个性化教育的“电力革命”:从工业化集约授课到AI双人教学
在过去25年里,尽管我们在教育慈善、特许学校等领域投入了数十亿美元,但美国学校的整体教学效果甚至低于我刚开始投身该领域时的水平。教育是一个极具挑战性的难题。为了理解我们所面临的瓶颈,可以回顾18世纪的工业革命。在早期,大多数工厂都由大型蒸汽机驱动,通过一根旋转的主轴、皮带轮和传送带来分发动力。当电力出现时,工厂主们只是简单地用一台电动机替换了蒸汽机,却发现生产率并没有任何提升。直到他们意识到真正的限制因素在于动力分发系统本身,并将庞大的机械传送系统彻底拆除,转而为每台设备安装独立的微型电动机,工厂的布局才得以自由调整,生产率也随之迎来了爆发式增长。
这个经典的经济学启示同样适用于当下的教育系统。我们一直在试图改进课堂教学,但最根本的“动力分发系统”依然是25个孩子被困在同一个学习进度上。这种工业化的集约授课模式创造了巨大的摩擦:大约三分之一的学生进度落后,三分之一的学生感到厌烦,而教师只能迁就剩下三分之一的中间段学生。个性化教育(Individualized Education: 针对每个学生独特认知节奏和背景知识定制的教学模式)是解决这一摩擦的终极方案。早在40年前,教育学家本杰明·布鲁姆(Benjamin Bloom)就在其著名的“二西格玛问题”(2 Sigma Problem: 接受一对一辅导的学生,其学习效果比传统课堂教学高出两个标准差)中证实了这一点。如果每个孩子都能拥有专属的导师,他们的学习效率将呈指数级提升。虽然目前人类一对一导师的成本高达每年10万美元,但随着AI性能在过去三年中以惊人的速度翻倍,我们正走向一个AI导师能够媲美甚至超越人类导师的时代,从而让全球每个孩子都能享受到顶级的个性化教育。
Original English Source
So, Reed, education, which a lot of people I don't I don't know how many know how active you are in education, education philanthropy, charter schools, et cetera. What are you doing in that space? Uh I'm on the board of Khan Academy. Um and generally I've been working for the last 25 years to try to find ways to make, in particular, US schools, but some international work better and have better outcomes for kids. And I would say after a billion dollars in 25 years, we're down below where I started. So, it's a hard problem. And what do you, you know, we heard about the risks of edtech, but you are investing in edtech, you're giving to edtech. What are your thoughts? What's the tradeoff there? Why do you continue to potentially believe there? You know, in the 18th century, most factories were steam engine driven. So, you have a big steam engine rotating rods and pulleys and belts and drove the factory, very efficient. And then electricity came in and they replaced the steam engine with an electric engine thinking that it was going to help a lot and productivity didn't increase at all and they were puzzled. And then they realized here the limiting factor is the power distribution system, all these spinning rods and mechanical power. And they ripped that out and just put in small electric motors for each device. Then they could move things around, fit them in better cuz it wasn't aligned to spinning. They could have variable speed, turn different motors on and off, and then productivity increased dramatically. So, it's a classic kind of economic surprise lesson. And it's always stuck with me because I think that's what we're seeing, which is we keep doing things to improve classroom education, but the fundamental power distribution is 25 kids stuck at the same level. And the friction that that creates, which is, you know, roughly a third of the kids are behind, a third of the kids are bored and above, and a third you're teaching to, is the fundamental friction in our mass education system. And the theory is if each of us had an individual human tutor, so imagine you go to school, you have your normal social activities, but when it comes to learning, you get an individual who's going to sit down with you, and then they could do Khan Academy, or they could do Color Book, or whatever's appropriate, that learning would be massively increased. Um and there was a famous study 40 years ago, Bloom, that documented this. Uh and now a friend of mine, Ben Summers, is redoing that study but at much bigger scale. And I think what we're going to see is the key to much more learning, where middle school kids know all of the high school curriculum, high school knows all of the college, much better outcomes, will be individualized education. How do you square that with what Jonathan Haidt said, "Look, these screens, I mean, at least it's a correlation. We don't know causal yet, but it seems to be distracting. It seems to correlate with some test scores going down." Is it for you a little bit of it? Is it an all or nothing type of thing? I'm a huge fan of Jonathan. Totally agree with all his zip up phones and don't use phones. And I think you guys clarified he likes offline tablets. That's fine. It's the internet that's the problem, not the physical device. And so I think there's lots of ways to cater to the concerns that he correctly expresses, which is letting kids go wild on the internet under 16 is not great. But you don't have to do that to be able to do individualized tutoring. So the individualized tutoring we're doing is with humans, okay? And they can use some technology if they want. But then obviously that's cost prohibitive cuz it's about $100,000 per kid per year. So then the hard challenge becomes how do we use AI to approximate that human and provide everyone an individualized education as AI gets better. Current AI is not good enough to do that. But you know, in 3 years we've gone from, you know, chat GPT 3.5 and barely being able to do high school math to just incredible intelligence. And that's likely to just continue to double, double, double and get better and better and better. So there is a world where the AI, I think, will be able to match and beat the human individual tutor.
教师角色的升维:告别“讲台圣人”,聚焦社群与情感共鸣
当AI导师在未来十年普及后,学校的运作模式将发生根本性转变。学校承载着三大核心功能:培养良好公民、提供经济上升通道以及促进社会化(Socialization: 学习如何与家庭成员以外的社会群体协作)。在线工具或AI仅在知识传授(即提供经济通道的硬技能部分)上表现出色,而这反而能让教师从繁重的课本教学中解放出来,将核心精力转向社交与情感学习(Social-emotional Learning: 培养学生的同理心、人际交往和团队协作等非认知能力)。
在传统的教育体制下,教师的职业自豪感主要源于对教材的讲授,这被教育界讽刺地称为“讲台上的圣人”(Sage on the stage: 教师作为知识垄断者在讲台上单向输出)。未来的教育将彻底消灭这种单向灌输的授课模式,推动教师向上游的高价值链移动。教师将转变为引导者,组织苏格拉底式讨论、场景模拟和主动学习项目。这不仅能显著提升学生的学习效果,对教师而言也更具乐趣和成就感。更为重要的是,这种软件驱动的教育模式具备极强的全球扩展性。以可汗学院(Khan Academy)为例,其技术平台已经极大地抬高了全球教育的兜底网,甚至让阿富汗身处困境的年轻女性通过自学考入麻省理工学院(MIT)。
Original English Source
And what does that world look like? Let's just say it's in 10 years. Are you imagining that you're just socializing and then you go to this AI tutor that even it maybe looks embodied in some way, but are you imagining there's no human teacher? What do you think happens to that role, that profession? What about the adult humans in the classroom? Well, let's think about schools. So three big purposes. One is create good citizens. Another is give economic opportunity to the kids. And then the other is socialization. Um social emotional learning, how to work with other people, adults outside of your family. So only in the first part is really where online is really good. And what we want to do is have teachers be able to focus on social emotional learning. They become really helping maturity, interpersonal skills, values clarification, all those kind of higher level things. And then we've got to figure out in the AI age, you know, how do we enhance that role of creating good citizens? Okay, cuz one of our ways to come together is to have a tighter idea of who we are as a country and the K-12 systems be able to take that for granted for the last 100 to 200 years, maybe post Civil War, because society was working well. But if we're going to go into a period of stress, it's really important for that mission to get attention also. And I just want to double click on that and make sure maybe we have a common vision or maybe it's divergent. You still see a major role for the human teacher and the human classroom. You just see that role shifting. But you're going sort of. Okay. So most teachers today, their pride center is teaching and connecting and understanding the material. Okay? Some part of that is really connecting on a personal level with the student. So that part is the social emotional. But in terms of transferring information, what educators cynically call sage on a stage, it's eliminating sage on a stage as a teaching modality. Okay? And so it's really just focused on the individual. What would education be if there was no mass teaching? And to be fair, you know, if you go to an ed school, if you went to an ed school 20 years ago, this is what they were preaching. Differentiated instruction, active learning, don't be sage on the stage, have a Socratic discussion, do a simulation. So it's really potentially, and this is what I say cuz I get this question a lot, the teacher I think moves up the value chain and is able to facilitate and drive a lot of that active learning, which is better for everyone. I think it's more fun for the teacher. Right, the positive side of it. And the other part is once you can do a lot of this in software, you can do it globally. So it's really hard to scale up the teacher force. But if you have incredible software, it's pretty inexpensive to make it globally available. No, that's right. I mean, you know, we talk a lot about it at the Khan Academy board that the technology can raise the safety net, raise the floor. We've seen stories of young women in Afghanistan using Khan Academy. One of them is at MIT now. I mean, some amazing things. But, we see also in the classroom most students need that human element. Arguably, all of them do ideally if they have it.
Anthropic 的非营利共识与 AI 发展速度的双向博弈
除了教育,我也加入了 Anthropic(人工智能安全与研发公司)的董事会。之所以做出这个决定,是因为该公司的使命并非追求利润最大化,而是为了帮助人类安全地跨越人工智能这道时代传送门。
在目前的AI安全讨论中,许多闭门会议充斥着对AI可能带来超过10%的“人类生存威胁”(Existential risk: 导致人类灭绝或文明永久性崩溃的风险)的担忧。如何在保持极快研发速度的同时,确保技术的安全与负责任,是董事会面临的核心挑战。随着行业内几大头部模型的快速迭代,各大厂商会在不同的技术维度上轮番领跑。对于国家而言,拥有三款相互竞争且同样优秀的自研模型供社会选择是良性的。我们会逐案评估每一次技术突破的潜在收益与下行风险,尤其是在AI开始具备自我优化和自我代码编写能力之后,必须严密审视其是否发明了全新的学习范式,而不仅仅是评估其生成的代码行数。
Original English Source
Switching gears a little bit because your other board you're on isn't obviously very related to this, Anthropic. I actually I'm just curious what made you join that board? What's it like at those board meetings when I'm assuming y'all talk about pressures from the White House, how your new model might break all software. Tell us what you can. Yeah, it's a lot like your board meetings, you know. Talking about the software and what it can do and how it needs to get better. So, the mission of the company is very clear. It's not maximizing profits. It's that we're successful the humanity. How do we get into the age of AI successfully crossing through sort of this portal. And they recognize it's going to be very challenging and that they're very dedicated to having that happen in rolling out AI and having the incredible beneficial outcomes, whether that's the Waymo's self-driving, whether that's gene editing, whether that's curing cancer. You know, 10 20 years from now, it's very likely we'll have pretty abundant energy. We will have amazing health outcomes. I mean, so much positive outcome from the AI infusion into science. And I would say Anthropic's very serious about helping us manage or avoid most of the downside. And how have y'all pulled that off in closed doors? And I've been in some of those closed doors where people are genuinely afraid of more than 10% chance that this could be an existential threat to humanity. It does seem that Anthropic somehow is proving it to be very responsible or that that's what we appears to be and at the same time moving very fast. The hyper speed. It feels like almost every few weeks there's something new and it's very tangible in what it might do for work. How are y'all balancing that at Anthropic instead of just saying go go go? You know, I think all of the big models are improving rapidly and you know, you're probably going to see them go, you know, certain ones are the lead in certain areas over time and you know, frankly, it's good for the country if you know, we have three really successful models to choose from. Um, and then how do they balance it? Um, you know, case by case. Uh, so I think each one have to see, you know, how accelerated is the learning, how powerful is it? What are the downside scenarios? What are the new possibilities it can do? And I'm curious about Anthropic itself. I had a chance to visit there a couple of weeks ago and you know, I take pride that, you know, Khan Academy were super nimble and we're innovating etc. and we're obviously trying to leverage AI for social good as much as possible. When I visited there, I tangibly felt that they were pioneering completely new ways of running an organization, new ways of developing product. I think it was something that co-work was what was it a week or two that that it was essentially built primarily by the AI itself. What will an organization look like in the future? I think y'all have a pretty good crystal ball there. Yeah, I don't know that most companies will come to look like Anthropic. I think it really depends on your industry. If you happen to be a pure software company, then might be relevant for that class of company, but broadly across the economy, I think everyone is figuring out, you know, it's a bigger version of the internet wave where all companies had to, you know, do things and we used to talk about our AOL keywords, you know, and crazy stuff like that, right? Which was the phasing in. Um, this is a lot bigger and more intense, but it's sort of a larger version of that same thing, which is all companies around the world, organizations, governments, militaries are scrambling to figure out, you know, how to use AI well.
技术扩散的延迟与未来智能红利的“主权基金”分配
面对AI在软件工程、客服中心等领域的广泛应用,公众对结构性失业(Structural unemployment: 因技术变革或经济结构调整导致劳动者技能与岗位需求不匹配的长期失业)的担忧日益加剧。尽管AI技术本身的演进速度惊人,但其在实际产业中的技术扩散(Technology diffusion: 一项新思想或新技术在社会系统成员中传播和采纳的过程)往往伴随着巨大的时间滞后。
以自动驾驶为例,早在2007年的国防高级研究计划局(DARPA)挑战赛中,业界就预期自动驾驶将迅速普及,但近20年后的今天,自动驾驶里程在总出行里程中的占比依然微乎其微。另一个例子是,人工智能先驱杰弗里·辛顿(Geoffrey Hinton)曾在2016年断言,由于AI的表现,5年内我们将不再需要放射科医生。然而实际情况是,AI的应用降低了诊断成本,促使医生开具了更多的核磁共振(MRI)检查,反而导致了今天放射科医生的严重短缺。这种预测的偏差证明,即使是顶尖学者也难以精准预估技术扩散的具体节奏。
尽管转型需要时间,但在未来20年内,这些技术颠覆必定会发生。与以往伴随股市暴跌、税收锐减的经济衰退不同,由AI驱动的失业潮将呈现出一种前所未有的奇特景观:高生产率、高GDP增长、股市繁荣、高额财政税收,却同时伴随着高失业率。因为政府有充足的资金来应对这一挑战,我们应当借鉴阿拉斯加永久基金(Alaska Permanent Fund)或挪威主权财富基金(Norwegian Sovereign Wealth Fund)的运作机制。通过建立由AI高额税收支撑的国民信托基金,向全体公民部分分享社会智能红利。这种基于资产收益分红的财富再分配模式,能够避免传统无条件基本收入(UBI)带来的社会福利负面联想,从而为人类走向丰饶与和谐的社会提供一条可行路径。
Original English Source
I guess related to that, people are talking about it with software engineering, people are talking about call centers. I have a friend who has one of his startups has a call center in the Philippines. They're going to lay off 80%. That's 7% of that country's GDP. How are you thinking about jobs? How just as a thinker, how is Anthropic thinking about it? Well, look, if you look over the last 200 years, there've been a bunch of dislocations, but they were in, you know, happened slowly and over one part of the economy. Um and so the danger is, you know, are there multiple that happen in multiple fields? If these happen slowly, then people are able to find other roles. Um so it depends on how fast this all comes. Um and again, there's I would say the biggest uncertainty for everybody is how fast is the AI getting better and how fast will it be getting better going forward? And then that um changes your views and assumptions. So, let's look at self-driving cars. I mean, you know, we thought 20 years ago it was going to be pretty fast, and it's 20 years later from when it started, 2007 in the DARPA Grand Challenge, and we're like what, 0.1% of all miles, maybe 0.001%? I mean, that's really pretty tiny, okay, 20 years later. So, these things take a long time to actually mature and diffuse. Another example is Geoffrey Hinton won the Nobel Prize for his work on neural nets and really the father of AI. And in 2016, so 10 years ago, he said, "Stop training radiologists now because in 5 years, 2021, there would be no need for them." So, what's happened instead is um as radiology got AI boosted, the cost came down, you can walk into an MRI center in the US for $300 and get an MRI now, and so docs started ordering them more and using them more and insurance covered them more. And so the number of scans has gone way up, and guess what? We have a shortage in radiologists. We have 35,000, we need about 40,000. Wages have climbed to close to $500,000 a year. So, even the best-intentioned people in the field, okay, can prophecy disaster in radiology and have it be not accurate. So, again, and it's just a timing thing because in 20 years, I'm confident Hinton will be right. Okay? So, just think there's two examples there, which is a lot of the stuff may not happen right away, but it's still probably going to happen in a long time, 20 years. Some of it might happen in a short time, so you want to be ready for it. And that makes sense, although something does feel different about this time. And all the people leading these these AI labs are talking about not 20 years, they're talking about next year. They're talking about 2 years, you're going to have a data center of, you know, superintelligent geniuses that can do our work. Do you Do you think they're wrong, or do you think it's a probability? And even if it's a even if it's a 10% chance that they're right, are you worried that this can lead to political polarization? What happened in globalization can now happen What happened in 30 years could happen in two or three. Is that Is that not a concern, or should we start doing something about that? Um if the AI really gets incredible in a very short amount of time, like starts writing itself and self-improving, Isn't it writing 90% of itself right now? Um you know, again, lines of code is a tricky measure. You know, when it's invented a new type so of learning, you know, so there's, you know, reinforcement learning. You know, when it invents something completely new as the then you can say that. But so there are cases where it's pretty fast, and so I think it's important to say we should be ready. Now, here's the thing, we talk a whole bunch about unemployment, what it will do. Every other time we've had big unemployment, it's been a recession, and so the stock market's down and government tax revenues are down. Um this time, if AI is successful in the way we think it will, I think we're going to see high productivity, high GDP growth, um high stock market, and high unemployment. So we'll have money to do things. And so think of it like the Alaska fund, which is oil, or the Norwegian sovereign fund. We need some ideas like that. What are we going to do with all these huge tax revenues that are going to be able to come in with big growth. And if we've got a fund which is for the benefit of all citizens, then we may in fact be able to have a path to a, you know, a glorious and harmonious society. And I don't mean, you know, UBI. That's got a a bad taste to it, but it's uh a partial sharing of the rewards, which is again what happened with the Alaska fund and and the Norwegian fund. Makes sense. Well, Reed Hastings, thank you so much. And thanks everyone. Accelerating possibilities.
📌 文中提及的人物和组织
公司/组织: Khan Academy, Anthropic