News

The Association for Computing Machinery, the primary professional organization in the field of computer science, has named two University of Texas at Austin professors — Peter Stone and Lizy Kurian John — as ACM Fellows. The award goes only to highly distinguished computer scientists representing the top 1% of ACM members.

At the age of four Amir Husain fell in love with computers — in his words, he "became obsessed" with them. In his teens he began writing to his heroes, computer scientists like Niklaus Wirth, who created the Pascal programming language, and Nicholas Negroponte, a pioneer in the study of how humans interact with computers. They all wrote back. Amir left his home of Lahore, Pakistan to attend The University of Texas at Austin 8,000 miles away, drawn by the work of one of UT's computer science labs. While at UT, he found a kindred spirit in his wife Zaib, who was also from Lahore and a student at the McCombs School of Business.

A methodical voice tells the watching crowd, "I am going to remove the lid." Everyone is quiet as a robot using artificial intelligence (AI) opens a trash can and lifts out a bag. Students and researchers hold their breath as it navigates across the floor of a mock house, avoiding obstacles to its destination. "Mission accomplished," it says. The trash has been taken out and the audience applauds. This is a huge achievement for the robot designed by a Texas Robotics team for the 2019 RoboCup competition.

We live in an increasingly digital era. Research shows that the average American checks their phone about 58 times daily, and spends an average of 4.5 hours a day on their phone. Without a doubt the amount of time the modern-day person spends on their phones has changed many aspects of how our society functions. For example, in the past decade we have seen a dramatic shift in forms of advertising. Companies are able to take note of people's patterns online and create personalized ads through the use of artificial intelligence (AI).

Evolutionary biologists never have enough time. Some of the most mysterious behaviors in the animal kingdom—like parenting—evolved over thousands of years, if not longer. Human lifespans are just too short to sit and observe such complex behaviors evolve. But computer scientists are beginning to offer clues by using artificial intelligence to simulate the life and death of thousands of generations of animals in a matter of hours or days. It's called computational evolution.

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. 

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?

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.