There are a lot of ‘what is AI’ articles out there, and here’s one more. 作为一家人工智能公司，这几乎是我们的义务 Fuzzy Labs 写一个.
历史的东西
对人工智能的研究可以追溯到20世纪50年代. 说计算能力有限是一种保守的说法, 尽管如此，还是打下了一些重要的基础:
- 自动推理人类擅长推理. We do it all the time, things like figuring out how to get home when a road is closed.
- 自然语言处理我们的梦想是计算机能够理解人类语言. 想象一下，向电脑询问去十大网博靠谱平台的路, and the computer doing some automated reasoning to figure out the best route — science fiction in 1950, 但是今天很正常.
- Robotics: 人工智能驱动的智能手机和智能家居是一个遥远的幻想. 人形机器人是未来的趋势! a robot needs to see, speak and hear, which launched work on computer vision and speech recognition.
第一个冬天——1974年到1980年
In the quest for intelligent machines it made sense to look to real brains for inspiration. 大脑是由神经元(神经细胞)相互交织的大网络组成的。, 如果我们模拟这些神经网络, 我们将拥有一台会思考的电脑, 就像这样!
这是个好主意，但比看起来要难. In 1969 Marvin Minsky published a book that pointed out some fundamental limitations in a particular kind of neural model called the perceptron. 这对人工智能研究来说是一个巨大的打击. 到1974年，对人工智能的乐观情绪已经消退，资金也消失了.
第二个冬天——1987年到1993年
In the 1980s computers transformed from vast room-sized monoliths to convenient desktop-sized machines. 人工智能又酷了起来，出现了很多应用, from its use in video games to provide challenging virtual opponents to businesses adopting expert systems to automate various processes.
This new wave of AI research was largely driven by businesses and by 1993 the money dried up once more.
半现代- 1993年至2010年
记得垃圾邮件? 在90年代和21世纪初，它让我们都疯了. Now it’s a non-problem because of something called a Bayesian classifier. This kind of spam detection learns what spam looks like from human spam reporting.
神经网络再次流行起来. These are supposed to imitate the networks of nerve cells that make up our brains, 就像我们的大脑, 神经网络可以学习识别模式. 比如猫的照片.
人们对优化问题重新产生了兴趣. Suppose you’re Amazon and you want to ship as many packages as possible using the fewest vans, or you employ shift workers and you want to fill all shifts at the lowest cost. For these problems it’s often infeasible to find the perfect solution, 但我们可能会想出足够好的解决方案.
今天
人工智能无处不在. Your inbox automatically categorises emails and populates your calendar when you book a flight, 你的照片应用知道你的照片里有什么, 机场使用面部识别检查你的护照.
Today’s proliferation of AI applications results largely from having powerful computers, 包括移动设备, 但这也是由于云计算的普及.
Cloud AI
想象一下，你想从一张照片中识别狗的品种. You start by collecting thousands of photos (great way to meet new dogs) and you manually label those photos with the correct breed. Next you feed this data into a mysterious box of AI, which produces something called a model.
The model is the prize you get for all the hard work collecting and labeling data. 这个模型可以识别没有训练过的新狗.
训练一个模型来识别狗的品种
很可爱，但这和云计算有什么关系呢?
Google Photos可以帮你整理照片. It knows whether a photo is outdoors, and if it contains people or animals. This is because Google have spent years collecting and labeling enormous quantities of photos which they use to train models.
All the major cloud providers offer pre-trained AI models for image classification, 文本分析等等. The same model used by Google Photos is available through Google Cloud.
Cloud AI represents a shortcut to having fully-fledged AI capabilities in a product. Using the work others have put into pre-trained models saves a great deal of time and money.
外来神经网络
自50年代以来，神经网络发生了很多变化. 也许你听说过深度学习这个词. This usually means using very large neural networks with very large sets of training data.
Another term that gets thrown around is convolutional neural network. 这是一个专门的神经网络, 根据我们自己的视觉皮层设计的, that’s popular for visual applications like self-driving cars or dog recognition.
道德的考虑
As AI has began to influence people’s lives in significant ways the public discourse has started to emphasise ethics.
If you applied for a job and discovered that an AI is responsible for screening applications, 你会相信吗?? A few years ago Amazon trialed this idea for engineering roles, resulting in 对女性的偏见. The AI was trained using historical decisions made by humans, so the computer adopted human biases.
When an AI makes decisions that affect people it’s crucial to consider what biases might affect it.
组织像 AI for Good英国 推动工业道德建设. 与此同时，人们也在努力利用人工智能做好事. 许多参与人工智能的公司都有一个人工智能公益计划， 包括我们自己.
The future
预测人工智能的未来是困难的. 我们正处于人工智能的繁荣时期，但这种繁荣可能不会永远持续下去, 但即使炒作停止，我们也认为人工智能会继续存在. 它与我们日常使用的技术交织在一起.
As computing power improves we’ll see more sophisticated AI models, and cloud-based AI-as-a-service 产品将使这些模型能够被广泛的受众所访问.
边缘计算正在引领新的应用领域. Edge just means ‘not on the cloud’, but it’s not quite as silly as it sounds. You might want to train a model in the cloud where there’s lots of computing power and deploy that model to a device with limited power and bandwidth. 想想监控工厂设备的智能摄像头.
另一个需要关注的是可解释的人工智能. This is the idea that AI should be able to explain itself when it makes a decision. 现在我们训练的模型给了我们一个答案, 比如“那只狗是博德牧羊犬”, 但他们无法解释原因. Explainable AI addresses some ethical concerns and it also makes it easier to debug and improve our models.
但实际上，什么是人工智能?
The curious thing is that once a research idea turns into a useful piece of technology, 它不再被大众认为是人工智能. Spam filtering for instance feels so simple while ‘AI’ sounds so sophisticated.
Perhaps what distinguishes AI is the ability to learn and generalise? 啊，但是人工智能还是有学习的能力. 最终，它只是软件.
