机器人使物质世界和虚拟世界融合。
使用数据解决商业问题。谷歌的数据中心每天使用25%的核电站，谷歌使用Deep Mind来优化所有服务器，并能降低15%到20%的能耗。最终，每个企业都能够实现相同类型的运营成本节省。
Tom Smith：您对当今人工智能/机器学习最大的关注是什么？
Matt Coatney：人工智能会被善用还是误用?它是中性的。取决于它如何被应用和谁来使用它。我们需要国际监督。它已经被用于网络战。要避免陷入局部最大值。在过去的60到70年里，我们使用了相同的硬件和软件架构，完成了前所未有的复杂工作。我们需要探索不同的方法来成倍地提高性能。
Tom Smith：从事人工智能/机器学习项目的开发者需要哪些技能？
Matt Coatney：从软技能开始。最好的开发人员和数据科学家注意提高他们的项目管理、沟通和时间管理技能。专注于理解抽象概念，并尽可能全面地使用不同的语言和技术。拥抱创造性的破坏，因为景色是迅速流动和变化的。
Tom Smith：你认为开发人员需要了解的关于人工智能和机器学习的问题哪些我还没有问到？
Matt Coatney：关于术语有很多误解。当我们使用这些术语时，我们需要弄清楚我们的意思：
机器学习是指我们如何使用软件来学习东西。
人工智能是机器学习的代名词，但往往意味着更高级的、人类的能力水平。
深度学习是一种特定的机器学习技术，能够处理更精妙的学习，往往与人工智能有关。
英文原文：
Thanks to Matt Coatney, V.P. Services at Exaptive for taking the time to talk with me about the state of AI and machine learning today and how he sees it evolving.
Q: What are the keys to a successful AI/machine learning strategy?
A: Not unlike the DevOps movement, it has more to do with the people and the approach, since the new technology is introducing a change in business management strategy. On the one hand, it can replace tasks that people have been doing and do those tasks more effectively, reliably, and efficiently. On the other hand, new business models are feasible where they weren’t before.
A couple of examples Matt shared:
In medicine, IBM’s Watson detected a completely different strain of leukemia than the group of doctors had even considered in less than 10 minutes.
Atomwise, a Silicon Valley biotech, is looking for existing drugs to apply to new targets and found two drugs that prevented the spread of Ebola in one day. This type of research used to take years.
Q: How can companies get more out of big data with AI and machine learning?
A: Companies spend too much time on the technology they think they need versus focusing on the technology needed to solve a particular business problem. Companies need to think about the problem they are trying to solve and how to make the solution palatable to the consumer. Think about how to make the solution effective so you can realize a positive ROI and move on to the next project or opportunity. Define your success metrics and get quick wins. It’s not that different than the projects we’ve been doing in IT for the past 20 years, we just need to keep the best practices in mind.
Q: How has AI/machine learning changed in the past year?
A: A lot of approaches have been the same for the last 50 to 60 years, it’s just that we have far more powerful computers with more memory and optimized algorithms like deep learning, so that we can now get better results in a fraction of the time. Examples include Facebook’s facial recognition and Google’s self-driving cars. In addition, we now have AI as a service where companies can rent time from a computer, issue requests, and get information back in record time. This lowers the barriers to entry while ensuring any organization gets the same level of quality as the Facebooks and Googles of the world.
Q: What are the technical solutions you use to collect and analyze data?
A: Most companies focus on the big data “Hadoopesque” tools. We can do that, but we also find value in smaller data using tools like SQL, NoSQL, Oracle, Microsoft, and Python’s scikit-learn library to get novel results without investing millions. There is still a lot of value to be mined from existing data regardless of size.
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