The Commonwealth Foundation said all entrants to the prize had avowed that their submissions were their own work. The Commonwealth Foundation said all entrants to the prize had avowed that their submissions were their own work. 'Obvious markers of AI': doubts raised over winner of short story prize Granta publisher says'perhaps we never will know' true authorship of work that won Commonwealth prize A few syntactical tics - and the verdict of an AI detection platform - have sparked a furore over the possibility that a short story given a prestigious literary award was written by AI. The foundation that awarded the prize and Granta, the magazine that published the winning story, said they had considered the allegations but had not reached a conclusion as to whether they were true. "It may be that the judges have now awarded a prize to an instance of AI plagiarism - we don't yet know, and perhaps we never will know," the publisher of Granta, Sigrid Rausing, said.

In this supplementary material, we provide more details regarding implementation details in Appendix B, more analysis of ERDA in Appendix C, full experimental results in Appendix D, and studies on parameters in Appendix E.

Contemporary coding education often presents students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games. While pedagogically compelling, there are no contemporary autonomous methods for providing feedback. Notably, interactive programs are impossible to grade by traditional unit tests.

This paper introduces SOAR: Spilling with Orthogonality-Amplified Residuals, a novel data indexing technique for approximate nearest neighbor (ANN) search. SOAR extends upon previous approaches to ANN search, such as spill trees, that utilize multiple redundant representations while partitioning the data to reduce the probability of missing a nearest neighbor during search. Rather than training and computing these redundant representations independently, however, SOAR uses an orthogonality-amplified residual loss, which optimizes each representation to compensate for cases where other representations perform poorly. This drastically improves the overall index quality, resulting in state-of-the-art ANN benchmark performance while maintaining fast indexing times and low memory consumption.

Large conferences such as NeurIPS and AAAI serve as crossroads of various AI fields, since they attract submissions from a vast number of communities. However, in some cases, this has resulted in a poor reviewing experience for some communities, whose submissions get assigned to less qualified reviewers outside of their communities. An often-advocated solution is to break up any such large conference into smaller conferences, but this can lead to isolation of communities and harm interdisciplinary research.

Simulation agents are controlled objects that perform realistic behaviors in a virtual world.

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