本文为美国著名数据分析网站DZone分析师Tom Smith与Exaptive的副总裁Matt Coatney的专访对话，对人工智能和机器学习的未来做了深度的探讨。Exaptive是一家美国俄克拉荷马州以提供bet36备用网址分析产品及服务为主的初创企业。
本文由可译网言午二二 , Amanda沈两位朋友翻译。
Tom Smith: 胜利的人工智能/机器知识策略的关键是什么？
Matt Coatney: 与DevOps（开拓运营）不同的是，它涉及到现金百家乐游戏的人员和方法，因为新技术正在给商业治理战略带来变革。一方面，它能够替代人们所做的工作，并且更有效、可靠、高效地完成这些任务。另一方面，以前不可行的新商业模式变得可行。
Matt Coatney: 与需要解决特别商业问题的技术相比，企业现金百家乐游戏地把时间花费在他们认为他们所需要的技术上。企业需要思索他们正试图解决的问题以及如何使解决方案让客户满足。思索如何让解决方案生效，以便你能够实现一个积极的回报率，进一步谈下一个项目和协作时机。设定你的胜利标准并快速取胜。这与我们过去20年来在IT方面所做的项目没有什么区别，我们只需要牢记最佳方法。
Tom Smith: 问：在过去的一年里人工智能/机器学习是如何变化的？
Matt Coatney: 大多数企业专注于bet36备用网址“Hadoopesque”工具。我们也能够这么做，但是我们也能够使用如SQL、NoSQL、Microsoft和Python’s的scikit-learn库等较小的数据工具来找到价值。无论数据规模如何，还有许多价值有待从现有数据中去挖掘。
Tom Smith: 对于人工智能/机器知识的可继续开展的最大时机在哪里？
- 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来优化一切服务器，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。
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.
Q: What real-world problems are your client solving with AI/machine learning?
Anything around forecasting, reconnecting, or predicting content — Netflix-style applications. Financial modeling and the democratization of advanced financial models. Also, content and knowledge management tools that help organizations get more insight and value from their content by tagging concepts, keywords, etc.
Q: What are the most common issues you see preventing companies from realizing the benefits of AI/machine learning?
Companies are focusing on tools and platforms instead of the business problem they are trying to solve. They need to separate the hype from reality, understanding what tools can and cannot do. The marketing hype is being bought and creating unrealistic expectations. There needs to be better vetting and understanding of the tools. Understand that it takes time to train AI for the industry and use case (e.g., how lawyers write and talk).
Q: Where do you see the biggest opportunities in the continued evolution of AI/machine learning?
A: I’m excited about AI as a service and the opportunity that provides for developers and entrepreneurs looking to start a business quickly without a lot of expense.
Decision support and automation in the knowledge space. Greater perspective on problems leads to better, less biased, solutions.
The merge of the physical and virtual world with robotics.
Use data to solve business problems. Google’s data centers use 25% of a nuclear power plant every day. Google used Deep Mind to optimize all their servers and reduced energy consumption by 15 to 20%. Ultimately every business will be able to realize the same type of OPEX savings.
Q: What are your biggest concerns regarding the state of AI/machine learning today?
A: Will AI be used for good or evil? It is neutral. It depends on how it’s applied and who applies it. We need international oversight. It is already being used in cyberwarfare.
Avoid getting stuck in a local maximum. We’ve used the same hardware and software architecture for the last 60 to 70 years to do something infinitely more complex than we’ve ever done before. We need to explore different approaches to exponentially improve performance.
Q: What skills do developers need to work on AI/machine learning projects?
A: Start with soft skills. The best developers and data scientists have paid attention to improving their project management, communication, and time management skills. Focus on understanding abstract concepts and be as well-rounded as you can with different languages and technologies. Embrace creative destruction since the landscape is fluid and changing rapidly.
Q: What have I failed to ask that you think developers need to know about AI and machine learning?
There is a lot of misconception around terminology. We need to get clarity about what we mean when we use these terms:
Machine learning is how we use software to learn something.
AI is synonymous with machine learning but tends to connote a more advanced, human level of capability.
Deep learning is a specific machine learning technique that is capable of handling more nuanced learning, which tends to be associated with AI.