Systems Engineering: An Enabler for Artificial Intelligence
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Artificial Intelligence (AI) is being increasingly applied to develop high-performance systems specialized for particular problem domains (e.g., image and speech understanding). These "knowledge-based" systems rely on large amounts of problem-specific knowledge and heuristics. This presentation reviews current AI knowledge-based applications and discuss SE principles required for their design and implementation. In addition, the possibilities of using AI to support systems engineering is discussed.
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Systems Engineering: An Enabler for Artificial Intelligence
- 1. Systems Engineering: An Enabler for Artificial Intelligence Rick Hefner, Caltech INCOSE Heartland Chapter 29 Oct 2020 1
- 2. A machine is said to have artificial intelligence if it can interpret data, potentially learn from the data, and use that knowledge to adapt and achieve specific goals 2
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- 6. 6Challenges in the integration of Systems
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- 12. 12 • Seeing AI: Talking camera for the blind that can recognize faces, emotions, text, and objects • Calorie Mama AI: automatically counts food calories from photos • ELSA Speak: English Language Speech Assistant • Aura: Tracks your mood AI on Your iPhone
- 13. 13 How is Systems Engineering different with AI components? How could AI change the way Systems Engineering is performed?
- 14. 14 https://www.slideshare.net/icrnk ovic/sofwtare-engineerig- challanges-in-builing-aibased- complex- systems?qid=6ac71ab1-62df- 4501-9eaf- 04756904c458&v=&b=&from_s earch= Software Engineering Challenges in building AI-based complex systems, Ivica Crnkovic
- 15. SE vs. AI Approaches 15
- 16. Changing Focus of Systems Engineering A fresh look at Systems Engineering – what is it, how should it work • Interdisciplinary • To engineer dependable, robust, pseudo-deterministic, mainly technological systems • Requirements and operational concepts – Can be established early in the lifecycle – Are not expected to change (much) through life • Transdisciplinary • To address resilient, adaptive systems and systems-of-systems that may be in a state of continual evolution (at least their operational environment, and probably the system as well) • Systems of interest may be autonomous, possibly involving Artificial Intelligence, probably involving environmental aspects, and certainly involving social aspects as well as engineering and technology • To address societal grand challenges related inter alia to the Sustainable Development Goals (SDGs) • Such systems will still need dependable robust technological building blocks (which is why we say the focus “opens out” rather than “shifts”). 16
- 17. Where Could AI Enhance Systems Engineering? 17
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- 19. 19 The Jobs That Artificial Intelligence Will Create MIT Sloan Management Review, Summer 2017 TRAINERS Customer- language tone and meaning trainer Teaches AI systems to look beyond the literal meaning of a communication by, for example, detecting sarcasm. Smart-machine interaction modeler Models machine behavior after employee behavior so that, for example, an AI system can learn from an accountant’s actions how to automatically match payments to invoices. Worldview trainer Trains AI systems to develop a global perspective so that various cultural perspectives are considered when determining, for example, whether an algorithm is “fair.” EXPLAINERS Context designer Designs smart decisions based on business context, process task, and individual, professional, and cultural factors. Transparency analyst Classifies the different types of opacity (and corresponding effects on the business) of the AI algorithms used and maintains an inventory of that information. AI usefulness strategist Determines whether to deploy AI (versus traditional rules engines and scripts) for specific applications. SUSTAINERS Automation ethicist Evaluates the noneconomic impact of smart machines, both the upside and downside. Automation economist Evaluates the cost of poor machine performance. Machine relations manager “Promotes” algorithms that perform well to greater scale in the business and “demotes” algorithms with poor performance.
- 20. AI Research Autonomous and Sensing Systems 20
- 22. 22 AI Research Collaborative and Swarm Intelligence
- 23. 23 AI in Space
- 24. AI Research Metamaterials and sensors Multi-scale integration Nature-inspired computing Autonomous agents Biological, mechanical, and electronic systems integration Autonomy and interaction Heterogeneous systems and decision-making Multi-agent collaboration Brain-like intelligence and bionics 24