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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.

There's an (albeit cliché) saying that says that two heads are better than one. Unsurprisingly, this idiom extends to artificial agents. In the field of AI, researchers have been working to understand how to make independent agents, who may have different goals, work together in an environment to complete a shared task. Three researchers in the Department of Computer Science, graduate student Ishan Durugkar, recent doctoral alumnus Elad Liebman, and professor Peter Stone, have been working to solve this problem. 

The University of Texas at Austin's Scott Aaronson is an initial member of a new multi-institution collaboration called the NSF Quantum Leap Challenge Institute for Present and Future Quantum Computation. The institute will work to overcome scientific challenges to achieving quantum computing and will design advanced, large-scale quantum computers that employ state-of-the-art scientific algorithms developed by the researchers.

Imagine that you are a robot in a hospital: composed of bolts and bits, running on code, and surrounded by humans. It's your first day on the job, and your task is to help your new human teammates—the hospital's employees—do their job more effectively and efficiently. Mainly, you're fetching things. You've never met the employees before, and don't know how they handle their tasks. How do you know when to ask for instructions? At what point does asking too many questions become disruptive?

A team of interdisciplinary researchers has discovered a new technique to store information in DNA – in this case "The Wizard of Oz," translated into Esperanto – with unprecedented accuracy and efficiency. The technique harnesses the information-storage capacity of intertwined strands of DNA to encode and retrieve information in a way that is both durable and compact. The technique is described in a paper in this week's Proceedings of the National Academy of Sciences.

University of Texas at Austin computer science researcher Kristen Grauman was selected as a finalist for the 2020 Blavatnik National Awards for Young Scientists.

The datasets used by many software applications can be represented as graphs, defined by sets of vertices and edges. These graphs are rich with useful information, and can be used to determine patterns and relationships among the stored data. This process of discovering relevant patterns from graphs is called Graph Pattern Mining. A team of Texas Computer Science (TXCS) researchers advised by Dr. Keshav Pingali has done groundbreaking work to make these programs more efficient and accessible. Their work was recently accepted to Very Large Databases (VLDB) 2020, one of the premier conferences in computer science.

In response to high demand for professionals with scientific and technical training to understand and work with massive amounts of data, The University of Texas at Austin is set to launch a new online master's degree program in data science. Pending final approval by UT System and the Texas Higher Education Coordinating Board, the new program will be a collaboration between the Department of Computer Science, ranked among the top 10 programs in the country by U.S. News and World Report; the Department of Statistics and Data Sciences, one of the university's newest and fastest-growing departments; and online learning company edX.

Texas Computer Science graduate students Garrett Bingham and William Macke, under the advisement of professor Risto Miikkulainen, are contributing to the improvement of AI with their research. Their paper, entitled "Evolutionary Optimization of Deep Learning Activation Functions" was accepted into the 2020 Genetic and Evolutionary Computation Conference. The work concerns the evolutionary optimization of activation functions as a potential means of improving neural networks, which may ultimately lead to the creation of smarter and more accurate AI.

The promise of artificial intelligence to solve problems in drug design, discover how babies learn language, and make progress in many other areas has been stymied by the inability of humans to understand what's going on inside AI systems.

Joydeep Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL) at UT, where he and other researchers work on building mobile service robots that assist humans in everyday environments. The lab investigates programs and algorithms that enable these robots to better navigate changing conditions, incorporate human assistance and recover from failures intelligently.

A powerful new approach to securing web browsers, using a tool called WebAssembly, is getting its first real-world application in the Firefox browser. Developed by a team of researchers from The University of Texas at Austin, the University of California San Diego, Stanford University and Mozilla, the approach shifts some of the browser code into "secure sandboxes" that prevent malicious code from taking over the user's computer.

Infectious disease researchers at The University of Texas at Austin and other institutions in Hong Kong, mainland China and France have concluded there is a high probability that the deadly Wuhan coronavirus spread beyond Wuhan and other quarantined cities before Chinese officials were able to put a quarantine in place. At least 128 cities in China outside of the quarantine zone, including cities with no reported cases to date, had a greater than even risk of exposure, according to a paper currently in press with Emerging Infectious Diseases, a journal of the U.S. Centers for Disease Control and Prevention.

Education is powerful — it's a means to inciting progress in the world, empowering individuals and transforming lives. Education's role in facilitating positive change is a major theme in Texas Computer Science student Dora Gurfinkel's life. She's so aware of it, in fact, that she is using her education to elicit real change within the university while paying homage to her family's unique history.

Texas Computer Science professor Scott Aaronson has been named as a 2019 Association for Computing Machinery (ACM) Fellow. ACM is the world's largest computing society and is dedicated to advancing the field. Each year, the organization honors members that have made a significant contribution to the field of computing and information technology.