Artificial Intelligence

The University of Texas at Austin is boosting its commitment to research and education in quantum science and engineering by establishing the Texas Quantum Institute.

A new National Institutes of Health-funded project by three scientists at The University of Texas at Austin and University of Rhode Island aims to shed light on real-world eating behaviors, using AI-enabled wearable technology.

When people learn things they should not know, getting them to forget that information can be tough. This is also true of rapidly growing artificial intelligence programs that are trained to think as we do, and it has become a problem as they run into challenges based on the use of copyright-protected material and privacy issues.

To respond to this challenge, researchers at The University of Texas at Austin developed what they say is the first "machine unlearning" method applied to image-based generative AI.

Texas Engineer Javad Mohammadi has dedicated his research to strengthening power grids, using artificial intelligence to make them more resistant to evolving threats.

The University of Texas at Austin is creating one of the most powerful artificial intelligence hubs in the academic world to lead in research and offer world-class AI infrastructure to a wide range of partners.

Artificial intelligence is quickly becoming embedded in many industries, and aerospace is no exception.

Texas Engineers are creating a new community to unite civil engineers, cyberinfrastructure professionals and experts in artificial intelligence to better understand and protect our virtual and physical infrastructure.

The University of Texas at Austin and Infleqtion, a global quantum technologies company, have signed a memorandum of understanding to develop a new center of excellence for quantum manufacturing. With the recent opening of its flagship corporate office in Austin, Infleqtion will work with UT’s Texas Institute for Electronics (TIE), collaborate with the University’s faculty experts in photonics and quantum technologies, and draw upon its world-class facilities to scale domestic manufacturing capacity for quantum-enabled products in areas such as energy, navigation, defense, and health care.

When new technology meets the real world, dynamic challenges threaten to derail progress, like a self-driving car that struggles to perceive rapid changes in the environment and adjust.

For decades, scientists have been investigating how to recreate the versatile computational capabilities of biological neurons to develop faster and more energy-efficient machine learning systems. One promising approach involves the use of memristors: electronic components capable of storing a value by modifying their conductance and then utilizing that value for in-memory processing.

How light interacts with matter is one of the most basic, yet important branches of science. A growing area in this field is nanophotonics, which studies these interactions at the smallest of scales where material building blocks begin to exhibit dynamic properties.

The landscape of buildings that populate cities around the world are both major contributors to greenhouse gas emissions, and potentially, significant suppliers of energy to the electrical grid. As a result, there is a movement around the world, across multiple industries, to better control building emissions and energy usage.

A competition created by researchers at The University of Texas at Austin challenges teams of engineers and scientists to deploy artificial intelligence to improve building energy consumption. Now in its third year, the CityLearn Challenge is the biggest it’s ever been, with more than 600 people from 50 countries, across roughly 100 teams, participating.

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.

The University of Texas at Austin and tech giant Cisco struck a research agreement that will begin with an emphasis on artificial intelligence and machine learning before branching out into additional technologies in the future. As part of the five-year partnership, Cisco Research will provide funding and expertise for four AI/ML research projects and one cybersecurity research project over the next year. The researchers from the Cockrell School of Engineering and College of Natural Sciences will delve into several different areas of AI and ML, including Internet of Things, computer vision, training learning networks and more.

The National Science Foundation just announced 11 new artificial intelligence institutes across the nation, and researchers from the Cockrell School of Engineering at The University of Texas at Austin will play prominent roles in two of them. UT Austin was already among the top universities in the world for AI, and its involvement in these new institutes bolsters its strength in this emerging area. Last year, the university was selected by NSF to lead an AI institute focused on machine learning, the technology that drives AI systems, enabling them to acquire knowledge and make predictions in complex environments.

Artificial intelligence and machine learning are among the trendiest technologies in the world right now, and The University of Texas at Austin is rapidly becoming a leader in advancing these concepts as they come to impact more aspects of our daily lives. UT’s AI/ML chops leveled up even further this year when Zhangyang “Atlas” Wang joined the Cockrell School after three years as an assistant professor at Texas A&M University. Wang’s research has garnered recognition from such luminaries in the field as Amazon and IBM.

Modern communications technology, regardless of use, relies on a similar formula: devices send signals and information through data centers, towers and satellites en route to their final destination. The effectiveness of the communication relies on how well that information travels, and there are a variety of factors that can slow down that journey – geography, weather and more.

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.

After graduating from UT Austin in 2010 with a master’s degree in electrical and computer engineering, Sowmiya Narayanan went on to co-found Lily AI — an AI-based software designed to enhance users’ online shopping experiences — and she now serves as its chief technology officer.

Bringing together experts in areas ranging from robotics to space, the Cockrell School of Engineering will host the U.S. Army’s annual Mad Scientist Conference on April 24-25 in the school’s Engineering Education and Research Center. The two-day event explored the individual and convergent impacts of technological innovations on the future of military operations, from present day through 2050.