Estimated reading time: 3 minutes

Nolan’s Notes: Coming to Terms With AI

How fast do things move in the world of data analytics? Here’s an example. We’ve been planning this issue on artificial intelligence for the past few months, and, in fact, I had already written this column about a month ago. Then I went to IPC APEX EXPO and upended it all. I originally had compared AI to drag racing in that (CPU) horsepower and new (data) vehicles have steadily delivered higher performance competition. That seemed pretty accurate given how generative AI models dominated the popular media with amazing results—and sometimes spectacular crashes.

In all my career, I’ve never seen a new technology move so fast into adoption as has been the case with AI and machine learning (ML). I’m not exaggerating when I call this the largest inflection point in manufacturing since steam, with undeniably the fastest rate of change, something that, understandably, is a lot to come to terms with.

This was made abundantly clear during my recent week in Anaheim. For me, news and perspective came from two main sources: My conversations at the show with industry experts were followed by a research report about data and AI recently published by MIT¹. In our special coverage of the trade show, Real Time With… IPC APEX EXPO 2024 Show and Tell Magazine, I write about the collaboration emerging across the electronics manufacturing equipment market.

As a result, we scrambled to gather the very latest AI-related news from the show to include in this issue of SMT007 Magazine. Take note: There was a lot to gather. Serendipitously, after returning home with my head full of digital factory developments, I came across this research from MIT that made clear that the collaboration I saw in our industry is emerging across nearly all levels of business.

Here is a list of key findings published in the Executive Summary of the paper:

• CIOs are doubling down on their investments in data and AI
• Consolidation of data and AI systems is a priority
• Democratization of AI raises the stakes for governance
• Executives expect AI adoption to be transformative in the short term
• As generative AI spreads, flexible approaches are favored
• Lakehouse has become the data architecture of choice for the era of generative AI
• Investment in people will unlock more value from data and AI

The data was collected last summer, and respondents came from eight industry sectors—retail, media, telecom, manufacturing, government, health care, financial, and energy—with each organization earning a minimum of $500 million a year in revenue.

In the conclusion, the report states, “Generative AI will be an inflection point. Experts predict that it will [unleash] a new wave of productivity, potentially adding trillions of dollars of new value across industries.”

All this begs the question: How does this change electronics manufacturing? Glad you asked. The new data infrastructures that AI is bringing mean creative new productivity tools—as evidenced by the tools the team at Plato Systems is bringing for human/machine interactions into your data infrastructure for efficiency and quality assessment. New data infrastructures are also driving hardware development in stressful ways. We spoke with IBM’s Arvind Kumar on work to push Moore’s Law forward, and not surprisingly, he talks at length about chiplet architectures and advanced packaging. We also excerpt a chapter from a new book recently published by I-Connect007 authored by Cogiscan’s Julia Cliche-Dubois, The Printed Circuit Assembler’s Guide to... Factory Analytics. Also included are fresh news on AI developments and collaborations from Arch Systems, Aegis, and Koh Young. Columnist Mike Konrad brings his inside-the-ballpark perspective on the transformation now underway.

Our long-time columnist Dr. Jennie Hwang continues her multi-part series on artificial intelligence. I encourage you to revisit her primer in the April issue.

We may call this AI stuff “intelligence,” but AI is to data what the steam engine tractor was to farm work. It didn’t replace the farmer, but those farmers who came to terms with the steam engine were able to produce like never before. AI won’t replace us. AI will automate the mundane and find hidden patterns in the data. It still takes skilled human insight to make sense of—and come to terms with—those patterns.

Reference:

1. “Laying the foundation for data- and AI-led growth,” MIT Technology Review Insights.

This column originally appears in the May 2024 issue of SMT007 Magazine.