认识（基础）人工智能

The key to all machine learning is a process called training, where a computer program is given a large amount of data - sometimes with labels explaining what the data is - and a set of instructions. The instruction might be something like: "Find all the images containing faces" or, "Categorise these sounds". The program will then search for patterns in the data it has been given to achieve these goals. It might need some nudging along the way - such as "that’s not a face" or "those two sounds are different" - but what the program learns from the data and the clues it is given becomes the AI model - and the training material ends up defining its abilities.

所有机器学习的关键是一个称为训练的过程，其中向计算机程序提供大量数据（有时带有解释数据是什么的标签）和一组指令。该指令可能类似于：“查找所有包含面孔的图像”或“对这些声音进行分类”。然后，该程序将在所提供的数据中搜索模式以实现这些目标。在此过程中可能需要一些提示 - 例如“这不是一张脸”或“这两个声音不同” - 但程序从数据中学习的内容以及给出的线索将成为人工智能模型 - 培训材料结束定义其能力。

One way to look at how this training process could create different types of AI is to think about different animals. Over millions of years, the natural environment has led to animals developing specific abilities, similarly, the millions of cycles an AI makes through its training data will shape the way it develops and lead to specialist AI models.

了解这种训练过程如何创造不同类型人工智能的一种方法是考虑不同的动物。数百万年来，自然环境导致动物发展出特定的能力，同样，人工智能通过其训练数据进行的数百万次循环将塑造其发展方式并导致专业的人工智能模型。

Think of a chatbot as a bit like a parrot. It’s a mimic and can repeat words it has heard with some understanding of their context but without a full sense of their meaning. Chatbots do the same - though on a more sophisticated level - and are on the verge of changing our relationship with the written word.

将聊天机器人想象成一只鹦鹉。它是一个模仿者，可以重复听到的单词，但对上下文有一定的了解，但不能完全理解其含义。聊天机器人也做同样的事情——尽管在更复杂的层面上——并且即将改变我们与书面文字的关系。

But how do these chatbots know how to write? They are a type of AI known as large language models (LLMs) and are trained with huge volumes of text. An LLM can consider not just individual words but whole sentences and compare the use of words and phrases in a passage to other examples across all of its training data. Using these billions of comparisons between words and phrases it can read a question and generate an answer - like predictive text messaging on your phone but on a massive scale. The amazing thing about large language models is they can learn the rules of grammar and how to use words in the correct context, without human assistance.

但这些聊天机器人如何知道如何写作呢？它们是一种被称为大语言模型（LLM）的人工智能，并接受大量文本的训练。法学硕士不仅可以考虑单个单词，还可以考虑整个句子，并将段落中单词和短语的使用与所有训练数据中的其他示例进行比较。通过对单词和短语之间数十亿次的比较，它可以读取问题并生成答案 - 就像手机上的预测短信一样，但规模很大。大型语言模型的惊人之处在于它们可以学习语法规则以及如何在正确的上下文中使用单词，而无需人工帮助。

If you've used Alexa, Siri or any other type of voice recognition system, then you've been using AI. Imagine a rabbit with its big ears, adapted to capture tiny variations in sound. The AI records the sounds as you speak, removes the background noise, separates your speech into phonetic units - the individual sounds that make up a spoken word - and then matches them to a library of language sounds. Your speech is then turned into text where any listening errors can be corrected before a response is given.

如果您使用过 Alexa、Siri 或任何其他类型的语音识别系统，那么您就一直在使用人工智能。想象一下一只兔子，它有一双大耳朵，可以捕捉声音的微小变化。人工智能会记录您说话时的声音，消除背景噪音，将您的语音分成语音单元（构成口语单词的各个声音），然后将它们与语言声音库进行匹配。然后，您的演讲将转换为文本，在给出答复之前可以纠正任何听力错误。

This type of artificial intelligence is known as natural language processing. It is the technology behind everything from you saying "yes" to confirm a phone banking transaction, to asking your mobile phone to tell you about the weather for the next few days in a city you are travelling to.

这种类型的人工智能被称为自然语言处理。从您说“是”来确认电话银行交易，到要求您的手机告诉您要去的城市未来几天的天气，一切背后的技术都是由它支撑的。

Has your phone ever gathered your photos into folders with names like "at the beach" or "nights out"? Then you’ve been using AI without realising. An AI algorithm uncovered patterns in your photos and grouped them for you. These programs have been trained by looking through a mountain of images, all labelled with a simple description. If you give an image-recognition AI enough images labelled "bicycle", eventually it will start to work out what a bicycle looks like and how it is different from a boat or a car.

您的手机是否曾将照片收集到名为“在海滩”或“夜晚外出”等名称的文件夹中？那么你就已经在不知不觉中使用人工智能了。人工智能算法会发现您照片中的模式并为您进行分组。这些程序是通过查看大量图像进行训练的，所有图像都标有简单的描述。如果你给图像识别人工智能足够多的标记为“自行车”的图像，最终它会开始弄清楚自行车的样子以及它与船或汽车的不同之处。

Sometimes the AI is trained to uncover tiny differences within similar images. This is how facial recognition works, finding a subtle relationship between features on your face that make it distinct and unique when compared to every other face on the planet. The same kind of algorithms have been trained with medical scans to identify life-threatening tumours and can work through thousands of scans in the time it would take a consultant to decide on just one.

有时，人工智能经过训练可以发现相似图像中的微小差异。这就是面部识别的工作原理，找到你脸上特征之间的微妙关系，使其与地球上的其他面孔相比显得独一无二。同样类型的算法已经通过医学扫描进行了训练，可以识别危及生命的肿瘤，并且可以在顾问仅决定一次扫描的时间内完成数千次扫描。

Recently image recognition has been adapted into AI models which have learned the chameleon-like power of manipulating patterns and colours. These image-generating AIs can turn the complex visual patterns they gather from millions of photographs and drawings into completely new images. You can ask the AI to create a photographic image of something that never happened - for example, a photo of a person walking on the surface of Mars. Or you can creatively direct the style of an image: "Make a portrait of the England football manager, painted in the style of Picasso."

最近，图像识别已被应用到人工智能模型中，这些模型已经学会了操纵图案和颜色的变色龙般的能力。这些图像生成人工智能可以将从数百万张照片和图画中收集的复杂视觉模式转变为全新的图像。您可以要求人工智能创建从未发生过的事情的照片图像 - 例如，一个人在火星表面行走的照片。或者，您可以创造性地指导图像的风格：“以毕加索的风格绘制英格兰足球经理的肖像。

The latest AIs start the process of generating this new image with a collection of randomly coloured pixels. It looks at the random dots for any hint of a pattern it learned during training - patterns for building different objects. These patterns are slowly enhanced by adding further layers of random dots, keeping dots which develop the pattern and discarding others, until finally a likeness emerges. Develop all the necessary patterns like "Mars surface", "astronaut" and "walking" together and you have a new image.

最新的人工智能开始使用一组随机颜色的像素生成新图像。它会查看随机点，寻找在训练期间学到的模式的任何提示——构建不同对象的模式。通过添加更多层随机点，保留形成图案的点并丢弃其他点，这些图案会慢慢增强，直到最终出现相似之处。一起开发所有必要的图案，如“火星表面”、“宇航员”和“行走”，你就有了一个新的形象。

Because the new image is built from layers of random pixels, the result is something which has never existed before but is still based on the billions of patterns it learned from the original training images. Society is now beginning to grapple with what this means for things like copyright and the ethics of creating artworks trained on the hard work of real artists, designers and photographers.

由于新图像是由随机像素层构建的，因此其结果是以前从未存在过的，但仍然基于从原始训练图像中学到的数十亿种模式。社会现在开始努力解决这对版权和以真正的艺术家、设计师和摄影师的辛勤工作为基础创作艺术品。