AI: Why a Systems Engineering Approach is Essential
Abstract
Artificial intelligence (AI) is already turning industries on their head, and the technology is poised to make an even greater impact on the world in the years to come. Doctors are using AI tools to help with diagnostics, carmakers are working to make autonomous vehicles a widespread reality, and nearly all of us each day view online or mobile advertisements that were selected specifically for us by an algorithm. Too often, though, business and IT leaders take a limited view of AI. They often focus almost exclusively on machine learning (ML) - sometimes even using "ML" as a synonym for "AI." But AI technologies are, in fact, key enablers to complex systems. They require not only ML technologies, but also trustworthy data sensors and sources, appropriate data conditioning processes, and a balance between human and machine interactions. Bringing all of these disparate sub-components together requires a system engineering approach - an approach that is, unfortunately, lacking in many organizations' views and implementations of AI.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 30, 2020
- Accession Number
- AD1147535
Entities
People
- David R. Martinez
Organizations
- MIT Lincoln Laboratory