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Over the past decade, the world has seen tremendous increases in the deployment of artificial intelligence (AI) technology. The main horsepower behind the success of AI systems is provided by deep learning models and machine learning (ML) algorithms. Recently, a new AI paradigm has emerged: Automated Machine Learning (AutoML) including its subfield Neural Architecture Search (NAS). State-of-the-art ML models consist of complex workflows with numerous design choices and variables that must be tuned for optimal performance.

Significant advances in artificial intelligence over the past decade have relied upon extensive training of algorithms using massive, open-source databases. But when such datasets are used “off label” and applied in unintended ways, the results are subject to machine learning bias that compromises the integrity of the AI algorithm, according to a new study by researchers at The University of Texas at Austin and the University of California, Berkeley.

WNCG professor Atlas Wang has received several grants for his work on artificial intelligence. 

The National Science Foundation has selected The University of Texas at Austin to lead the NSF AI Institute for Foundations of Machine Learning, bolstering the university’s existing strengths in this emerging field. Machine learning is the technology that drives AI systems, enabling them to acquire knowledge and make predictions in complex environments. This technology has the potential to transform everything from transportation to entertainment to health care.

With Big Data playing a role in the lives of companies and individuals across the globe, and data being collected on everything from apps to electronic health records to parking meters, society debates how best to use this mass of information. “The stormy sea of Big Data can lead to data indigestion,” WNCG Associate Director Prof. Constantine Caramanis states. “We are interested in the application of data for engineering problems, from petroleum to health to recommendation engines.”

With Big Data playing a role in the lives of companies and individuals across the globe, and data being collected on everything from apps to electronic health records to parking meters, society debates how best to use this mass of information. “The stormy sea of Big Data can lead to data indigestion,” WNCG Associate Director Prof. Constantine Caramanis states. “We are interested in the application of data for engineering problems, from petroleum to health to recommendation engines.”

There is a new app on the market that encourages users to go nuts over healthcare, complete with a squirrely mascot. Created by the founders of Accordion Health and dubbed the “health nuts,” the two new apps, Pistachio and Chestnut, bring medical care back under the control, and into the palms of, users and patients.