The newly formed UT ラーメンベット 出金 スピード has awarded its first round of ラーメンベット 出金 スピード gifts and fellowships to scientists and engineers across three schools and colleges at the Forty Acres.
News archive ラーメンベット 入金, Thehubis the sixth such alliance between the tech company and a leading university. It aims to advance ラーメンベット 出金 スピード that prompts new discoveries addressing significant challenges and leads to solutions that benefit society.
One of the main ways it’s doing that is through providing ラーメンベット 出金 スピード gifts to faculty working in areas of joint interest and supporting doctoral graduate student through fellowships.
“Already the new UT Austin-Amazon Science Hub is serving its grand purpose – to help start new, innovative, and sometimes risky ラーメンベット 出金 スピード propositions, as well as solving problems that are critical to the workflows that occur throughout industry,” said Al Bovik, director of the Science Hub and professor in the ラーメンベット 禁止ゲーム of Engineering’s Chandra Family Department of Electrical and Computer Engineering. “These projects all tackle important problems in next-generation technology. Most importantly, they are engaging some of our best students and faculty on problems of great engineering interest, through direct collaborations with leading-edge industry teams.”
All three of the first round of awards are focused on artificial intelligence, specifically language processing. Here are the first round of ラーメンベット 出金 スピード funding awards:
Electrical and computer engineering faculty members Sujay Sanghavi and Alex Dimakis and Ph.D. ラーメンベット 出金 スピード Georgios Smyrnis, will receive funding for work on ラーメンベット 出金 スピード related to CLIP models. This type of artificial intelligence focuses on identifying visual information and describing it in text. They hope to create smaller models that don’t take up as much computing bandwidth that can combine to match the capabilities of larger models.
Assistant professor Greg Durrett and Ph.D. ラーメンベット 出金 スピード Juan Diego Rodriguez from the College of Natural Sciences' Department of Computer Science are working on large language models, powerful models like ChatGPT that can perform a variety of tasks. Their ラーメンベット 出金 スピード for this project focuses in particular on fact-checking statements generated by these models to make sure they are accurate. This is particularly challenging as these models can produce long responses, requiring algorithms that can systematically check individual statements to identify errors.
Ajay Jaiswal, a Ph.D. student in the School of Information, received a ラーメンベット 出金 スピード fellowship from Amazon. His work also focuses on addressing several fundamental bottlenecks (training, transfer, inference efficiency, and scalability, etc.) for modern-day neural networks (especially large foundational models). Through the fellowship, he will take a “big-little” approach to making these highly expensive and compute-heavy models (big) more accessible. Like Sanghavi and Dimakis, he wants to shrink the footprint of these types of AI (little), and to do that, he will focus on a more efficient design of the algorithms.
As part of the mission of the hub, community and knowledge exchange will take place through multiple annual events. The first awardees will have an opportunity to share their work later this year as part of the next UT ラーメンベット 出金 スピード event on campus.
