Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be improved if we also employ empirical rigor that incorporates reproducibility guidelines, especially so in the medical imaging field. The Medical Imaging with Deep Learning (MIDL) conference has made advancements in this direction by advocating open access, and recently also recommending authors to make their code public - both aspects being adopted by the majority of the conference submissions. This helps the reproducibility of the methods, however, there is currently little or no support for further evaluation of these supplementary material, making them vulnerable to poor quality, which affects the impact of the entire submission. We have evaluated all accepted full paper submissions to MIDL between 2018 and 2022 using established, but slightly adjusted guidelines on reproducibility and the quality of the public repositories. The evaluations show that publishing repositories and using public datasets are becoming more popular, which helps traceability, but the quality of the repositories has not improved over the years, leaving room for improvement in every aspect of designing repositories. Merely 22% of all submissions contain a repository that were deemed repeatable using our evaluations. From the commonly encountered issues during the evaluations, we propose a set of guidelines for machine learning-related research for medical imaging applications, adjusted specifically for future submissions to MIDL.

The Conference on Neural Information Processing Systems, held in 2019 in Vancouver, Canada, is the largest in the discipline of artificial intelligence. Artificial intelligence (AI) researchers are hoping to use the tools of their discipline to solve a growing problem: how to identify and choose reviewers who can knowledgeably vet the rising flood of papers submitted to large computer science conferences. In most scientific fields, journals act as the main venues of peer review and publication, and editors have time to assign papers to appropriate reviewers using professional judgment. But in computer science, finding reviewers is often by necessity a more rushed affair: Most manuscripts are submitted all at once for annual conferences, leaving some organizers only a week or so to assign thousands of papers to a pool of thousands of reviewers. This system is under strain: In the past 5 years, submissions to large AI conferences have more than quadrupled, leaving organizers scrambling to keep up.

As machine learning algorithms and other artificial intelligence (AI) tools become increasingly widespread, some governments and institutions have started introducing regulations aimed at ensuring that they are ethically designed and implemented. Last year, for instance, the Neural Information Processing Systems (NeurIPS) conference introduced a new ethics-related requirement for all authors submitting AI-related research. Researchers at University of Oxford's Institute for Ethics in AI, the department of Computer Science and the Future of Humanity Institute have recently published a perspective paper that discusses the possible impact and implications of requirements such as the one introduced by the NeurIPS conference. This paper, published in Nature Machine Intelligence, also recommends a series of measures that may maximize these requirements' chance of success. "Last year, NeurIPS introduced a requirement that submitting authors include a broader impact statement in their papers," Carina E. Prunkl, one of the researchers who carried out the study, told TechXplore.

The Partnership on AI, a nonprofit group researching the uses of artificial intelligence, is calling for a softening of immigration laws and visa rules to make it easier for AI experts to travel around the world. The PAI's new policy paper, released today, addresses what it says is the impact of current restrictive visa laws and immigration rules on AI and machine learning technology development. It says these policies impede the ability of many students, researchers and industry practitioners to travel freely and, as a result, hurt the progress of AI research. "It is tremendously important to have international scholars be able to meet in person to discuss issues in technology ethics, especially in AI, which is transforming the world so rapidly," said Brian Green, director of technology ethics at the Markkula Center for Applied Ethics at Santa Clara University. "Visas have supported these meetings."

Our client hosts a large annual conference of 20,000 technical decision makers, IT professionals, and software developers from around the world. The conference includes over 700 sessions across multiple days that range from product demos to insights from industry leaders. Selected sessions from the annual event are repeated in smaller events in cities around the world. Each conference event generates a lot of feedback from attendees. The conference organizers analyze the feedback to determine whether each day was a success.

Dear Editor: ... May I also take this opportunity to praise the staff of the AI Magazine for a most informative and professional journal, and one which I find increasingly important for acquainting me with the latest progress in American research. I look forward to the continuing success of the Association in all its activities. Dear Sir, Yours sincerely, Marten E. Bennett Gzllingham, Kent, UK I would like to comment on something disturbing that appeared to be revealed at the recent I J C AI conference at Karlsruhe. The background to it is the "Marietta affair." At the industrial exhibition associated with the conference a Germany company, Marietta, was due to mount an exhibit.

List of sponsors of the 2015 IJCAI conference.

Constructing a good conference schedule for a large multi-track conference needs to take into account the preferences and constraints of organizers, authors, and attendees. Creating a schedule which has fewer conflicts for authors and attendees, and thematically coherent sessions is a challenging task. Cobi introduced an alternative approach to conference scheduling by engaging the community to play an active role in the planning process. The current Cobi pipeline consists of committee-sourcing and author-sourcing to plan a conference schedule. We further explore the design space of community-sourcing by introducing attendee-sourcing -- a process that collects input from conference attendees and encodes them as preferences and constraints for creating sessions and schedule. For CHI 2014, a large multi-track conference in human-computer interaction with more than 3,000 attendees and 1,000 authors, we collected attendees’ preferences by making available all the accepted papers at the conference on a paper recommendation tool we built called Confer, for a period of 45 days before announcing the conference program (sessions and schedule). We compare the preferences marked on Confer with the preferences collected from Cobi’s author-sourcing approach. We show that attendee-sourcing can provide insights beyond what can be discovered by author-sourcing. For CHI 2014, the results show value in the method and attendees’ participation. It produces data that provides more alternatives in scheduling and complements data collected from other methods for creating coherent sessions and reducing conflicts.