AI Needs to Be Both Trusted and Trustworthy（AI 需要既值得信赖又值得被信赖）

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原文链接：AI Needs to Be Both Trusted and Trustworthy | WIRED

原文：

In 2016, I wrote about an internet that affected the world in a direct, physical manner. It was connected to your smartphone. It had sensors like cameras and thermostats. It had actuators: thermostats, drones, autonomous cars. And it had smarts in the middle, using sensor data to figure out what to do and then actually do it.

The classical definition of a robot is something that senses, thinks, and acts—that’s today’s internet. We’ve been building a world-sized robot without even realizing it.

In 2023, we upgraded the “thinking” part with large language models (LLMs) like GPT. ChatGPT both surprised and amazed the world with its ability to understand human language and generate credible, on-topic, humanlike responses. But what these are really good at is interacting with systems formerly designed for humans. Their accuracy will get better, and they will be used to replace actual humans.

In 2024, we’re going to start connecting those LLMs and other AI systems to both sensors and actuators. In other words, they will be connected to the larger world, through APIs. They will receive direct inputs from our environment, in all the forms I thought about in 2016. And they will increasingly control our environment, through IoT devices and beyond.

It will start small: summarizing emails and writing limited responses. Arguing with customer service—on chat—for service changes and refunds. Making travel reservations.

But these AIs will interact with the physical world as well, first controlling robots and then having those robots as part of them. Your AI-driven thermostat will turn the heat and air-conditioning on based on who’s in what room, their preferences, and where they are likely to go next. It will negotiate with the power company for the cheapest rates by scheduling usage of high-energy appliances or car recharging.

This is the easy stuff. The real changes will happen when these AIs group together in a larger intelligence: a vast network of power generation and power consumption, with each building just a node, like an ant colony or a human army.

Future industrial-control systems will include traditional factory robots, as well as AI systems to schedule their operation. It will automatically order supplies, as well as coordinate final product shipping. The AI will manage its own finances, interacting with other systems in the banking world. It will call on humans as needed: to repair individual subsystems or to do things too specialized for the robots.

Consider driverless cars. Individual vehicles have sensors, of course, but they also make use of sensors embedded in the roads and on poles. The real processing is done in the cloud, by a centralized system that is piloting all the vehicles. This allows individual cars to coordinate their movement for more efficiency: braking in synchronization, for example.

These are robots, but not the sort familiar from movies and television. We normally think of robots as discrete metal objects, with sensors and actuators on their surface and processing logic inside. But our new robots are different. Their sensors and actuators are distributed in the environment. Their processing is somewhere else. They’re a network of individual units that become a robot only in aggregate.

This turns our notion of security on its head. If massive, decentralized AIs run everything, then who controls those AIs matters a lot. It’s as if all the executive assistants or lawyers in an industry worked for the same agency. An AI that is both trusted and trustworthy will become a critical requirement.

This future requires us to see ourselves less as individuals, and more as parts of larger systems. It’s AI as nature, as Gaia—everything as one system. It’s a future more aligned with the Buddhist philosophy of interconnectedness than Western ideas of individuality. (And also with science fiction dystopias, like Skynet from the Terminator movies.) It will require a rethinking of much of our assumptions about governance and economy. That’s not going to happen soon, but in 2024 we will see the first steps along that path.

译文：

2016 年，我曾写过一篇关于互联网的文章，它以直接、物理的方式影响着世界。它与你的智能手机相连。它有传感器，如摄像头和恒温器。它有执行器：恒温器、无人机、自动驾驶汽车。它在中间有智能装置，利用传感器数据来计算该做什么，然后真正去做。

机器人的经典定义是能够感知、思考和行动的东西，这就是今天的互联网。我们在不知不觉中打造了一个世界级的机器人。

2023 年，我们利用 GPT 等大型语言模型（LLM）升级了 “思考 ”部分。ChatGPT 能够理解人类语言，并生成可信的、符合主题的、类似人类的回复，这让全世界都感到惊讶和惊喜。但是，它们真正擅长的是与以前为人类设计的系统进行交互。它们的准确性会越来越高，并将被用来取代真正的人类。

2024 年，我们将开始把这些 LLM 和其他人工智能系统与传感器和执行器连接起来。换句话说，它们将通过应用程序接口与更广阔的世界相连。它们将以我在 2016 年想到的所有形式，接收来自我们环境的直接输入。它们还将通过物联网设备和其他设备，越来越多地控制我们的环境。

这将从小事做起：总结电子邮件并撰写有限的回复。通过聊天与客服人员争论服务变更和退款事宜。进行旅行预订。

但这些人工智能还将与物理世界互动，首先控制机器人，然后让这些机器人成为它们的一部分。人工智能驱动的恒温器会根据房间里的人员、他们的喜好以及他们下一步可能去的地方打开暖气和空调。它还能通过安排高能耗电器或汽车充电的使用时间，与电力公司协商最便宜的电价。

这是简单的事情。当这些人工智能组合成一个更大的智能系统时，真正的变化才会发生：一个巨大的发电和用电网络，每个建筑只是一个节点，就像蚂蚁群落或人类军队一样。

未来的工业控制系统将包括传统的工厂机器人，以及安排其运行的人工智能系统。它将自动订购供应品，并协调最终产品的运输。人工智能将管理自己的财务，并与银行界的其他系统互动。它还会根据需要召唤人类：修理各个子系统，或者做一些对机器人来说过于专业的事情。

考虑一下无人驾驶汽车。当然，每辆车都有传感器，但它们也会利用嵌入在道路和电线杆上的传感器。真正的处理是在云端进行的，由一个中央系统来控制所有车辆。这样，每辆车都能协调行动，提高效率：例如，同步制动。

这些是机器人，但不是电影和电视中熟悉的那种。我们通常认为机器人是离散的金属物体，表面有传感器和执行器，内部有处理逻辑。但我们的新型机器人不同。它们的传感器和执行器分布在环境中。它们的处理过程在其他地方。它们是一个由单个单元组成的网络，只有聚集在一起才能成为一个机器人。

这颠覆了我们的安全概念。如果大规模、分散化的人工智能掌管一切，那么谁来控制这些人工智能就变得非常重要。这就好比一个行业的所有行政助理或律师都为同一家机构工作一样。一个既值得信任又值得信赖的人工智能将成为关键要求。

这种未来要求我们不再将自己视为个体，而更多地将自己视为更大系统的一部分。人工智能是大自然，是盖亚–万物是一个系统。与西方的个体观念相比，这种未来更符合佛教的互联哲学。(也符合科幻小说中的乌托邦，比如电影《终结者》中的天网）。这需要我们重新思考关于治理和经济的许多假设。这不会很快发生，但在 2024 年，我们将看到在这条道路上迈出的第一步。