Anthropic's Chloe Lubinski explains how AI works (in 14 minutes)

章节 1：人工智能正在加速前进

📝 本节摘要：

本节介绍人工智能发展的速度与动力。随着算力、能源、数据和训练投入增加，模型能力会持续提升，资本又会进一步推动算力扩张，形成难以停止的循环。未来系统可能参与构建自身后继者，并在网络安全、生物学等领域带来巨大影响，因此法律、制度与安全护栏亟须跟上。

[原文] [Chloe Lubinski]: I work at Anthropic, where I lead the research partnerships with the world's wisdom traditions. And my job really has two parts to it. So, the first, it's to help these experts in these various fields and disciplines actually understand AI, what it is, what's happening now, and where it's going. And the second part is to listen and to learn and to funnel wisdom back into the organization, back to the people that are building this technology. Just last week, I was walking my little red-haired cocker spaniel in San Francisco, thinking of what might be most helpful to you today in having these conversations. And the thing is, I've probably had hundreds of conversations now across 20 or so traditions and disciplines, and I found, again and again, just how important it is for folks to really understand the basics before we can even start to talk

[译文] [Chloe Lubinski]: 我在 Anthropic 工作，负责与世界各大智慧传统开展研究合作。我的工作主要有两部分。第一，是帮助来自不同领域和学科的专家真正理解人工智能：它是什么、现在正在发生什么，以及未来会走向哪里。第二，是倾听、学习，并把这些智慧带回组织，带给那些正在构建这项技术的人。就在上周，我在旧金山遛我那只红毛可卡犬时，还在思考今天怎样才能最有效地帮助大家展开这些对话。事实上，我大概已经和来自二十种传统或学科的人进行了数百次交流，而我一次又一次地发现，在我们开始讨论如何让人工智能良性发展之前，让大家真正理解基础知识是多么重要。

[原文] [Chloe Lubinski]: about how this can go well. So, my hope today in this short time is to give you some of those essentials as quickly as I can. So, I'm going to jump right in. The first thing that I really want to tell you is, my goodness, to know that this technology is real and that it's coming faster than you think, and the force behind it is enormous. Now, you may or may not have heard of the scaling laws, which is really what kicked off this whole race to begin with. And you really don't need to understand anything about this graph other than this, which is these models get predictably better with more compute. And the more energy, the more data, the more training that goes into them, and they get smarter, and they get smarter about everything.

[译文] [Chloe Lubinski]: 也就是如何让它朝着良好的方向发展。所以，我希望利用今天这段短暂的时间，尽可能快地向大家介绍其中一些要点。那我就直接开始。首先，我真的想告诉大家：这项技术是真实存在的，而且它到来的速度比你想象的更快，背后的推动力也极其强大。你可能听说过，也可能没听说过“规模定律”；正是它真正开启了这场竞赛。关于这张图，你其实不需要理解太多，只要知道一点：投入更多计算资源，这些模型就会以可预测的方式变得更强。投入越多的能源、数据和训练，它们就越聪明，而且会在方方面面变得更聪明。

[原文] [Chloe Lubinski]: And so, with more money, which buys compute, you can essentially purchase intelligence. And that's kicked off a cycle that is very hard to stop. A better model does more economically valuable work, which attracts more capital, which buys more compute, which trains a better model, and around and around it goes. And now, there's a further turn of the wheel. These systems are starting to build their own successors. What researchers call recursive self-improvement is helping to build them. But when Claude 8 can build Claude 9, which can build Claude 10, things will begin to move even more quickly.

[译文] [Chloe Lubinski]: 因此，有了更多资金，就能购买更多算力，从本质上说，也就能购买智能。这启动了一个很难停止的循环：更好的模型能够完成更多具有经济价值的工作，从而吸引更多资本；更多资本购买更多算力，训练出更好的模型；如此周而复始。现在，这个循环又出现了进一步的发展。这些系统开始构建自己的后继者。研究人员所说的“递归式自我改进”，正帮助它们做到这一点。但当 Claude 8 能够构建 Claude 9，而 Claude 9 又能构建 Claude 10 时，事情的进展速度将会进一步加快。

[原文] [Chloe Lubinski]: And just to be concrete about what more capable actually means, our most capable model, in its first month of only limited release, found over 10,000 serious security vulnerabilities across partner software. Flaws that human experts had missed for years and sometimes decades. Now, the same trajectory there also holds in biology, which is why we have entire teams at Anthropic dedicated to safeguarding against it. And we also think other domains will soon follow. So, Anthropic stated just a few weeks ago that if it were possible to slow down, so that our laws and our institutions and guardrails that we actually need have time to catch up, it would be a very good thing.

[译文] [Chloe Lubinski]: 具体来说，更强的能力究竟意味着什么？我们最强大的模型在仅限量发布的第一个月里，就在合作伙伴的软件中发现了超过一万个严重的安全漏洞。这些漏洞，有些人类专家已经漏看了数年，甚至数十年。同样的发展轨迹也适用于生物学，这就是为什么 Anthropic 整个团队都在专门开展相关安全防护工作。我们还认为，其他领域很快也会出现类似情况。因此，Anthropic 几周前表示，如果能够放慢脚步，让我们的法律、制度以及真正需要建立的安全护栏有时间跟上，那将是一件非常好的事情。