UT Austin启动了利用数据革命的研究所-新东方前途出国

　　Submitted by Spoorthi Krishnaraj on Fri, 11/01/2019 - 4:18pm 

 

　　Research from UT professors and TRIPODS members Alex Dimakis and Eric Price shows that it is possible to learn a deep generative model that dreams images of human faces (right panel), trained by observing only occluded images (left panel). The middle panel shows a previous approach for solving this problem, that fails. [Figure from: AmbientGAN: Generative models from lossy measurements, by A. Bora, E. Price and A.G. Dimakis, ICLR 2018.] 

　　来自UT教授和TRIPODS成员Alex Dimakis和Eric Price的研究表明，学习一种深度生成模型是可能的，该模型可以实现对人脸图像的做梦（右面板），通过只观察遮挡图像（左面板）来训练。中间面板显示了以前解决此问题的方法，但失败了。[数据来源：AmbientGAN:A.Bora，E.Price和A.G.Dimakis的有损测量生成模型，ICLR 2018。]

　　Advances in machine learning are announced every day, but efforts to fundamentally rethink the core algorithms of AI are rare. 

　　机器学习的进步每天都在宣布，但从根本上重新思考人工智能核心算法的努力却很少。

　　The University of Texas at Austin announced this week that it has received a three-year, $1.5 million National Science Foundation TRIPODS (Transdisciplinary Research in Principles of Data Science) award to establish a new institute on the foundations of data science. The institute will coordinate foundational research in AI and data science across several university departments, launch a large-scale workshop and signature seminar series, and provide seed funding for a number of graduate and post-doctoral fellowships in artificial intelligence and machine learning. 

　　得克萨斯大学奥斯汀分校本周宣布，它已经获得了一个为期三年、价值150万美元的国家科学基金会三级（数据科学原理跨学科研究）奖，以建立一个新的数据科学研究所。该研究所将在多个大学部门协调人工智能和数据科学的基础研究，开展大型研讨会和签名研讨会系列，并为人工智能和机器学习中的一些研究生和博士后奖学金提供种子资金。

　　The grant is co-led by Adam Klivans (Computer Science), Sujay Sanghavi (Electrical and Computer Engineering), Purnamrita Sarkar (Statistics and Data Science), and Rachel Ward (Math/Oden Institute), and will involve additional faculty from these departments. 

　　这项拨款由Adam Klivans （计算机科学）、Sujay Sanghavi （电气和计算机工程）、Purnamrita Sarkar （统计和数据科学）和Rachel Ward（数学/奥登研究所）共同领导，并将包括这些系的其他教员。

　　“We are extremely excited to have successfully brought together experts from across four departments for this venture,” Sanghavi said. “Over the years UT has managed to amass significant talent in this important area, but it remains somewhat fragmented across organizational units. This TRIPODS institute will help centralize UT’s expertise in this area via a series of joint initiatives.” 

　　Sanghavi说：“我们非常高兴能成功地召集来自四个部门的专家参加这次冒险活动。”。“多年来，UT在这一重要领域积累了大量人才，但在整个组织单位中，它仍然有些支离破碎。这个三脚研究所将通过一系列的联合倡议，帮助集中UT在这一领域的专业知识。”

　　The award is the first phase of a multi-step nationwide process to build larger institutes for machine learning and AI, as part of NSF’s Harnessing the Data Revolution initiative. 

　　该奖项是建立大型机器学习和人工智能研究所的多步骤全国进程的第一阶段，是国家科学基金会利用数据革命倡议的一部分。

　　The impact of artificial intelligence and machine learning has exploded in business, science, and society, but many important fundamental questions remain. The Texas institute aims to address these along three themes: developing an algorithmic theory of deep learning; making machine learning robust; and improving graph-based applications. By looking at foundational approaches to analysis and design, the institute will form a central hub for theoretical machine learning and data science research. 

　　人工智能和机器学习的影响已经在商业、科学和社会中爆发，但许多重要的基本问题仍然存在。德州研究所的目标是围绕三个主题来解决这些问题：开发深度学习的算法理论；使机器学习变得健壮；以及改进基于图形的应用程序。通过研究分析和设计的基本方法，该研究所将成为理论机器学习和数据科学研究的中心枢纽。

　　Current machine learning methods produce models that are enormous and very hard to analyze. Developing a new science to better understand, and hence better train, such models is the first thrust area of this award. 

　　目前的机器学习方法产生的模型是巨大的，很难分析。发展一门新的科学来更好地理解，从而更好地训练，这样的模型是这个奖项的第一个重点领域。

　　“Training a machine learning model is computationally expensive and deciding how to set parameters is more of an art than a science,” Klivans explained. “There are no good mathematical underpinnings for how to set the dials and levers. For all we know, we are leaving a lot of performance on the table.” 

　　Klivans解释说：“训练机器学习模型在计算上很昂贵，决定如何设置参数更像是一门艺术，而不是一门科学。”。“对于如何设置刻度盘和杠杆，没有很好的数学基础。据我们所知，我们将留下很多表演。”

　　The second thrust is robustness, both to errors in the data and to deliberate adversarial manipulations. “The current model of relying on vast amounts of spotless training data is untenable in most applications. As people try to use machine learning models for high-stakes applications, this problem will be exacerbated by attempts to attack its reliability,” said Sanghavi. 

　　第二个要点是稳健性，既能防止数据中的错误，又能防止恶意操作。“目前依赖大量一尘不染的训练数据的模式在大多数应用中都是站不住脚的。当人们试图将机器学习模型用于高风险的应用时，试图攻击其可靠性会加剧这个问题。 

　　The UT team will design systems that are less-easily deceived. “Developing robust machine learning models will be key to their wide deployment,” said Klivans. 

　　UT团队将设计不易被欺骗的系统。“开发健壮的机器学习模型将是它们广泛应用的关键，”Klivans说。 

　　The final research thrust will explore graphs — a mathematical way of capturing interactions between individual units or nodes — and apply new approaches to questions in neuroscience, including searching for signs of Alzheimer’s, ADHD, or autism based on changes in brain connectivity. 

　　最后的研究方向是探索图形——一种捕捉个体单元或节点之间相互作用的数学方法——并将新方法应用于神经科学的问题，包括根据大脑连接的变化寻找阿尔茨海默氏症、多动症或自闭症的迹象。 

　　“A main focus will be on developing provable, scalable, and robust methods for large biological networks,” said Purnamrita Sarkar. “A very important part of developing a large-scale AI or ML systems is to quantify and estimate uncertainty, because without that, one can in fact make disastrous decisions in this era of big data.” 

　　Purnamita Sarkar说：“一个主要的焦点将是为大型生物网络开发可证明的、可扩展的和健壮的方法。”。“开发大规模人工智能或人工智能系统的一个非常重要的部分是量化和估计不确定性，因为没有这些，在这个大数据时代，人们实际上可以做出灾难性的决定。” 

　　UT has one of the most esteemed and widely-published faculty in the area of AI and machine learning. The award will give UT researchers time and space to work together on some of the most pressing fundamental problems in the field, and in the process make large gains in AI performance. The TRIPODS faculty will leverage the supercomputers at the Texas Advanced Computing Center (TACC) for building algorithms at scale. 

　　UT拥有人工智能和机器学习领域最受尊敬和广泛出版的教师之一。该奖项将给予联合技术研究人员时间和空间，共同解决该领域中一些最紧迫的基本问题，并在这一过程中取得人工智能性能的巨大进步。三脚架学院将利用德州高级计算中心（TACC）的超级计算机来大规模构建算法。 

　　Beyond the work of the individual researchers, the new institute will engage undergraduates, especially women and underrepresented minorities, in research; support the undergraduate Machine Learning and Data Science club; contribute to a new portfolio program in machine learning; and generally, create a more fertile environment for AI research on campus. 

　　除了个别研究人员的工作外，新研究所还将让本科生，特别是妇女和代表性不足的少数民族参与研究；支持本科生机器学习和数据科学俱乐部；为机器学习的新组合计划作出贡献；并为人工智能研究创造一个更为丰富的环境校园。 

　　“The PIs have a strong track record for engaging women and minorities in research. For example, the very successful Rising Stars Workshop for women graduates in computational and data sciences was successfully organized by one of the PIs at the Oden institute last year,” said Sarkar. “The new institute will contribute funding and participants to the upcoming 2020 Rising Stars conference as well as future years.” 

　　“PIs在让妇女和少数民族参与研究方面有着良好的记录。例如，去年由奥登研究所的一位PIs成功组织了一个非常成功的计算和数据科学女毕业生新星研讨会。“新的研究所将为即将到来的2020新星会议以及未来几年提供资金和参与者。” 

　　Successful TRIPODS projects will be eligible to apply for Phase II of the project which will provide $10 million awards to a handful of institutes. 

　　成功的三脚架项目将有资格申请该项目的第二阶段，该阶段将为少数研究所提供1000万美元的奖励。 

　　“Machine learning is changing the world and has become a core tool across all the sciences. I think future progress in science will be dependent on how well we as a community do machine learning," Klivans said. "This grant positions UT to take the lead on these challenges.” 

　　“机器学习正在改变世界，已经成为所有科学领域的核心工具。我认为未来科学的进步将取决于我们作为一个社区如何进行机器学习这项补助金使UT在这些挑战中处于领先地位。”

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