市面上有很多全球最大的博彩平台“什么是人工智能”的文章，这里还有一篇. As an AI company it’s almost an obligation for us at Fuzzy Labs 写一个.
The History Stuff
Research into AI goes all the way back to the 1950s. It’s an understatement to say computing power was limited, nevertheless some important foundations were laid:
- Automated Reasoning: humans are good at reasoning. 我们一直在做这样的事情，比如在道路封闭时想办法回家.
- Natural Language Processing: the dream is computers that understand human language. Imagine asking a computer for directions to Manchester, 计算机通过自动推理找出最佳路线——这是1950年的科幻小说, but quite normal today.
- 机器人: AI-driven smartphones and smart homes were a distant fantasy. Humanoid robots were the future! 机器人需要能看、能说、能听，由此开启了计算机视觉和语音识别的研究.
The First Winter — 1974 to 1980
在探索智能机器的过程中，从真正的大脑中寻找灵感是有意义的. Brains are made of big interwoven networks of neurons (nerve cells), so if we simulate these neural networks, we’ll have a thinking computer, just like that!
A great idea but harder than it seemed. 1969年，马文·明斯基(Marvin Minsky)出版了一本书，指出了一种叫做感知器的特殊神经模型的一些基本局限性. It was a huge blow for AI research. By 1974 optimism for AI had worn out and the funding disappeared.
The Second Winter — 1987 to 1993
在20世纪80年代，计算机从房间大小的庞然大物变成了方便的桌面大小的机器. AI was cool again and saw a number of applications, 从在视频游戏中使用它来提供具有挑战性的虚拟对手到采用专家系统来自动化各种流程的企业.
这波新的人工智能研究浪潮主要是由企业推动的，到1993年，资金再次枯竭.
Semi-modern — 1993 to 2010
记得垃圾邮件? In the 90s and early 2000s it drove us all mad. 现在这不是问题了，因为有了贝叶斯分类器. 这种垃圾邮件检测从人类垃圾邮件报告中学习垃圾邮件的样子.
Neural networks gained popularity again. 这些被认为是模仿构成我们大脑的神经细胞网络, and like our brains, neural networks can learn to recognise patterns. Pictures of cats, for instance.
There was renewed interest in optimisation problems. 假设您是亚马逊，希望使用最少的货车运送尽可能多的包裹, 或者你雇佣轮班工人，你想以最低的成本完成所有的轮班. 对于这些问题，找到完美的解决方案往往是不可行的, but we might come up with good enough solutions.
The present day
AI is everywhere. 当你预订航班时，你的收件箱会自动对邮件进行分类，并填充你的日历, your photos app understands what’s in your photos, airports use face recognition to check your passport.
今天人工智能应用的激增很大程度上是因为拥有了强大的计算机, including mobile devices, but it’s also due to the popularity of cloud computing.
云的人工智能
Imagine you want to identify a dog’s breed from a photo. 你首先收集成千上万张照片(认识新狗的好方法)，然后手动给这些照片贴上正确的品种标签. 接下来，你把这些数据输入一个神秘的人工智能盒子，它会产生一个被称为模型的东西.
这个模型是你辛苦收集和标记数据所得到的奖励. That model can identify new dogs which it wasn’t trained on.
培训 a model to identify dog breeds
Cute, but what’s this got to do with cloud computing?
Google Photos can organise your photos for you. 它知道照片是在户外拍的，里面是人还是动物. 这是因为谷歌花了数年时间收集和标记大量的照片，他们用这些照片来训练模型.
所有主要的云提供商都为图像分类提供了预训练的人工智能模型, text analysis and a lot more. 谷歌照片使用的相同模型可以通过谷歌云获得.
云人工智能代表了在产品中拥有成熟人工智能功能的捷径. 使用其他人投入到预训练模型中的工作可以节省大量的时间和金钱.
Exotic neural networks
A lot has happened with neural networks since the 50s. Perhaps you’ve heard the term deep learning. 这通常意味着使用非常大的神经网络和非常大的训练数据集.
另一个经常出现的术语是卷积神经网络. This is a specialised neural network, designed after our own visual cortex, 这在自动驾驶汽车或狗狗识别等视觉应用中很受欢迎.
Ethical considerations
随着人工智能开始以重大方式影响人们的生活，公共话语开始强调道德.
如果你申请了一份工作，发现人工智能负责筛选申请, would you trust it? 几年前，亚马逊在工程职位上试用了这个想法，结果是 bias against women. 人工智能是根据人类做出的历史决策进行训练的，因此计算机采用了人类的偏见.
当人工智能做出影响人类的决定时，考虑哪些偏见可能会影响它是至关重要的.
Organisations like AI for Good UK work to make industry more ethical. In parallel there’s efforts to use AI to do good. Many companies involved in AI have an AI for Good initiative, including our own.
未来
Predicting the future of AI is hard. We’re in an AI boom which may not last forever, but even when the hype lulls we think AI is here to stay. It’s intertwined with the technology we use on a daily basis.
随着计算能力的提高，我们将看到更复杂的人工智能模型和基于云的模型 AI-as-a-service offerings will make these models accessible to a wide audience.
Edge computing is leading to new application areas. Edge的意思是“不在云端”，但它并不像听起来那么傻. 你可能想在云端训练一个模型，那里有很多计算能力，并将该模型部署到功率和带宽有限的设备上. Think about smart cameras monitoring factory equipment.
Another one to keep an eye on is explainable AI. 这是指当人工智能做出决定时，它应该能够解释自己. Right now the models we train give us an answer, like ‘that dog is a Border Collie’, but they can’t explain why. 可解释的人工智能解决了一些道德问题，也使我们更容易调试和改进我们的模型.
But really, what is AI?
奇怪的是，一旦一个研究想法变成了一项有用的技术, it’s no longer thought of by the general public as being AI. 例如，垃圾邮件过滤感觉很简单，而“人工智能”听起来很复杂.
Perhaps what distinguishes AI is the ability to learn and generalise? Ah, but the AI was still programmed to learn. Ultimately it’s just software.
