Background Artificial intelligence (AI) is evolving in the medical education system. ChatGPT, Google Bard, and Microsoft Bing are AI-based models that can solve problems in medical education. However, the applicability of AI to create reasoning-based multiple-choice questions (MCQs) in the field of medical physiology is yet to be explored. Objective We aimed to assess and compare the applicability of ChatGPT, Bard, and Bing in generating reasoning-based MCQs for MBBS (Bachelor of Medicine, Bachelor of Surgery) undergraduate students on the subject of physiology. Methods The National Medical Commission of India has developed an 11-module physiology curriculum with various competencies. Two physiologists independently chose a competency from each module. The third physiologist prompted all three AIs to generate five MCQs for each chosen competency. The two physiologists who provided the competencies rated the MCQs generated by the AIs on a scale of 0-3 for validity, difficulty, and reasoning ability required to answer them. We analyzed the average of the two scores using the Kruskal-Wallis test to compare the distribution across the total and module-wise responses, followed by a post-hoc test for pairwise comparisons. We used Cohen's Kappa () to assess the agreement in scores between the two raters. We expressed the data as a median with an interquartile range. We determined their statistical significance by a p-value
Ashish Aggarwal earned his B.Tech in Computer Science and Engineering from the Jaypee University of Information Technology, India before earning his M.S. in Computer Science from the University of Florida in 2017, specializing in computer science educational research and human centered computing. His works included developing of computational reasoning skills and mental simulation ability in K-12 students using Kodu Game Lab environment. As a faculty member he focuses on research and improvement of computer science education in engineering students.
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We introduce Housekeep, a benchmark to evaluate commonsense reasoning in the home for embodied AI. In Housekeep, an embodied agent must tidy a house by rearranging misplaced objects without explicit instructions specifying which objects need to be rearranged. Instead, the agent must learn from and is evaluated against human preferences of which objects belong where in a tidy house. Specifically, we collect a dataset of where humans typically place objects in tidy and untidy houses constituting 1799 objects, 268 object categories, 585 placements, and 105 rooms.
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The RePEc plagiarism page FaD-CODS Fake News Detection on COVID-19 Using Description Logics and Semantic ReasoningKartik Goel, Charu Gupta, Ria Rawal, Prateek Agrawal and Vishu Madaan
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Kartik Goel: Bhagwan Parshuram Institute of Technology, India
Charu Gupta: Bhagwan Parshuram Institute of Technology, India
Ria Rawal: Bhagwan Parshuram Institute of Techology, India
Prateek Agrawal: Lovely Professional University, India
Vishu Madaan: Lovely Professional University, IndiaInternational Journal of Information Technology and Web Engineering (IJITWE), 2021, vol. 16, issue 3, 1-20Abstract:COVID-19 has affected people in nearly 180 countries worldwide. This paper presents a novel and improved Semantic Web-based approach for implementing the disease pattern of COVID-19. Semantics gives meaning to words and defines the purpose of words in a sentence. Previous ontology approaches revolved around syntactic methods. In this paper, semantics gives due priority to understand the nature and meaning of the underlying text. The proposed approach, FaD-CODS, focuses on a specific application of fake news detection. The formal definition is given by depiction of knowledge patterns using semantic reasoning. The proposed approach based on fake news detection uses description logic for semantic reasoning. FaD-CODS will affect decision making in medicine and healthcare. Further, the state-of-the-art method performs best for semantic text incorporated in the model. FaD-CODS used a reasoning tool, RACER, to check the consistency of the collected study. Further, the reasoning tool performance is critically analyzed to determine the conflicts between a myth and fact.Date: 2021
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We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the AI2 Reasoning Challenge (ARC) dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the "challenge" set and statistics related to them. Additionally, we confirm an observation made in the original paper by demonstrating that, although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.
Visual question answering deals with coming up with an efficient representation of both the text and visual domains in order to perform reasoning. This is a challenging problem because reasoning in real world requires us to understand how different objects interact and behave with each other in the scene. To build systems that can reason, we need to incorporate concepts such as compositionality, physics, world knowledge etc. which is trivial for humans but not for current intelligent systems. We try to explore this task via the specific problem of question answering in the space of plots and figures using the recently released FigureQA dataset. We build on the ideas of task specific architectures such as Relation Networks and task generic architectures like FiLM to improve the state of the art performance on the FigureQA dataset. be457b7860
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