Artificial Intelligence is an umbrella term used to describe a rapidly evolving, highly competitive technological field. It is often used erroneously and has come to define so many different approaches that even some experts are not able to define, in plain terms, exactly what artificial intelligence is. This makes the rapidly growing field of AI tricky to navigate and even more difficult to regulate properly.

人工智能是一个笼统的术语，用于描述快速发展，竞争激烈的技术领域。 它经常被错误地使用，并且已经定义了许多不同的方法，甚至一些专家也无法简单地定义什么是人工智能。 这使得Swift发展的AI领域难以驾驭，甚至难以正确调节。

The point of regulation should be to protect people from physical, mental, environmental, social, or financial harm caused by the actions or negligence of others. (Some may add requirements like “fairness” or “transparency”, or expand the protection to animals, plants, institutions, historic landmarks, etc. For this article, let’s stick to the general point described above). Regulation doesn’t guarantee that accidents won’t happen. But, if something were to go wrong, there has to be a fix. This requires both explainability (to know why the error occurred) and determinism (to assure the fix works every time) in the solution.

监管的重点应该是保护人们免受他人的行为或疏忽造成的身体，精神，环境，社会或经济伤害。 (有些人可能会添加诸如“公平”或“透明度”之类的要求，或者将保护范围扩大到动植物，机构，历史地标等。对于本文，让我们坚持上述一般要点)。 法规并不能保证不会发生事故。 但是，如果出现问题，必须进行修复。 这既需要解决方案的可解释性(要知道为什么会发生错误)，也需要确定性(以确保每次都能修复)。

Imagine if someone asked, “When should we start putting regulations on computer software?” That’s not a very precise question. What kind of software? Video games? Spread sheets? Malware? Similarly, artificial intelligence can be implemented in many ways. It’s important to distinguish types and use cases. Below, some basic types are briefly described.

假设有人问：“我们什么时候应该开始对计算机软件制定法规？” 这不是一个非常精确的问题。 什么样的软件？ 视频游戏？ 分页了吗？ 恶意软件？ 同样，人工智能可以通过多种方式实现。 区分类型和用例很重要。 下面，简要描述一些基本类型。

1. Automation — A lot of what is called “AI” today is simply what was called “automation” a decade ago. Someone notices a pattern in their daily work with some variables, and writes up a program to repeat that work for them. There’s no learning by the program. The “intelligence” is provided by the developer when coding. Occasionally, some patterns change or new variables appear requiring the developer to update the code. If you’ve ever created a “macro” (or cleverly used redstone in Minecraft), then you’ve automated your work.

1. 自动化 -如今，许多所谓的“ AI”就是十年前的“自动化”。 有人在日常工作中注意到带有一些变量的模式，并编写了一个程序来为他们重复这项工作。 该程序没有学习。 开发人员在编码时提供“情报”。 有时，某些模式会更改或出现新的变量，要求开发人员更新代码。 如果您曾经创建过一个“宏”(或者在Minecraft中巧妙地使用了红石)，那么您就可以使工作自动化。

2. Modeling — A step more sophisticated than mere automation, modeling requires a developer to understand the problem enough to consider edge cases, variables, and patterns not yet seen. The better the model, the more of the possibility space is covered without going overboard to handle cases that will never be encountered. Models are also not capable of learning. Again, the “intelligence” is provided by the developer. Models are static and require manual effort to improve over time. These work best in deterministic, well-defined problem domains where information is fully known, such as chess. All the rules are clearly understood. Pieces move in exactly specified ways. Everyone sees the full board — nothing is hidden. The variable is the opponent’s choices, but they are restricted to a finite set of possibilities. Brute-force methods that test all possibilities before selecting, or search algorithms (e.g. A*, pronounced “A star”) that reduce the number of possibilities to test, find optimal game play. People have also applied models to non-deterministic (i.e. stochastic) problems that also don’t provide all the information. This is what the weather service does when telling you that there is an 80% chance of rain tomorrow. There’s no way for them to know where every molecule is, or their velocity, yet there are multiple “weather models” that provide a reasonable accounting of the possibilities. Notice that the results are returned as probabilities instead of absolutes like in chess. Similarly, quants (“quantitative analysts”) create models to predict the stock market.

2. 建模 - 建模比单纯的自动化要复杂得多，它要求开发人员充分了解问题，以考虑尚未发现的极端情况，变量和模式。 模型越好，在不过度处理无法遇到的案例的情况下，覆盖的可能性空间就越大。 模型也无法学习。 同样，“情报”由开发商提供。 模型是静态的，需要人工来逐步改进。 这些方法在确定性的，定义明确的问题域中效果最好，在这些域中信息是众所周知的，例如国际象棋。 所有规则都清楚理解。 作品以确切指定的方式移动。 每个人都看到了整个局面-没有任何隐藏的东西。 变量是对手的选择，但仅限于有限的可能性集。 在选择之前测试所有可能性的蛮力方法，或搜索算法(例如A *，发音为“ A star”)以减少测试可能性的数量，从而找到最佳的游戏玩法。 人们也将模型应用于不确定的(即随机的)问题，这些问题也无法提供所有信息。 这是气象服务部门在告诉您明天明天下雨的机会为80％时所做的事情。 他们无法知道每个分子的位置或速度，但是有多个“天气模型”可以合理地解释各种可能性。 请注意，结果以概率形式返回，而不是像象棋那样以绝对值形式返回。 同样，量化指标(“量化分析师”)会创建模型来预测股市。

3. Machine Learning — Providing the software a way to change its internal models starts to remove the human from some parts of the solution. Essentially, data becomes the majority of the system’s programming. Humans, however, still create machine learning models, select what they believe is relevant data to use for training, iterate and interpret results until the answers fit the developer’s belief of what a good answer should look like. All this introduces human bias into the solution. Examples of machine learning techniques include “deep learning”, “convolutional neural networks”, “support vector machines”, and “random forests”. These solutions address problem domains that are stochastic in nature, and/or have missing or hidden information. Games of chance, use cases like stock market predictions, actuarial sciences, or complicated “big data” all good candidates for machine learning techniques.

3. 机器学习 -为软件提供一种更改其内部模型的方法，这开始使解决方案中某些部分的人员不再需要。 本质上，数据成为系统编程的主要内容。 但是，人类仍然会创建机器学习模型，选择他们认为相关的数据用于训练，迭代和解释结果，直到答案符合开发人员对好的答案应该是什么样的信念为止。 所有这些都将人为的偏见引入了解决方案。 机器学习技术的示例包括“深度学习”，“卷积神经网络”，“支持向量机”和“随机森林”。 这些解决方案解决了本质上是随机的和/或信息丢失或隐藏的问题域。 机会游戏，诸如股票市场预测之类的用例，精算科学或复杂的“大数据”都是机器学习技术的良好候选人。

4. Artificial General Intelligence (AGI) — This is the holy grail of, well, everything. Building machines that learn and think generally and can adapt to environmental changes on their own without human involvement is the endgame of computing, to pick just one field. There may seem to be a large gap from 3 to 4, but the reality is that all the other solutions are just variations of what already exist.

4. 人工智能 (AGI)-这是一切的圣杯。 仅学习一个领域，构建具有一般学习能力和思维能力并且能够自行适应环境变化而无需人类参与的机器就是计算的最终目标。 从3到4似乎有很大的差距，但是现实是所有其他解决方案只是已经存在的解决方案的变体。

Let’s consider what regulation would mean for each type. Keep in mind that regulation requires people to review and understand what is happening between the inputs and outputs of a system enough to assure the solution can do no harm. Regulation is built on trust. We trust that the regulators both understand and are competent in doing their jobs. We can’t automate regulation, otherwise we are stuck in an endless loop to regulate the software that regulates software that regulates software…see? Regulation requires humans, trust, and competence.

让我们考虑一下每种类型的法规意味着什么。 请记住，法规要求人们检查并了解系统的输入和输出之间发生的情况，以确保解决方案不会造成任何损害。 监管建立在信任之上。 我们相信，监管者既能够理解又能胜任其工作。 我们不能自动执行调节，否则我们陷入了无休止的循环来调节软件，而调节软件又可以调节软件……看吗？ 监管需要人类，信任和能力。

Regulations currently exist for the first two types of AI. The third and fourth types become tricky.

当前存在针对前两种AI的法规。 第三和第四类型变得棘手。

Regulating Automation — These programs rely exclusively on the work of people. These types of programs are already regulated in industries and critical applications. For example, the autopilot software on an aircraft must pass stringent certifications, as do many medical devices. Regulation can consist of auditing the software to uncover malware, bugs, or deficiencies. Basically, this aggregates the responsibilities of one person (the developer) with another (the independent auditor). The assumption is that a second person’s check will catch any irregularities or issues. (This is a trivialized view of what actually occurs. Ideally, there are multiple testing teams at different stages of development and deployment). Regulation works here because humans can understand these systems.

调节自动化 -这些程序完全依靠人的工作。 这些类型的程序已经在行业和关键应用中受到监管。 例如，飞机上的自动驾驶软件必须像许多医疗设备一样通过严格的认证。 监管可以包括对软件进行审核以发现恶意软件，错误或缺陷。 基本上，这将一个人(开发人员)与另一个人(独立审计师)的职责汇总在一起。 假设第二个人的支票会发现任何不合规定或问题。 (这是实际情况的简单视图。理想情况下，有多个测试团队处于不同的开发和部署阶段)。 监管之所以在这里起作用，是因为人们可以理解这些系统。

Regulating Modeling — These programs also rely exclusively on the work of people. Therefore, they lend themselves well to regulation. Modeling is a step more sophisticated than automation, so this does get trickier. But, it is still within the realm of trustworthy people competently executing their regulatory duties. The financial models used by banks, for example, are very highly regulated to ensure they work without bias. Modelers must prove to regulators that their models don’t discriminate, say, loan provisions based on ethnicity.

规范建模 -这些程序还完全依赖于人员的工作。 因此，它们很适合监管。 建模比自动化要复杂得多，因此这确实变得棘手。 但是，它仍然属于可信赖的人，他们有能力执行其监管职责。 例如，银行使用的财务模型受到严格的监管，以确保它们运作时不会产生偏差。 建模者必须向监管机构证明，他们的模型不会歧视基于种族的贷款条款。

Regulating Machine Learning — Since these techniques are implemented specifically to tackle problems that are too difficult for mere mortals to understand, regulating them requires something different from the prior two types. Regardless of how trustworthy and competent the regulators, they won’t fully understand the internals of the majority of these machine learning solutions. At least not for any interesting, real-world problems. Definitely not for any non-deterministic solutions. Even treating a specific technique as an understandable model ignores the behavior of that model under load from unvalidated data. Perhaps regulation means that all training data must be verified and validated prior to digestion by the algorithm? This negates the point of doing machine learning in the first place. An example of where regulation is needed for this type of AI is in autonomous vehicles. A suggestion of evidence based results to determine safety is mentioned in a January 2020 article by AP News regarding multiple Tesla crashes where, the executive director of the Center for Auto Safety in Washington said,

调节机器学习 -由于这些技术是专门为解决凡人难以理解的问题而实施的，因此对它们的调节需要与前两种类型有所不同。 无论监管机构如何值得信赖和胜任，他们都不会完全理解大多数这些机器学习解决方案的内部。 至少不是针对任何有趣的现实问题。 绝对不是任何不确定性解决方案。 即使将特定技术视为可理解的模型，也忽略了未验证数据在负载下该模型的行为。 也许法规意味着必须在算法消化之前对所有训练数据进行验证和确认？ 首先，这否定了进行机器学习的目的。 在自动驾驶汽车中，需要针对此类AI进行监管的示例。 华盛顿汽车安全中心执行主任在2020年1月的AP News文章中提到了关于基于证据的结果来确定安全性的建议，其中涉及多起特斯拉车祸。

“At some point, the question becomes: How much evidence is needed to determine that the way this technology is being used is unsafe? In this instance, hopefully these tragedies will not be in vain and will lead to something more than an investigation by NHTSA.”

“在某个时候，问题就变成了：需要多少证据来确定该技术的使用方式是不安全的？ 在这种情况下，希望这些悲剧不会白费，只会带来更多的事情，而不仅仅是美国国家公路交通安全管理局的调查。”

The article goes on to say:

文章继续说：

“Levine and others have called on the agency to require Tesla to limit the use of Autopilot to mainly four-lane divided highways without cross traffic. They also want Tesla to install a better system to monitor drivers to make sure they’re paying attention all the time. Tesla’s system requires drivers to place their hands on the steering wheel. But federal investigators have found that this system lets drivers zone out for too long.”

莱文和其他人呼吁该机构要求特斯拉将自动驾驶仪的使用范围限制在主要四车道分开的高速公路，且不得交叉行驶。 他们还希望特斯拉安装一个更好的系统来监视驱动程序，以确保他们一直在关注。 特斯拉的系统要求驾驶员将手放在方向盘上。 但是联邦调查人员发现，该系统使驾驶员的区域划分时间过长。”

3 crashes, 3 deaths raise questions about Tesla’s Autopilot

3起撞车，3人死亡引发了有关特斯拉自动驾驶仪的疑问

That’s an appropriate measure for regulating stochastic machine learning systems. It’s less about the known limitations of the software, and more about the way it ought to be -and not be- used.

这是调节随机机器学习系统的适当措施。 它与软件的已知局限性无关，而与软件应被使用和不被使用的方式有关。

Alternative deterministic and explainable machine learning algorithms exist. These offer themselves better to regulations. If regulatory laws are required on the software for specific use cases, then the solutions must be implemented using these fully explainable technologies. These are absolutely necessary for mission-critical applications that attempt to replace type one AIs in industries that are already highly regulated.

存在替代的确定性和可解释的机器学习算法。 这些使自己更好地符合法规。 如果针对特定用例需要软件上的法规，则必须使用这些完全可解释的技术来实施解决方案。 对于试图替换已经受到严格监管的行业中的一类AI的任务关键型应用程序，这些绝对是必需的。

Regulating Artificial General Intelligence — For there to be any chance of regulating AGI solutions, the components of that solution must be completely deterministic and explainable. Compared to the others, this one would seem to be the most challenging in terms of regulations. But, consider the goal: It is ultimately intended that these systems work like human minds. At that point, regulations would revert to regular laws to which we hold people accountable. But, there won’t be a light-switch moment. Before reaching full human-level intelligence, these systems will first evolve through much more humble abilities. They may progress through the equivalence of snail, mouse, squirrel, dog, and monkey minds. If allowed to apply their decisions, it is necessary that some randomness enters the algorithm. This is simply due to decision science, and not a feature of the AI/AGI. Deterministic and stochastic pathways can be separable, therefore regulated independently.

调节人工智能 -为了有可能调节AGI解决方案，该解决方案的组件必须是完全确定性和可解释的。 与其他法规相比，就法规而言，这似乎是最具挑战性的。 但是，请考虑一下目标：最终希望这些系统像人类的思想一样工作。 届时，法规将恢复为我们要追究人民责任的常规法律。 但是，不会有电灯开关的时刻。 在达到完整的人类智能之前，这些系统将首先通过更谦卑的能力发展。 它们可能会通过蜗牛，老鼠，松鼠，狗和猴子的思想等同而发展。 如果允许应用他们的决策，则必须将一些随机性输入算法。 这仅仅是由于决策科学，而不是AI / AGI的功能。 确定性和随机路径可以是可分离的，因此可以独立调节。

Regulations for AI systems already exist. This trend will continue. Unfortunately, our society tends to be reactive instead of proactive. It is likely that regulations will only be implemented after harmful events occur. When these tragedies occur, we shouldn’t rush to blame the technology or developer, solely. Users that misuse the technology and lawmakers that fail to educate themselves on the technology must also shoulder the blame.

人工智能系统的法规已经存在。 这种趋势将继续下去。 不幸的是，我们的社会倾向于被动而不是主动。 法规可能仅在有害事件发生后才能实施。 当这些悲剧发生时，我们不应该仅仅责怪技术或开发人员。 滥用技术的用户和未能对技术进行自我教育的立法者也必须承担责任。

A driver that doesn’t follow the Tesla Autopilot instructions of staying awake, keeping hands on the steering wheel, or operating it only on highways is using the system outside of its design. If an accident occurs, that driver ought to be held responsible. It’s not enough to claim ignorance of the limitations and blame the engineers. Nor are the Tesla marketers absque culpa for naming the system “Autopilot”, giving consumers an overhyped sense of functionality. It is within these very human deceptions, self-made or as active participants, that lawmakers can impose controls. They must, however, be willing to work hard in understanding the technology so that they can delineate where the technology’s limitations end and human limitations begin.

不遵循特斯拉自动驾驶仪的指示保持清醒，把手放在方向盘上或仅在高速公路上操作的驾驶员正在使用其设计之外的系统。 如果发生事故，该驾驶员应承担责任。 仅仅声称对限制的无知并责怪工程师是不够的。 特斯拉营销人员也不是将系统命名为“ Autopilot”的罪魁祸首，从而给消费者带来了过度的功能感。 立法者可以在这些非常人性的欺骗中，无论是自制的还是作为积极参与者，都可以施加控制。 但是，他们必须愿意努力理解技术，以便他们能够描述技术局限性从何而来和人为局限性开始的地方。