到参数、数据和计算的正确平衡”。数据也有所改善。更好的过滤、重复数据删除、质量控制、数据混合以及最终的合成数据意味着模型在垃圾上花费的训练时间更少，而在有用模式上花费的训练时间更多。上下文窗口也爆炸了。较旧的聊天机器人经常忘记您所说的内容，因为它们实际上看不到很远的情况。现代系统可以将整本书、大型代码库或庞大的文档集放入上下文中，而检索系统可以搜索外部信息并在模型答案之前插入相关片段。然后是多模态。模型不再仅仅理解文本，而是学会了处理图像、音频、屏幕截图、图表、PDF 和视频。您不再需要描述损坏的 UI。您只需向其显示屏幕截图即可。然后我们给了他们工具，这是人们错过的另一个重要部分。在内部进行算术的神经网络可能在算术方面表现平平。知道何时打开计算器的神经网络则不然。给它网络搜索、Python、终端、数据库、API、文件、计算器和外部存储器，你就不再真正与“聊天机器人”交谈。您正在与模型控制软件交谈。编码改进得特别快，因为代码有客观的反馈。编译通过了吗？测试通过了吗？输出匹配吗？这为研究人员提供了大量的自动强化信号，而无需询问人类每个答案是否“看起来正确”。然后出现了更新的推理模型。您不仅可以使用强化学习来教授模型人类喜欢的内容，还可以奖励它实际正确解决可验证的数学、科学、逻辑和编码问题。经过足够多的尝试，模型会学习一些策略，例如分解问题、检查假设、使用工具、回溯和尝试其他方法。这导致了推理时间计算，这是另一条缩放曲线。您不仅可以在训练模型时花费更多的计算，而且在用户提出问题之后也可以花费更多的计算。难题？花更多的精力探索解决方案并检查工作。容易出问题吗？立即回答。还有一些较小的改进。专家混合让巨大的模型只能同时激活自身的一部分。蒸馏将功能转移到更小的模型中。量化和更好的内核使它们的运行成本更低。合成数据可以让强大的模型生成新的训练问题，前提是您可以验证它们。我们的代理也越来越多。模型不需要回答一次，而是可以搜索、打开文件、编写代码、运行代码、检查错误、修复错误，然后继续实现目标。图像和视频同时爆发。扩散模型学会了通过反复细化将噪声转化为图像，然后更好的数据、架构、文本理解和计算将其转化为逼真的图像和连贯的视频。所以没有任何一个突破。大致是：变压器 > 扩展 > 更好的数据/计算方法 > 指令调整 > RLHF > 思想链 > 长上下文/检索 > 多模态 > 工具 > 强化学习推理 > 推理时间计算 > 代理 这些东西相互相乘。借助工具，更好的推理会更有用。工具在长上下文中更有用。当答案可以自动验证时，强化学习就会变得更加强大。更强大的模型可以生成更好的训练数据并帮助研究人员编写更好的代码。所以，是的，在这个问题上投入大量的计算绝对很重要。但这不仅仅是计算。六年来，研究人员不断寻找更好的方法来使用计算、训练模型、从中提取有用的行为，并将它们与外部世界连接起来。这就是为什么感觉 AI 一夜之间从 0 上升到 100。曲线一直向上移动。大多数人只是没有注意到，直到足够的改进叠加在一起，屏幕另一侧的东西不再感觉像一个糟糕的聊天机器人，而是开始感觉像一些根本不同的东西。我认为人们应该注意的是，推动这条曲线的因素都没有停止。我们仍在构建更大的集群。培训仍然变得更加高效。推理强化学习仍在扩展。模型正在获得更多的工具和更多的推理时间计算。综合数据正在改进。人工智能在制造更好的人工智能所需的实际研究和工程中越来越有用。这就是“奇点”讨论开始变得不像十年前听起来那么愚蠢的地方。您不需要计算机唤醒的神奇时刻。你只需要人工智能系统在人工智能研究方面变得足够好，从而实质性地加速下一代的创造。然后人工智能进步的某些部分开始反馈给自身。没有人确切知道反馈循环的强度或运行速度。但是，将我们从几乎无法对话的 GPT-3 模型转变为可以推理、编码、使用计算机、进行研究并生成令人信服的视频的模型的过程仍在运行。现在我们正在改进的东西开始帮助我们改进它。因此，如果过去五六年感觉快得离谱，我不会认为接下来的五年会感觉更慢。