Why applied AI requires skills and knowledge beyond data science

There are a dozen artificial intelligence conferences where researchers push the boundaries of science and show how neural networks and deep learning architectures can take on new challenges in areas such as computer vision and natural language processing. But using machine learning in real-world applications and business problems--often referred to as "applied machine learning" or "applied AI"--presents challenges that are absent in academic and scientific research settings. Applied machine learning requires resources, skills, and knowledge that go beyond data science, that can integrate AI algorithms into applications used by thousands and millions of people every day. Alyssa Simpson Rochwerger and Wilson Pang, two experienced practitioners of applied machine learning, discuss these challenges in their new book Real World AI: A Practical Guide for Responsible Machine learning. Rochwerger, a former director of product at IBM Watson, and Pang, the CTO of Appen, draw on their personal experience and knowledge to provide many examples of how organizations succeeded or failed in integrating machine learning into their products and business models.