Artificial Intelligence

A new partnership between industrial technology leader Emerson and The University of Texas at Austin will support advanced research into artificial intelligence, automation, energy, semiconductors and more.

In the quest to bring the next evolution of computing to the masses — electronics that can think like the human brain — researchers from The University of Texas at Austin have achieved several key milestones.

Civil engineer Krishna Kumar has developed various outreach initiatives to engage young students in learning about AI.

A team led by scientists and engineers at The University of Texas at Austin created a new “thermal interface material” that could organically remove heat from high-powered electronic devices, reducing or even eliminating the need for extensive cooling of data centers and other electronic devices.

A global artificial intelligence challenge led by Texas Engineers Zoltan Nagy and Javad Mohammadi has been recognized by the Climate Change AI Innovation (CCAI) Grants program.

This fall, UT hosted several robotics events, with experts from around the world converging on the Forty Acres to discuss the future of the field.

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.

Artificial intelligence could be the defining technology of our time. Texas Engineers are hard at work refining and improving the technology, imagining new ways to deploy AI to solve important problems and putting up guardrails to protect users — and the technology itself.

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.