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Industry analysts say that more data has been collected in the past two years than in all of human history combined. Data about what we buy, what we watch, where we go and who our friends are is constantly being collected and stored. Analyzing all that data and gaining insights from it is the hard part.

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.

Many of the decisions we make are now guided by computational simulations, from designing new spacecraft to predicting the spread of a pandemic. But it's not enough for a simulation model to just issue predictions. A decision-maker needs to know just how much those predictions can be trusted.

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.