Inside Silicon Design: ESD Protection, Wearable Analog Front-Ends, and Technical Outreach

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Published: October 24, 2026

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Semiconductor design traditionally takes place in quiet laboratories, centered around schematics, parasitic extraction tools, and computer simulations. However, as integrated circuits become more deeply embedded in everyday consumer products, the bridge between deep technical engineering and real-world application has never been more vital. For Preethi Ashwath, an integrated circuit designer at Analog Devices, building high-performance microchips and communicating their underlying mechanics go hand in hand.

Specializing in analog and mixed-signal domain design, Ashwath focuses on critical architectural components that ensure chip reliability and sensor accuracy. Her work spans across protection structures for complex microchips, biomedical hearables, low-latency audio transmission, and optical power delivery systems for emerging hardware platforms.

Safeguarding Human Knowledge: Inside Wikipedia's Engineering Approach to Detecting Synthetic AI Content

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Published: October 23, 2026

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As consumer generative artificial intelligence tools reached widespread adoption, open-access knowledge bases faced an unprecedented surge in synthetic content. Wikipedia, long established as a primary reference for human-curated information, quickly encountered a high volume of automated submissions. What began as scattered entries generated by early large language models rapidly evolved into a persistent data integrity challenge across multiple languages.

In response to this surge, community volunteers formed WikiProject AI Cleanup in late 2023. Led by contributors including Ellia Lebleu—a researcher pursuing a doctorate in artificial intelligence at France's Inria Center—the project focuses on auditing, flagging, and removing machine-written articles that fail to meet strict editorial criteria. This ongoing effort highlights a critical engineering problem in the modern internet ecosystem: preserving authentic data pipelines when synthetic data generation is cheap and ubiquitous.

Machine Learning vs. Synthetic Text: How Open Source Communities Keep Wikipedia Human-Written

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Published: October 22, 2026

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As generative artificial intelligence becomes deeply integrated into software workflows and web interfaces, open-access information repositories face a growing architectural challenge: maintaining data authenticity. Wikipedia, the world's largest open-source reference database, relies heavily on crowdsourced editorial oversight to preserve accuracy. To tackle the sudden surge of synthetic text entering the platform, community contributors established WikiProject AI Cleanup in late 2023—a dedicated task force aimed at identifying, reviewing, and filtering algorithmic content that breaches platform guidelines.

Detecting machine-written content is a moving target for data moderators. When consumer-grade large language models first gained mass adoption, synthetic contributions exhibited obvious, repetitive tropes. Early models frequently overused specific stylistic phrases, loaded paragraphs with overly grand adjectives, or inserted formulaic conclusion blocks titled with artificial prompts. These predictable patterns made initial heuristic filtering straightforward for human moderators.