虽然人工智能技术还需要继续完善，但其将会越来越多的走进我们的工作及生活，其表现为：

大数据：大型非结构化数据集培训强大的机器智能，现在有大量的出现。例如，语言翻译和图像，面部，行为和情感识别 - 基于预测的分析在有充足数据支持的条件下变得更加准确。尤其对于社交媒体，其有大量的数据集可利用。据报告所指出，Facebook面部识别领域有所成功，而谷歌在机器翻译领域通过搜集大量的多语种文档而获得优势。展望未来人工智能必将在大数据中得到重要的应用。

软件和硬件的进步：神经网络和并行处理将是人工智能的重要发展工具，因为它们更接近于人类大脑的工作方式。特别是，基于GPU计算的出现可以大大加快神经网络的处理能力。 总之，深度学习软件和并行处理硬件现在提供了一个功能强大的[机器智能]平台。

云计算的商业模式：云计算是机器学习商业模式的强大动力，据报告：“我们基本上看到机器智能与云计算经济的重合。”云计算之前，大多数人工智能的工作是孤立的，成本比较高，但与云计算相结合后，机器学习能力，如识别人脸或语言翻译，会既便宜又好用

Three reasons why AI is taking off right now (and what you need to do about it)

From ZDNet

Three factors are combining to create a tipping point after which the use of artificial intelligence will become commonplace.

According to the Leading Edge Forum - the research arm of tech vendor CSC - while there is still plenty of work to do, the three main ingredients needed for AI to take off are now in place:

Big Data: Large unstructured data sets are handy for training powerful machine intelligence and there are now plenty of these around. Initiatives such as language translation and image, facial, activity and emotion recognition - are based on predictive analytics that get more accurate as the data behind them gets richer. And the rise of big data - and social media in particular - means there are lots of data sets to exploit. As the report notes, Facebook enjoys a huge head start in facial recognition because it can already match our names and faces, just as Google has important advantages in machine translation because it has aggregated the best set of multilingual documents.

"Looking ahead, new and established MI companies will use millions of internet images, videos and podcasts of people smiling, laughing, frowning, talking, arguing, holding hands, walking, playing football and so on as the basis for unprecedented emotion and activity recognition capabilities. MI is now clearly among the most important Big Data applications."

Software and hardware advances: It's long been known that neural networks and parallel processing would be important development tools of AI because they more closely resemble the way the human brain works. In particular, the emergence of GPU-based computing can greatly accelerate neural network processing capabilities - and if more processing power is needed there are the vast cloud computing resources of Amazon, Microsoft, Google. "Taken together, deep learning software and parallel processing hardware now provide a powerful [machine intelligence] platform," the report said.

Cloud business models: The emergence of machine learning business models based on the use of the cloud is the single biggest reason that the field is so energized today, the report said: "We are essentially seeing the merger of machine intelligence with cloud economics."

Before the cloud, most AI work was isolated and relatively high cost, but the economics of the cloud mean machine learning capabilities, such as recognizing faces or translating languages, will cheap and easy to use

"It is this realization that is triggering both the explosion of highly specialized MI start-ups, as well as the major machine intelligence pushes at Google, Facebook, Microsoft,Apple, IBM and their various global rivals."

The researchers set out a 10 point plan for organisations that want to prepare for machine intelligence:

1. Embrace the idea that machine intelligence will matter to your organization.

2. Identify which forms could be most important to your firm.

3. Check out relevant start-ups and developments.

4. Understand which parts of your firm could be safely run by algorithms.

5. Determine which internal and external data sets have the most potential.

6. Assess the extent to which your firm's key professional expertisecan be automated.

7. Try out deep learning, neural computing and other technologies.

8. Map the relevant MI services and technologies to your firm's value chain.

9. Develop machine intelligence experts in your organisation.

10. Factor AI advances into your strategic planning.