简易AI指南-AI如何生成新图像和开车-慢速及文稿

Can AI understand images?
AI能理解图像吗？


Has your phone ever gathered your photos into folders with names like "at the beach" or "nights out"?
您的手机是否曾经将照片收集到名为“在海滩”或“夜晚外出”等名称的文件夹中？
Then you’ve been using AI without realising. An AI algorithm uncovered patterns in your photos and grouped them for you. 
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。
These programs have been trained by looking through a mountain of images, all labelled with a simple description.
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。
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.
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.
有时，人工智能经过训练可以发现相似图像中的微小差异。
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.
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特且唯一。
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 make a decision on just one.
同样类型的算法已经进行了医学扫描的训练，可以识别危及生命的肿瘤，可以在专家顾问仅对一次扫描做出决定的时间内，完成数千次扫描的判断。


How does AI make new images?
AI如何生成新图像？


Recently image recognition has been adapted into AI models which have learned the chameleon-like power of manipulating patterns and colours.
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变色龙般操纵图案和颜色变化的能力。
These image-generating AIs can turn the complex visual patterns they gather from millions of photographs and drawings into completely new images.
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。
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.
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。
Or you can creatively direct the style of an image: "Make a portrait of the England football manager, painted in the style of Picasso."
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.
最新的人工智能在生成新图像时，先使用一组随机颜色的像素。
It looks at the random dots for any hint of a pattern it learned during training - patterns for building different objects.
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。
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.
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。
Develop all the necessary patterns like "Mars surface", "astronaut" and "walking" together and you have a new image.
当你将一组需要的图案都发展并叠加，如“火星表面”、“宇航员”和“行走”，你就得到了一个新的图象。
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.
由于新图像是由随机像素层构建的，因此其产生的最终画面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。
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.
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的艺术品，等道德问题意味着什么。


What about self-driving cars?
那么自动驾驶汽车呢？


Self-driving cars have been part of the conversation around AI for decades and science fiction has fixed them in the popular imagination.
几十年来，自动驾驶汽车一直是人工智能讨论的一部分，而科幻小说已经将它们牢牢根植与大众想象中。
Self-driving AI is known as autonomous driving and the cars are fitted with cameras, radar and range-sensing lasers.
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.

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。

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.

以一直类似的方式，人工智能模型使用来自传感器的数据来识别物体，并弄清楚它们是否在移动，如果是，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。

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.

为了了解什么是优秀的驾驶，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。

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.

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.

自动驾驶也是一个非常明显公开的例子，说明新技术必须克服的不仅仅是技术障碍。

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.

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。