Augmented Intelligence: Combining Model Based Systems Engineering with AI & Machine Learning
Growing complexity and scope of modeled systems has increased the difficulty for engineers to manually represent and interpret system engineering artifacts. Complex systems also have the interplay of many different factors, leading to difficult to predict “emergent phenomenons" that may not be identified by individual system engineers working with a model, nor the stakeholders reviewing the model. Artificial intelligence (AI) and machine learning offer an data-centric approach to model development and validation, where past experiences can be used to inform and improve future designs. However, those computational decisions can be opaque, with the human engineer lacking shared intuition.
This talk will explore the opportunities for artificial intelligence in the system engineering domain in ways that unite the unique capabilities of the systems engineer with the AI. This collaboration of human and machine intelligence is known as Augmented Intelligence. There is little doubt that systems engineering productivity could be improved with effective utilization of well-established AI techniques, such as machine learning, natural language processing, and statistical models. However, human engineers excel at many tasks that remain difficult for AIs, such as visual interpretation, abstract pattern matching, and drawing broad inferences based on experience. Combining the best of AI and human capabilities, along with effective human/machine interfaces and data visualization, offers the potential for orders-of-magnitude improvements in systems engineering capabilities.
Augmented Intelligence promotes “team play” of human and machine intelligence. By effectively joining the human skills in pattern matching, unstructured data, and intuition with computational approaches that excel in domain search, systematic trade space exploration, and statistical evaluation, the combined “team” has been proven to be more effective than either in isolation. For instance, machine learning algorithms can process past designs, and visually present outcomes and the various tradeoffs. The human team can evaluate the domain space quickly, and watch for exceptional cases that might not be accurately handled by the machine. This talk will explore the potential, challenges, and requirements of implementing Augmented Intelligence in the systems engineering domain.