A professor says AI chatbot software, such as ChatGPT, could restructure postsecondary education by replacing some textbooks and promoting critical thinking. New York City is reversing course after restricting the use of OpenAI's artificial intelligence chatbot ChatGPT in public schools. David Banks, the chancellor of the Big Apple's school system, announced the shift in a Thursday op-ed in Chalkbeat. He said that while the technology had initially caught educators off guard, the school system is now determined to embrace its potential. "While initial caution was justified, it has now evolved into an exploration and careful examination of this new technology's power and risks," Banks explained.

In each case, the students confessed to using such systems and agreed to rewrite the assignments themselves. With all the panic about how students might use these systems to get around the burden of actually learning, we often forget that as of 2023, the systems don't work well at all. It was easy to spot these fraudulent essays. They used text that did not respond to the prompt we had issued to students. Or they just sounded unlike what a human would write.

During an appearance on "The Ingraham Angle", Jimmy Failla shares his thoughts on the latest interesting development in the world of artificial intelligence. A group of graduating seniors at Texas A&M University were temporarily denied their diplomas after a ChatGPT bot claimed to have written their papers. Per multiple reports, animal science professor Jared Mumm told his class he would be giving them incomplete grades after running their essays through a chatbot that asserted to have written all the papers. That assertion was later determined to be not entirely accurate. The school said no students ultimately failed the class or were barred from graduating because of this issue.

Modern approaches for keyword spotting rely on training deep neural networks on large static datasets with i.i.d. distributions. However, the resulting models tend to underperform when presented with changing data regimes in real-life applications. This work investigates a simple but effective online continual learning method that updates a keyword spotter on-device via SGD as new data becomes available. Contrary to previous research, this work focuses on learning the same KWS task, which covers most commercial applications. During experiments with dynamic audio streams in different scenarios, that method improves the performance of a pre-trained small-footprint model by 34%. Moreover, experiments demonstrate that, compared to a naive online learning implementation, conditional model updates based on its performance in a small hold-out set drawn from the training distribution mitigate catastrophic forgetting.

Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accelerate this process while achieving unprecedented levels of accuracy, but scaling this approach up to higher resolutions relevant for future detector upgrades leads to prohibitive memory constraints. To overcome this problem, we introduce Inductive CaloFlow (iCaloFlow), a framework for fast detector simulation based on an inductive series of normalizing flows trained on the pattern of energy depositions in pairs of consecutive calorimeter layers. We further use a teacher-student distillation to increase sampling speed without loss of expressivity. As we demonstrate with Datasets 2 and 3 of the CaloChallenge2022, iCaloFlow can realize the potential of normalizing flows in performing fast, high-fidelity simulation on detector geometries that are ~ 10 - 100 times higher granularity than previously considered.

This paper looks at the ability of large language models to participate in educational guided reading. We specifically, evaluate their ability to generate meaningful questions from the input text, generate diverse questions both in terms of content coverage and difficulty of the questions and evaluate their ability to recommend part of the text that a student should re-read based on the student's responses to the questions. Based on our evaluation of ChatGPT and Bard, we report that, 1) Large language models are able to generate high quality meaningful questions that have high correlation with the input text, 2) They generate diverse question that cover most topics in the input text even though this ability is significantly degraded as the input text increases, 3)The large language models are able to generate both low and high cognitive questions even though they are significantly biased toward low cognitive question, 4) They are able to effectively summarize responses and extract a portion of text that should be re-read.

Disaster summarization approaches provide an overview of the important information posted during disaster events on social media platforms, such as, Twitter. However, the type of information posted significantly varies across disasters depending on several factors like the location, type, severity, etc. Verification of the effectiveness of disaster summarization approaches still suffer due to the lack of availability of good spectrum of datasets along with the ground-truth summary. Existing approaches for ground-truth summary generation (ground-truth for extractive summarization) relies on the wisdom and intuition of the annotators. Annotators are provided with a complete set of input tweets from which a subset of tweets is selected by the annotators for the summary. This process requires immense human effort and significant time. Additionally, this intuition-based selection of the tweets might lead to a high variance in summaries generated across annotators. Therefore, to handle these challenges, we propose a hybrid (semi-automated) approach (PORTRAIT) where we partly automate the ground-truth summary generation procedure. This approach reduces the effort and time of the annotators while ensuring the quality of the created ground-truth summary. We validate the effectiveness of PORTRAIT on 5 disaster events through quantitative and qualitative comparisons of ground-truth summaries generated by existing intuitive approaches, a semi-automated approach, and PORTRAIT. We prepare and release the ground-truth summaries for 5 disaster events which consist of both natural and man-made disaster events belonging to 4 different countries. Finally, we provide a study about the performance of various state-of-the-art summarization approaches on the ground-truth summaries generated by PORTRAIT using ROUGE-N F1-scores.

Online social media platforms, such as Twitter, are one of the most valuable sources of information during disaster events. Therefore, humanitarian organizations, government agencies, and volunteers rely on a summary of this information, i.e., tweets, for effective disaster management. Although there are several existing supervised and unsupervised approaches for automated tweet summary approaches, these approaches either require extensive labeled information or do not incorporate specific domain knowledge of disasters. Additionally, the most recent approaches to disaster summarization have proposed BERT-based models to enhance the summary quality. However, for further improved performance, we introduce the utilization of domain-specific knowledge without any human efforts to understand the importance (salience) of a tweet which further aids in summary creation and improves summary quality. In this paper, we propose a disaster-specific tweet summarization framework, IKDSumm, which initially identifies the crucial and important information from each tweet related to a disaster through key-phrases of that tweet. We identify these key-phrases by utilizing the domain knowledge (using existing ontology) of disasters without any human intervention. Further, we utilize these key-phrases to automatically generate a summary of the tweets. Therefore, given tweets related to a disaster, IKDSumm ensures fulfillment of the summarization key objectives, such as information coverage, relevance, and diversity in summary without any human intervention. We evaluate the performance of IKDSumm with 8 state-of-the-art techniques on 12 disaster datasets. The evaluation results show that IKDSumm outperforms existing techniques by approximately 2-79% in terms of ROUGE-N F1-score.

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.

Due to the poor bandwidth throttling especially global server and cooperatively train one model on the global server, it adopts multiple local training with privacy protection. Due to the multiple local and partial participation to mitigate the communication bottleneck updates and the isolated non-iid dataset, clients to a greater extent. With extensive studies of FL, are prone to overfit into their own optima, which theoretical analysis reveals that the major influence on limiting extremely deviates from the global objective and the performance of FL is client drifts, whose essence significantly undermines the performance. Most is that the inconsistent local optima deviate from the global previous works only focus on enhancing the consistency objective on the heterogeneous dataset (Karimireddy et al., between the local and global objectives 2020; Woodworth et al., 2020; Li et al., 2020b; Kairouz to alleviate this prejudicial client drifts from the et al., 2021). Yang et al. (2021) theoretically demonstrate perspective of the optimization view, whose performance that the performance of the classical FedAvg method suffers would be prominently deteriorated on from the length of local updates and the number of the high heterogeneity. In this work, we propose a partial participation multiplied by the constant upper bound novel and general algorithm FedSMOO by jointly of the variance of the heterogeneous gradient, which contributes considering the optimization and generalization as the dominant term of the convergence rate. This targets to efficiently improve the performance in divergence would be extremely multiplied by both increasing FL. Concretely, FedSMOO adopts a dynamic regularizer the local interval and reducing the participation ratio.