认识（高级）人工智能

Self-driving cars have been part of the conversation around AI for decades and science fiction has fixed them in the popular imagination. Self-driving AI is known as autonomous driving and the cars are fitted with cameras, radar and range-sensing lasers.

几十年来，自动驾驶汽车一直是人工智能讨论的一部分，科幻小说已经将它们固定在大众的想象中。自动驾驶人工智能被称为自动驾驶，汽车配备了摄像头、雷达和距离感应激光器。

Think of a dragonfly, with 360-degree vision and sensors on its wings to help it manoeuvre and make constant in-flight adjustments. In a similar way, the AI model uses the data from its sensors to identify objects and figure out whether they are moving and, if so, what kind of moving object they are - another car, a bicycle, a pedestrian or something else. Thousands and thousands of hours of training to understand what good driving looks like has enabled AI to be able to make decisions and take action in the real world to drive the car and avoid collisions. Predictive algorithms may have struggled for many years to deal with the often unpredictable nature of human drivers, but driverless cars have now collected millions of miles of data on real roads. In San Francisco, they are already carrying paying passengers. 

想象一下蜻蜓，它具有 360 度视野，翅膀上有传感器，可以帮助它机动并在飞行中不断进行调整。以类似的方式，人工智能模型使用来自传感器的数据来识别物体，并确定它们是否在移动，如果是的话，它们是什么类型的移动物体——另一辆车、自行车、行人还是其他物体。为了了解什么是良好的驾驶，经过数千小时的训练，人工智能能够在现实世界中做出决策并采取行动来驾驶汽车并避免碰撞。多年来，预测算法可能一直在努力应对人类驾驶员的不可预测性，但无人驾驶汽车现在已经在真实道路上收集了数百万英里的数据。在旧金山，他们已经载着付费乘客。

Autonomous driving is also a very public example of how new technologies must overcome more than just technical hurdles. Government legislation and safety regulations, along with a deep sense of anxiety over what happens when we hand over control to machines, are all still potential roadblocks for a fully automated future on our roads.

自动驾驶也是一个非常公开的例子，说明新技术必须克服的不仅仅是技术障碍。政府立法和安全法规，以及对我们将控制权交给机器时会发生什么的深深焦虑感，仍然是我们道路上完全自动化的未来的潜在障碍。

Some AIs simply deal with numbers, collecting and combining them in volume to create a swarm of information, the products of which can be extremely valuable. There are likely already several profiles of your financial and social actions, particularly those online, which could be used to make predictions about your behaviour. Your supermarket loyalty card tracks your habits and tastes through your weekly shop. The credit agencies track how much you have in the bank and owe on your credit cards. Netflix and Amazon are keeping track of how many hours of content you streamed last night. Your social media accounts know how many videos you commented on today. And it’s not just you, these numbers exist for everyone, enabling AI models to churn through them looking for social trends. These AI models are already shaping your life, from helping decide if you can get a loan or mortgage, to influencing what you buy by choosing which ads you see online.

一些人工智能只是简单地处理数字，将它们大量收集和组合以创建大量信息，其产品可能非常有价值。可能已经有一些关于您的财务和社会行为的资料，尤其是在线的资料，可以用来预测您的行为。您的超市会员卡会跟踪您每周购物的习惯和品味。信贷机构会追踪您的银行账户余额和信用卡欠款金额。 Netflix 和 Amazon 会记录您昨晚流式传输内容的时长。您的社交媒体帐户知道您今天评论了多少个视频。不仅仅是你，每个人都存在这些数字，人工智能模型可以通过这些数字来寻找社会趋势。这些人工智能模型已经在塑造你的生活，从帮助你决定是否可以获得贷款或抵押贷款，到通过选择你在网上看到的广告来影响你的购买内容。

Would it be possible to combine some of these skills into a single, hybrid AI model? That is exactly what one of the most recent advances in AI does. It’s called multimodal AI and allows a model to look at different types of data - such as images, text, audio or video - and uncover new patterns between them. This multimodal approach was one of the reasons for the huge leap in ability shown by ChatGPT when its AI model was updated from GPT3.5, which was trained only on text, to GPT4, which was trained with images as well.

是否有可能将其中一些技能组合到一个单一的混合人工智能模型中？这正是人工智能最新进展之一所做的事情。它被称为多模式人工智能，允许模型查看不同类型的数据——例如图像、文本、音频或视频——并发现它们之间的新模式。这种多模态方法是 ChatGPT 的 AI 模型从仅基于文本训练的 GPT3.5 更新到也使用图像训练的 GPT4 时所表现出的巨大飞跃的原因之一。

The idea of a single AI model able to process any kind of data and therefore perform any task, from translating between languages to designing new drugs, is known as artificial general intelligence (AGI). For some, it’s the ultimate aim of all artificial intelligence research; for others, it’s a pathway to all those science fiction dystopias in which we unleash an intelligence so far beyond our understanding that we are no longer able to control it.

单一人工智能模型能够处理任何类型的数据，从而执行任何任务，从语言之间的翻译到设计新药物，这一想法被称为通用人工智能（AGI）。对一些人来说，这是所有人工智能研究的最终目标；对一些人来说，这是所有人工智能研究的最终目标。对于其他人来说，这是通向所有科幻反乌托邦的一条途径，在这些反乌托邦中，我们释放出一种远远超出我们理解的智能，以至于我们不再能够控制它。

Until recently the key process in training most AIs was known as "supervised learning". Huge sets of training data were given labels by humans and the AI was asked to figure out patterns in the data. The AI was then asked to apply these patterns to some new data and give feedback on its accuracy. For example, imagine giving an AI a dozen photos - six are labelled "car" and six are labelled "van".

直到最近，训练大多数人工智能的关键过程还被称为“监督学习”。人类给大量的训练数据贴上标签，并要求人工智能找出数据中的模式。然后，人工智能被要求将这些模式应用于一些新数据，并就其准确性提供反馈。例如，想象一下给人工智能一张照片——六张照片被标记为“汽车”，六张照片被标记为“货车”。

Next, tell the AI to work out a visual pattern that sorts the cars and the vans into two groups. Now what do you think happens when you ask it to categorise this photo below?

接下来，告诉人工智能制定一种视觉模式，将汽车和货车分为两组。现在，当您要求它对下面这张照片进行分类时，您认为会发生什么？

Unfortunately, it seems the AI thinks this is a van - not so intelligent. Now you show the AI this. It tells you this is a car. 

很遗憾，人工智能会认为这是一辆货车——不那么聪明。并且，如果你向人工智能展示以下这个图片，它确会告诉你这是一辆汽车。

It’s pretty clear what’s gone wrong. From the limited number of images it was trained with, the AI has decided colour is the strongest way to separate cars and vans. But the amazing thing about the AI program is that it came to this decision on its own - and we can help it refine its decision-making. We can tell it that it has wrongly identified the two new objects - this will force it to find a new pattern in the images. 

很清楚出了什么问题。从训练时使用的有限图像来看，人工智能认为颜色是区分汽车和货车的最强方法。但人工智能程序的惊人之处在于，它自己做出了这个决定——我们可以帮助它完善决策。我们可以告诉它，它错误地识别了两个新对象——这将迫使它在图像中找到新的模式。

But more importantly, we can correct the bias in our training data by giving it more varied images. These two simple actions taken together - and on a vast scale - are how most AI systems have been trained to make incredibly complex decisions. 

但更重要的是，我们可以通过提供更多样的图像来纠正训练数据中的偏差。大多数人工智能系统都是通过这两个简单的大规模行动一起进行训练来做出极其复杂的决策的。

Supervised learning is an incredibly powerful training method, but many recent breakthroughs in AI have been made possible by unsupervised learning. In the simplest terms, this is where the use of complex algorithms and huge datasets means the AI can learn without any human guidance. ChatGPT might be the most well-known example.

监督学习是一种非常强大的训练方法，但最近人工智能领域的许多突破都是通过无监督学习实现的。简而言之，复杂算法和庞大数据集的使用意味着人工智能可以在没有任何人类指导的情况下学习。 ChatGPT 可能是最著名的例子。

The amount of text on the internet and in digitised books is so vast that over many months ChatGPT was able to learn how to combine words in a meaningful way by itself, with humans then helping to fine-tune its responses. Imagine you had a big pile of books in a foreign language, maybe some of them with images. Eventually, you might work out that the same word appeared on a page whenever there was a drawing or photo of a tree, and another word when there was a photo of a house. And you would see that there was often a word near those words that might mean “a” or maybe “the” - and so on.

互联网和数字化书籍中的文本量如此之大，以至于 ChatGPT 能够在几个月的时间里学会如何以有意义的方式自行组合单词，然后由人类帮助微调其响应。想象一下，您有一大堆外语书籍，其中一些可能带有图像。最终，您可能会发现，每当有树的图画或照片时，页面上就会出现相同的单词，而当有房子的照片时，页面上就会出现另一个单词。你会发现这些词附近经常有一个词可能表示“a”或“the”——等等。

ChatGPT made this kind of close analysis of the relationship between words to build a huge statistical model which it can then use to make predictions and generate new sentences. It relies on enormous amounts of computing power which allows the AI to memorise vast amounts of words - alone, in groups, in sentences and across pages - and then read and compare how they are used over and over and over again in a fraction of a second.

ChatGPT 对单词之间的关系进行了这种仔细的分析，建立了一个巨大的统计模型，然后可以用它来进行预测并生成新的句子。它依赖于巨大的计算能力，使人工智能能够记住大量的单词——单独的、成组的、句子中的和跨页的——然后在很短的时间内一遍又一遍地阅读和比较它们的使用方式。

The rapid advances made by deep learning models in the last year have driven a wave of enthusiasm and also led to more public engagement with concerns over the future of artificial intelligence. There has been much discussion about the way biases in training data collected from the internet – such as racist, sexist and violent speech or narrow cultural perspectives - leads to artificial intelligence replicating human prejudices.

去年可以深度学习的人工智能凭借其快速进步引发了一股热点，也引发了更多公众对人工智能未来的担忧。关于从互联网收集的训练数据中的偏见（例如种族主义、性别歧视和暴力言论或狭隘的文化观点）如何导致人工智能复制人类偏见，已经有很多讨论。

Another worry is that artificial intelligence could be tasked to solve problems without fully considering the ethics or wider implications of its actions, creating new problems in the process. After a thought experiment by the philosopher Nick Bostrom, he imagined an artificial intelligence asked to create as many paperclips as possible which slowly diverts every natural resource on the planet to fulfil its mission – including killing humans to use as raw materials for more paperclips.

另一个担忧是，人工智能的任务可能是在没有充分考虑其行为的道德或其更广泛影响的情况下解决问题，从而在这个过程中产生新的问题。在哲学家尼克·博斯特罗姆（Nick Bostrom）进行思想实验后，他想象了一个人工智能，它被要求制造尽可能多的回形针，它会慢慢地转移地球上的每一种自然资源来完成其使命——包括杀死人类以用作更多回形针的原材料。

Others say that, rather than focusing on murderous AIs of the future, we should be more concerned with the immediate problem of how people could use existing AI tools to increase distrust in politics and scepticism of all forms of media. In particular, the world's eyes are on the 2024 presidential election in the US, to see how voters and political parties cope with a new level of sophisticated disinformation. What happens if social media is flooded with fake videos of presidential candidates, created with AI and each tailored to anger a different group of voters?

其他人则表示，我们不应该关注未来凶残的人工智能，而应该更关注眼前的问题，即人们如何利用现有的人工智能工具来增加对政治的不信任和对所有形式媒体的怀疑。尤其是，全世界的目光都在关注 2024 年美国总统选举，看看选民和政党如何应对新水平的复杂虚假信息。如果社交媒体充斥着由人工智能制作的总统候选人的虚假视频，并且每个视频都是为了激怒不同的选民群体而定制的，会发生什么？

In Europe, the EU is creating an Artificial Intelligence Act to protect its citizens' rights by regulating the deployment of AI – for instance, a ban on using facial recognition to track or identify people in real-time in public spaces. These are among the first laws in the world to establish guidelines for the future use of these technologies – setting boundaries on what companies and governments will and will not be allowed to do – but, as the capabilities of artificial intelligence continue to grow, they are unlikely to be the last.

在欧洲，欧盟正在制定一项人工智能法案，通过规范人工智能的部署来保护其公民的权利——例如，禁止在公共场所使用面部识别来实时跟踪或识别人员。这些是世界上首批为这些技术的未来使用制定指导方针的法律之一，为公司和政府可以做什么和不可以做什么设定界限，但是，随着人工智能能力的不断增长，它们不太可能是最后一个。