The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

The Archives Unleashed Project: Technology, Process, and Community to Improve Scholarly Access to Web Archives

The Archives Unleashed project aims to improve scholarly access to web archives through a multi-pronged strategy involving tool creation, process modeling, and community building -- all proceeding concurrently in mutually --reinforcing efforts. As we near the end of our initially-conceived three-year project, we report on our progress and share lessons learned along the way. The main contribution articulated in this paper is a process model that decomposes scholarly inquiries into four main activities: filter, extract, aggregate, and visualize. Based on the insight that these activities can be disaggregated across time, space, and tools, it is possible to generate "derivative products", using our Archives Unleashed Toolkit, that serve as useful starting points for scholarly inquiry. Scholars can download these products from the Archives Unleashed Cloud and manipulate them just like any other dataset, thus providing access to web archives without requiring any specialized knowledge. Over the past few years, our platform has processed over a thousand different collections from over two hundred users, totaling around 300 terabytes of web archives.This research was supported by the Andrew W. Mellon Foundation, the Social Sciences and Humanities Research Council of Canada, as well as Start Smart Labs, Compute Canada, the University of Waterloo, and York University. We’d like to thank Jeremy Wiebe, Ryan Deschamps, and Gursimran Singh for their contributions

Efficient Computation of Sparse Matrix Functions for Large-Scale Electronic Structure Calculations: The CheSS Library

We present CheSS, the “Chebyshev Sparse Solvers” library, which has been designed to solve typical problems arising in large-scale electronic structure calculations using localized basis sets. The library is based on a flexible and efficient expansion in terms of Chebyshev polynomials and presently features the calculation of the density matrix, the calculation of matrix powers for arbitrary powers, and the extraction of eigenvalues in a selected interval. CheSS is able to exploit the sparsity of the matrices and scales linearly with respect to the number of nonzero entries, making it well-suited for large-scale calculations. The approach is particularly adapted for setups leading to small spectral widths of the involved matrices and outperforms alternative methods in this regime. By coupling CheSS to the DFT code BigDFT, we show that such a favorable setup is indeed possible in practice. In addition, the approach based on Chebyshev polynomials can be massively parallelized, and CheSS exhibits excellent scaling up to thousands of cores even for relatively small matrix sizes.We gratefully acknowledge the support of the MaX (SM) and POP (MW) projects, which have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 676598 and 676553, respectively. This work was also supported by the Energy oriented Centre of Excellence (EoCoE), grant agreement number 676629, funded within the Horizon2020 framework of the European Union, as well as by the Next-Generation Supercomputer project (the K computer project) and the FLAGSHIP2020 within the priority study5 (Development of new fundamental technologies for high-efficiency energy creation, conversion/storage and use) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. We (LG, DC, WD, TN) gratefully acknowledge the joint CEA-RIKEN collaboration action.Peer ReviewedPostprint (author's final draft

“Where lies the grail? AI, common sense, and human practical intelligence”

The creation of machines with intelligence comparable to human beings—so-called "human-level” and “general” intelligence—is often regarded as the Holy Grail of Artificial Intelligence (AI) research. However, many prominent discussions of AI lean heavily on the notion of human-level intelligence to frame AI research, but then rely on conceptions of human cognitive capacities, including “common sense,” that are sketchy, one-sided, philosophically loaded, and highly contestable. Our goal in this essay is to bring into view some underappreciated features of the practical intelligence involved in ordinary human agency. These features of practical intelligence are implicit in the structure of our first-person experience of embodied and situated agency, deliberation, and human interaction. We argue that spelling out these features and their implications reveals a fundamental distinction between two forms of intelligence in action, or what we call “efficient task-completion” versus “intelligent engagement in activity.” This distinction helps us to see what is missing from some widely accepted ways of thinking about human-level intelligence in AI, and how human common sense is actually tied, conceptually, to the ideal of practical wisdom, or good (normative) judgment about how to act and live well. Finally, our analysis, if sound, also has implications for the important ethical question of what it means to have AI systems that are aligned with human values, or the so-called “value alignment” problem for artificial intelligence.info:eu-repo/semantics/publishedVersio

The Advocate

Headlines include: COMMISSIONER BRATTON ADDRESSES FORDHAM LAW: NYC HAS ROGUE COPS ; FLS\u27 Habitat For Humanity: Breaking New Groundhttps://ir.lawnet.fordham.edu/student_the_advocate/1109/thumbnail.jp

A First Amendment for Second Life: What Virtual Worlds Mean for the Law of Video Games

In the first decade of the twenty-first century, video games have finally taken their place alongside movies, comic books, and drawings as a form of protected First Amendment speech. Since the Seventh Circuit\u27s 2001 decision in American Amusement Machine Association v. Kendrick, court after court has struck down ordinances and statutes aimed at restricting violent video games--on the grounds that such violate game designers\u27 and players\u27 First Amendment speech rights. This series of rulings marks a stark change from courts\u27 previous stance on video games, which consigned them to the same realm of unprotected non-speech conduct as games like tennis, chess, or checkers. Video games were able to escape from this unprotected realm--and become First Amendment expression--largely because advances in computer graphics and design made them more and more like interactive movies and television shows, and less and less like digitized board games and pinball machines. But instead of simply forging ahead in this jurisprudential evolution, as video games evolve from personal forms of recreation to virtual worlds, this Article suggests that virtual worlds should make us rethink the First Amendment theory that got us to this point. This is because, while video games may have become First Amendment speech by becoming intricate movie-like stories, many virtual worlds are decidedly not scripted stories. They are rather stages for a multitude of expressive activity, some of which is an electronic analogue of the chess-playing, tennis-playing, car racing, or aimless lounging and wandering, that the courts excluded from the realm of First Amendment speech in an earlier era. This Article argues that this exclusion was a mistake. Virtual worlds are realms of First Amendment expression not because of the stories and role play they make possible, but rather because they provide a setting for giving form to imagination in sounds and imagery, a setting that can be walled off from the business of civil government and thus reserved for more unconstrained exercises of individual freedom. Stories and messages are an optional part of this setting and are not a necessary ingredient of First Amendment speech. This is not to say that government has no role to play in regulating virtual worlds: where individuals bring harm-threatening activity into virtual worlds involving acts that abuse others\u27 money or reputation, for example, government might have to regulate such worlds. But such regulation must take place alongside of, and not simply displace, the First Amendment\u27s application to virtual worlds

The Need for a System View to Regulate Artificial Intelligence/Machine Learning-Based Software as Medical Devices

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition