Executive Summary The UT-GraphCast Hindcast Dataset (1979-2024) is a comprehensive global weather forecast archive generated using the Google DeepMind GraphCast Operational model. Developed by researchers at The University of Texas at Austin and published under the WCRP umbrella, this dataset provides daily 15 day deterministic forecasts at 00 UTC on a 0.25 0.25 global grid ( 25 km) for a 45-year period. It predicts more than a dozen key atmospheric and surface variables on 37 vertical levels, delivering a full medium-range forecast in under one minute on modern hardware. This new hindcast archive enables retrospective studies of historical weather, climate variability, and extreme events with unprecedented spatial and temporal detail. Preliminary validation shows that GraphCast forecasts generally reproduce ERA5 conditions with high fidelity and skill comparable or superior to conventional numerical models up to 10-15 days. In particular, GraphCast is known to outperform the state-of-the-art ECMWF IFS High-Resolution model (HRES) [Lam et al., 2023] on most verification targets, and to predict severe events (e.g., tropical cyclones, atmospheric rivers, heatwaves) with excellent accuracy. These benchmarks suggest that the GraphCast hindcast will be a valuable tool for climate and weather research.

We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin. In response to a call for the rapid deployment of class to serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community members outside of the University, were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience.

E. Allen Emerson was the first graduate student of Edmund M. Clarke at Harvard University. After discussing several ideas for Allen's dissertation, they identified a promising candidate: verifying a finite-state system against a formal specification. According to Martha Clarke, Edmund's widow, it was during a walk across Harvard Yard that they decided to call it "model checking." Emerson received his Ph.D. in applied mathematics for this work in 1981. Twenty-five years later, he and Clarke (along with Joseph Sifakis) shared the ACM A.M. Turing Award in 2007 for this and related work.

A new partnership between the University of Texas at Austin (UT Austin) and the MITRE Corporation is aiming to accelerate innovative ethical artificial intelligence (AI) research by interdisciplinary researchers involved in UT Austin’s Good Systems research grand challenge.  MITRE is a nonprofit dedicated to “solving problems for a safer world.” Minimizing Potential Adverse Effects of […]

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. Researchers at six universities, including The University of Texas at Austin, are launching a partnership aimed at turning these AI "black boxes" into human-interpretable computer code, allowing them to solve hitherto unsolvable problems. The new partnership, called Understanding the World Through Code, is made possible by a major new grant from the National Science Foundation, through its Expeditions in Computing program. This initiative is focused on "ambitious fundamental research agendas that promise to define the future of computing and information." These grants, given to just three teams every two years, are the largest given by the NSF's Directorate for Computer and Information Science and Engineering.

I have some exciting news (for me, anyway). Starting next week, I'll be going on leave from UT Austin for one year,to work at OpenAI. They're the creators of the astonishing GPT-3 and DALL-E2, which have not only endlessly entertained me and my kids, but recalibrated my understanding of what, for better and worse, the world is going to look like for the rest of our lives. Working with an amazing team at OpenAI, including Jan Leike, John Schulman, and Ilya Sutskever, my job will be think about the theoretical foundations of AI safety and alignment. What, if anything, can computational complexity contribute to a principled understanding of how to get an AI to do what we want and not do what we don't want?

In brief A synthetic enzyme designed using machine-learning software can break down waste plastics in 24 hours, according to research published in Nature. Scientists at the University of Texas Austin studied the natural structure of PETase, an enzyme known to degrade polymer chains in polyethylene. Next, they trained a model to generate mutations of the enzyme that work fast at low temperatures, let the software loose, and picked from the output a variant they named FAST-PETase to synthesize. FAST stands for functional, active, stable, and tolerant. FAST-PETase, we're told, can break down plastic in as little as 24 hours at temperatures between 30 and 50 degrees Celsius.

On April 1, UT Austin's Machine Learning Lab held its inaugural public lecture, "AI For Accurate and Fair Imaging" with Alex Dimakis. The talk covered recent research into bias in AI imaging algorithms. In 2020, a deep learning generative model with groundbreaking performance was posted on the web. The model could turn low-resolution images to high-quality photos. A user uploaded a low-resolution image of President Obama and obtained an image that is now referred to as'White Obama'.

Citizen scientists have helped researchers discover new types of galaxies, design drugs to fight COVID-19, and map the bird world. The term describes a range of ways that the public can meaningfully contribute to scientific and engineering research, as well as environmental monitoring. As members of the Computing Community Consortium (CCC) recently argued in a Quadrennial Paper, "Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research," non-scientists can help advance science by "providing or analyzing data at spatial and temporal resolutions or scales and speeds that otherwise would be impossible given limited staff and resources." Recently, citizen scientists' efforts have found a new purpose: helping researchers develop machine learning models, using labeled data and algorithms, to train a computer to solve a specific task. This approach was pioneered by the crowdsourced astronomy project Galaxy Zoo, which started leveraging citizen scientists in 2007.

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. An upside of the Christchurch quake was that it was one of the most well-documented in history. Because New Zealand is seismically active, the city was instrumented with numerous sensors for monitoring earthquakes.