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Bertrand Braunschweig

Bio: Bertrand Braunschweig is an academic researcher from University of Tennessee. The author has contributed to research in topics: Computer science & Trustworthiness. The author has an hindex of 1, co-authored 1 publications receiving 705 citations.

Papers
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Journal ArticleDOI
01 Feb 2011
TL;DR: The work of the community to prepare for the challenges of exascale computing is described, ultimately combing their efforts in a coordinated International Exascale Software Project.
Abstract: Over the last 20 years, the open-source community has provided more and more software on which the world’s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

736 citations

Journal Article
TL;DR: This work presents the concrete approach taken by confiance.ai and the validation strategy based on real-world industrial use cases provided by the members, and discusses the walls of AI and their relation with safety.
Abstract: AI faces some « walls » towards which it is advancing at high pace. Apart from social and ethics consideration, there are walls on several subjects very dependent but gathering each some considerations from AI community, both for use, design and research: trust, safety, security, energy, human-machine cooperation, and « inhumanity ». Safety questions are an particularly important subjects for all of them. The Confiance.ai industrial program aims at solving some of these issues by developing seven interrelated projects that address these aspects from different viewpoints and integrate them in an engineering environment for AI-based systems. We will present the concrete approach taken by confiance.ai and the validation strategy based on real-world industrial use cases provided by the members. The walls of AI and their relation with safety Artificial intelligence is advancing at a very fast pace, both in terms of research and applications, and is raising societal questions that are far from being answered. But as it moves forward rapidly, it runs into what we call the five walls of AI, walls that it is likely to crash into if we don't take precautions. Any one of these five walls is capable of halting its progress, which is why it is essential to know what they are and to seek answers in order to avoid the so-called third winter of AI, a winter that would follow the first two in the years 197x and 199x, during which AI research and development came to a virtual standstill for lack of budget and community interest. The five walls are those of trust, energy, safety, human interaction and inhumanity. They each contain a number of ramifications, and obviously interact. Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). There are different opinions on this matter. The paper (Bengio et al. 2021) by Yoshua Bengio, Yann LeCun and Geoffrey Hinton, written after their collective Turing Award, provides insights into the future of AI through deep learning and neural networks without addressing the same topics; the 2021 progress report of Stanford's 100-year longitudinal study (Littman et al. 2021) examines AI advances to date and presents challenges for the future, very complementary to those we discuss here; the recent book by César Hidalgo (2021) looks at how humans perceive AI (and machines); the book "Human Compatible" by Stuart Russell (2019), is interested in the compatibility between machines and humans, a subject we treat differently when we talk about the interaction wall.

2 citations

Journal ArticleDOI
TL;DR: The first ever Symposium on the assessment of AI trustworthiness was held in 2013 as discussed by the authors , which led to the birth of a new research community on the subject of trustworthiness.
Abstract: We report about the first ever symposium on the assessment of AI trustworthiness, leading to the birth of a new research community on the matter.

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Journal ArticleDOI
TL;DR: This work reviews the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
Abstract: Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

660 citations

Journal ArticleDOI
01 May 2014
TL;DR: This report presents a report produced by a workshop on ‘Addressing failures in exascale computing’ held in Park City, Utah, 4–11 August 2012, which summarizes and builds on discussions on resilience.
Abstract: We present here a report produced by a workshop on 'Addressing failures in exascale computing' held in Park City, Utah, 4-11 August 2012. The charter of this workshop was to establish a common taxonomy about resilience across all the levels in a computing system, discuss existing knowledge on resilience across the various hardware and software layers of an exascale system, and build on those results, examining potential solutions from both a hardware and software perspective and focusing on a combined approach. The workshop brought together participants with expertise in applications, system software, and hardware; they came from industry, government, and academia, and their interests ranged from theory to implementation. The combination allowed broad and comprehensive discussions and led to this document, which summarizes and builds on those discussions.

406 citations

Journal ArticleDOI
TL;DR: This work unifies traditionally separated high-performance computing and big data analytics in one place to accelerate scientific discovery and engineering innovation and foster new ideas in science and engineering.
Abstract: Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics.

373 citations

Journal ArticleDOI
TL;DR: New opportunities in materials design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest are discussed.
Abstract: Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the design and realization of advanced functional materials. Here we discuss new opportunities in materials design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.

295 citations

Journal ArticleDOI
01 Feb 2013
TL;DR: This study considers multiphysics applications from algorithmic and architectural perspectives, where “algorithmic” includes both mathematical analysis and computational complexity, and “architectural’ includes both software and hardware environments.
Abstract: We consider multiphysics applications from algorithmic and architectural perspectives, where “algorithmic” includes both mathematical analysis and computational complexity, and “architectural” includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.

278 citations