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Tool or threat? A pastor’s view

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In a recent column in Knox News (“Why the people of Tennessee should be worried about artificial intelligence,” Aug. 9) the writer argued that “it’s time to take the AI threat seriously.” The piece raises legitimate concerns, such as the possibility of misinformation and the economic disruption that comes when machines take over work once done by people. Those are not small issues.

But as a pastor, I want to add a different perspective: AI is not just a threat. Like every new technology in history, it can also be a tool for human flourishing − if used wisely.

Take the printing press. In the 1400s, critics warned that printed Bibles would never match the devotion stirred by handwritten ones. They were partly right: Printed books changed the way people interacted with Scripture. But history shows that the press ultimately multiplied access to God’s Word beyond what any scribe could have imagined.

AI is today’s printing press moment. Yes, it has dangers. But it also has potential to expand human creativity, not replace it.

AI does the repetitive work so we can help people grow in faith

In my own work as a pastor, I’ve seen this firsthand. Every week I face a mountain of preparation − studying Scripture, crafting messages, writing questions for small groups, and creating follow-up tools. None of that is “busy work.” It’s the stuff that helps people grow in faith. But pastors are often stretched thin. That’s where AI can serve, not supplant.

For example, when one is brainstorming fresh sermon illustrations, AI can act like the fastest research assistant in the world. Instead of reusing the same stories, I can generate dozens of new angles, check them against Scripture, and then adapt them in my own voice. That saves me hours and helps me connect timeless truth to modern life.

Or consider creating discipleship materials. In the past, a pastor might have needed an entire staff team to write devotional guides, small group questions, or children’s activities that reinforce Sunday’s message. Today, I can generate drafts in minutes, then refine them through prayer, study, and pastoral wisdom. That doesn’t replace me − it amplifies my ministry by making good content available faster and more widely.

And this is not just about pastors. Writers, teachers, business leaders, and community organizers are finding similar value. AI can clear the clutter of repetitive work so humans can focus on what only humans can do: empathy, creativity, moral judgment, and vision.

The test for us is to use artificial intelligence wisely

To be clear: We should never use AI uncritically. No algorithm can pray, love, or discern truth the way a human can. And if misused, AI could indeed foster laziness or deception. But that’s not new. Books, radio, and television all faced the same critiques. The real question is not “Will AI ruin us?” but “Will we steward it wisely?”

For pastors like me, that means remembering that AI is a tool, not a teacher. It can help with brainstorming, research, and communication, but the sacred calling of preaching belongs to the pastor who listens to God’s Spirit and shepherds God’s people.

Rather than panic about AI, I suggest a better approach: regulate where necessary, but also explore how it can serve humanity. From where I stand, AI is not about replacing people. Instead, it’s about making pastors more efficient so we can spend less time formatting slides and more time in prayer, conversation, and community.

AI will certainly change our world. The only question is whether we will fear it, or − like generations before us − adapt it for the good of our neighbors.

Darrell Stetler II is a pastor in Oklahoma City and the creator of NewStart Discipleship, which provides tools and training to help pastors and churches make disciples in today’s world.



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If I Could Only Buy 1 Artificial Intelligence (AI) Chip Stock Over The Next 10 Years, This Would Be It (Hint: It’s Not Nvidia)

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While Nvidia continues to capture headlines, a critical enabler of the artificial intelligence (AI) infrastructure boom may be better positioned for long-term gains.

When investors debate the future of the artificial intelligence (AI) trade, the conversation generally finds its way back to the usual suspects: Nvidia, Advanced Micro Devices, and cloud hyperscalers like Microsoft, Amazon, and Alphabet.

Each of these companies is racing to design GPUs or develop custom accelerators in-house. But behind this hardware, there’s a company that benefits no matter which chip brand comes out ahead: Taiwan Semiconductor Manufacturing (TSM -3.05%).

Let’s unpack why Taiwan Semi is my top AI chip stock over the next 10 years, and assess whether now is an opportune time to scoop up some shares.

Agnostic to the winner, leveraged to the trend

As the world’s leading semiconductor foundry, TSMC manufactures chips for nearly every major AI developer — from Nvidia and AMD to Amazon’s custom silicon initiatives, dubbed Trainium and Inferentia.

Unlike many of its peers in the chip space that rely on new product cycles to spur demand, Taiwan Semi’s business model is fundamentally agnostic. Whether demand is allocated toward GPUs, accelerators, or specialized cloud silicon, all roads lead back to TSMC’s fabrication capabilities.

With nearly 70% market share in the global foundry space, Taiwan Semi’s dominance is hard to ignore. Such a commanding lead over the competition provides the company with unmatched structural demand visibility — a trend that appears to be accelerating as AI infrastructure spend remains on the rise.

Image source: Getty Images.

Scaling with more sophisticated AI applications

At the moment, AI development is still concentrated on training and refining large language models (LLMs) and embedding them into downstream software applications.

The next wave of AI will expand into far more diverse and demanding use cases — autonomous systems, robotics, and quantum computing remain in their infancy. At scale, these workloads will place greater demands on silicon than today’s chips can support.

Meeting these demands doesn’t simply require additional investments in chips. Rather, it requires chips engineered for new levels of efficiency, performance, and power management. This is where TSMC’s competitive advantages begin to compound.

With each successive generation of process technology, the company has a unique opportunity to widen the performance gap between itself and rivals like Samsung or Intel.

Since Taiwan Semi already has such a large footprint in the foundry landscape, next-generation design complexities give the company a chance to further lock in deeper, stickier customer relationships.

TSMC’s valuation and the case for expansion

Taiwan Semi may trade at a forward price-to-earnings (P/E) ratio of 24, but dismissing the stock as “expensive” overlooks the company’s extraordinary positioning in the AI realm. To me, the company’s valuation reflects a robust growth outlook, improving earnings prospects, and a declining risk premium.

TSM PE Ratio (Forward) Chart

TSM PE Ratio (Forward) data by YCharts

Unlike many of its semiconductor peers, which are vulnerable to cyclicality headwinds, TSMC has become an indispensable utility for many of the world’s largest AI developers, evolving into one of the backbones of the ongoing infrastructure boom.

The scale of investment behind current AI infrastructure is jaw-dropping. Hyperscalers are investing staggering sums to expand and modernize data centers, and at the heart of each new buildout is an unrelenting demand for more chips. Moreover, each of these companies is exploring more advanced use cases that will, at some point, require next-generation processing capabilities.

These dynamics position Taiwan Semi at the crossroad of immediate growth and enduring long-term expansion, as AI infrastructure swiftly evolves from a constant driver of growth today into a multidecade secular theme.

TSMC’s manufacturing dominance ensures that its services will continue to witness robust demand for years to come. For this reason, I think Taiwan Semi is positioned to experience further valuation expansion over the next decade as the infrastructure chapter of the AI story continues to unfold.

While there are many great opportunities in the chip space, TSMC stands alone. I see it as perhaps the most unique, durable semiconductor stock to own amid a volatile technology landscape over the next several years.

Adam Spatacco has positions in Alphabet, Amazon, Microsoft, and Nvidia. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Amazon, Intel, Microsoft, Nvidia, and Taiwan Semiconductor Manufacturing. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft, short August 2025 $24 calls on Intel, short January 2026 $405 calls on Microsoft, and short November 2025 $21 puts on Intel. The Motley Fool has a disclosure policy.



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Researchers train AI to diagnose heart failure in rural patients using low-tech electrocardiograms

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WVU computer scientists are training AI models to diagnose heart failure using data generated by low-tech equipment widely available in rural Appalachian medical practices. Credit: WVU/Micaela Morrissette

Concerned about the ability of artificial intelligence models trained on data from urban demographics to make the right medical diagnoses for rural populations, West Virginia University computer scientists have developed several AI models that can identify signs of heart failure in patients from Appalachia.

Prashnna Gyawali, assistant professor in the Lane Department of Computer Science and Electrical Engineering at the WVU Benjamin M. Statler College of Engineering and Mineral Resources, said —a chronic, persistent condition in which the heart cannot pump enough blood to meet the body’s need for oxygen—is one of the most pressing national and global health issues, and one that hits rural regions of the U.S. especially hard.

Despite the outsized impact of heart failure on rural populations, AI models are currently being trained to diagnose the disease using data representing patients from urban and suburban areas like Stanford, California, Gyawali said.

“Imagine Jane Doe, a 62-year-old woman living in a rural Appalachian community,” he suggested. “She has limited access to specialty care, relies on a small local clinic, and her lifestyle, diet and health history reflect the realities of her environment: high physical labor, minimal preventive care, and increased exposure to environmental risk factors like coal dust or poor air quality. Jane begins to experience fatigue and shortness of breath—symptoms that could point to heart failure.

“An AI system, trained primarily on data from urban hospitals in more affluent, coastal areas, evaluates Jane’s lab results. But because the system was not trained on patients who share Jane’s socioeconomic and environmental context, it fails to recognize her condition as urgent or abnormal,” Gyawali said. “This is why this work matters. By training AI models on data from West Virginia patients, we aim to ensure people like Jane receive accurate diagnoses, no matter where they live or how their lives differ from national averages.”

The researchers identified the AI models that were most accurate at diagnosing heart failure in an anonymized sample of more than 55,000 patients who received medical care in West Virginia. They also pinpointed the exact parameters for providing the AI models with data that most enhanced diagnostic accuracy. The findings appear in Scientific Reports, a Nature portfolio journal.

Doctoral student Alina Devkota emphasized they trained the AI models to work from patients’ electrocardiogram results, rather than the echocardiogram readings typical for patient data from urban areas.

Electrocardiograms rely on round electrodes stuck to the patient’s torso to record electrical signals from the heart. According to Devkota, they don’t require specialized equipment or specialized training to operate, but they still provide valuable insights into heart function.

“One of the criteria to diagnose heart failure is by measuring the ‘ejection fraction,’ or how much blood is pumped out of the heart with every beat, and the gold standard for doing that is with echocardiography, which uses to create images of the heart and the blood flowing through its valves,” she said.

“But echocardiography is expensive, time-consuming and often unavailable to patients in the very same rural Appalachian states that have the highest prevalence of heart failure across the nation. West Virginia, for example, ranks first in the U.S. for the prevalence of heart attack and , but many West Virginians don’t have local access to high-tech echocardiograms. They do have access to inexpensive electrocardiograms, so we tested whether AI models could use electrocardiogram readings to predict a patient’s ejection fraction.”

Devkota, Gyawali and their colleagues trained several AI models on patient records from 28 hospitals across West Virginia. The AI models used either “deep learning,” which relies on multilayered neural networks, or “non-deep learning,” which relies on simpler algorithms, to analyze the patient records and draw conclusions.

The researchers found the models, particularly one called ResNet, did best at correctly predicting a patient’s ejection fraction based on data from 12-lead electrocardiograms, with the results suggesting that a larger dataset for training would yield even better results. They also found that providing the AI models with specific “leads,” or combinations of data from different electrode pairs, affected how accurate the models’ ejection fraction predictions were.

Gyawali said while AI models are not yet being used in due to reliability concerns, training an AI to successfully estimate from electrocardiogram signals could soon give clinicians an edge in protecting patients’ cardiac health.

“Heart failure affects more than six million Americans today, and factors like our aging population mean the risk is growing rapidly—approximately 1 in 4 people alive today will experience heart failure during their lifetimes. The prevalence is even higher in rural Appalachia, so it’s critical the people here do not continue to be overlooked.”

Additional WVU contributors to the research included Rukesh Prajapati, graduate research assistant; Amr El-Wakeel, assistant professor; Donald Adjeroh, professor and chair for computer science; and Brijesh Patel, assistant professor in the WVU Health Sciences School of Medicine.

More information:
AI analysis for ejection fraction estimation from 12-lead ECG, Scientific Reports (2025). DOI: 10.1038/s41598-025-97113-0scientific

Citation:
Researchers train AI to diagnose heart failure in rural patients using low-tech electrocardiograms (2025, August 31)
retrieved 31 August 2025
from https://medicalxpress.com/news/2025-08-ai-heart-failure-rural-patients.html

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Should artificial intelligence be embraced in the classroom? – CBS News

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Should artificial intelligence be embraced in the classroom?  CBS News



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