News

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.

Texas Wireless Summit (TWS) 2016: Reshaping Wireless Through Automated Vehicles, recently announced their speaker line-up and other exciting special reveals for this year’s event. On October 18, 2016, TWS will explore how automated vehicles will reshape wireless over the next 10 years with their demands for coordinated sensing and decision-making. The event will look at the benefits and requirements of connectivity, the key challenges of vehicle sensing and shared data analytics, including lightly processed radar, lidar and camera data.