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SMT Perspectives & Prospects: Artificial Intelligence, Part 7—Data Module 2

04/08/2026 | Dr. Jennie Hwang -- Column: SMT Perspectives and Prospects
When I last wrote about data, I focused on data quality and size, data infrastructure, the boundaries of data training, data preparation, and the general challenges and concerns of data. This month, I will focus on alternative approaches, data governance, retrieval-augmented generation (RAG), and agentic RAG.

Is AI in Design the Magic Tonic for All That Ails Us?

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In electronic systems design, engineering teams are pressured by management to adopt AI wherever possible to reduce design cycle time and cost. Engineers look at AI with a wary eye: Will it consistently return accurate results? Will it take longer to set up, train, run, and verify than doing it manually? Will it replace me? Yes, there is a transition with AI, but it’s no different than any other form of automation. It must be extensively

SMT Perspectives & Prospects: 12 Predictions for Using AI in 2026

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At a committee meeting in Washington, D.C., someone asked me if I think AI is a bubble. Over the past several months, this has become a prevalent and pervasive question in both business circles and broader society. Yet, despite its ubiquity, no one had ever asked me that question directly, and especially with such immediacy in front of a committee. My instinctive reply was, “It depends on how you define the bubble. It is certainly not an internet bubble.” The querier lamented, almost disappointed, “Well, you are in the minority.”
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