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The Army Research Lab has tapped the University of Maryland for an ambitious effort to integrate data science and engineering on a sweeping array of projects, ranging from a “smart nose” to sniff out hazardous materials to lightning-quick “fingerprinting” of cell phones and other radio-emitting devices. ARL, part of the U.S. Army Combat Capabilities Development Command, recently awarded the A. James Clark School of Engineering a five-year, $78.2 million cooperative agreement to spearhead the Data Driven Engineering Research (DataDrivER) program.

In recent years, artificial intelligence (AI) has made tremendous strides thanks to advances in machine learning and growing pools of data to learn from. Large language models (LLMs) and their derivatives, such as OpenAI’s ChatGPT and Google’s BERT, can now generate material that is increasingly similar to content created by humans. As a result, LLMs have become popular tools for creating high-quality, relevant and coherent text for a range of purposes, from composing social media posts to drafting academic papers.

Diagnosing and treating gastrointestinal tract diseases can be notoriously invasive and time-consuming: blood and stool lab work; biopsies, colonoscopies and endoscopies; and X-rays, CT scans and MRI imaging. But what if there was an alternative as simple as popping a Bayer aspirin?