Intelligent Systems

For the first time in over 50 years, an American spacecraft has landed on the Moon. Overnight, the lunar lander Odysseus—called Odie for short—became a household name.

The University of Texas at Austin has introduced what is believed to be the first undergraduate robotics program in the country that allows high school students to apply directly as part of their first-year admissions application.

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.

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 agreement with NASA paves the wave for researchers from The University of Texas at Austin and the Texas Spacecraft Laboratory to build and launch a small satellite network with embedded machine learning technology to help spacecraft figure out their location in relationship to the Earth and, eventually, the moon. That will ultimately help ensure more accurate navigation and landing.

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.

In one of the first studies of its kind, several people with motor disabilities were able to operate a wheelchair that translates their thoughts into movement.

The study by researchers at The University of Texas at Austin and published today in the journal iScience is an important step forward for brain-machine interfaces — computer systems that turn mind activity into action. The concept of a thought-powered wheelchair has been studied for years, but most projects have used non-disabled subjects or stimuli that leads the device to more or less control the person rather than the other way around.

In recent years, more vehicles include partially autonomous driving features, such as blind spot detectors, automatic braking and lane sensing, that are said to increase safety. However, a recent study by researchers from The University of Texas at Austin finds that some of that safety benefit may be offset by people driving more, thereby clogging up roads and exposing themselves to more potential crashes.

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.

Our homes and offices are only as solid as the ground beneath them. When that solid ground turns to liquid — as sometimes happens during earthquakes — it can topple buildings and bridges. This phenomenon is known as liquefaction, and it was a major feature of the 2011 earthquake in Christchurch, New Zealand, a magnitude 6.3 quake that killed 185 people and destroyed thousands of homes.

Texas Engineers have turned to the research community to help solve a complicated energy problem, one that is likely top of mind for many Texans after the February storms that knocked out power for millions. The CityLearn Challenge, now in its second edition, tasks research teams from around the globe with creating control systems for virtual neighborhoods composed of nine buildings to best maximize energy efficiency. The teams must program an "intelligent agent" that uses a machine learning model called reinforcement learning, a way of training machine learning models to make a sequence of decisions with a specific goal or reward in mind and a trial-and-error component. Participating teams’ intelligent agents must make energy usage decisions with a goal of reducing the load on the grid during times of high demand.

Researchers at the Cockrell School of Engineering have, for the first time, applied a machine learning algorithm to measure the surface roughness of different types of land with a high level of detail.  The team used a type of satellite imagery that is more dependable and easier to capture than typical optical photographs but also more challenging to analyze. And they are working to integrate this data into storm surge models to give a clearer picture of what will happen during major weather events.

Researchers at The University of Texas at Austin are studying a magnetic device that breaks down key barriers for how information is stored and processed as part of the quest to make computers think more like humans. Jean Anne Incorvia, an assistant professor in the Cockrell School's Department of Electrical and Computer Engineering, recently published a trio of papers seeking to solve challenges facing these new computing paradigms.

Texas Engineering alumnus Chetan Kapoor, who leads R&D at Yaskawa American, Motoman Robotics Division, spearheaded a new partnership initiative between Yaskawa and UT Austin with the gift of a dual-arm SDA10F robot that will be available to Texas Robotics faculty. The partnership aims to facilitate interdisciplinary research and innovation within the robotics community in the university.