This repository contains a collection of papers and resources on Reasoning in Large Language Models. Feel free to let me know the missing papers (issue or pull request). Thank Kevin Chen-Chuan Chang @UIUC, Jason Wei @Google Brain, Denny Zhou @Google Brain for insightful discussions and suggestions. We mainly focus on techniques that are applicable to improving or eliciting "reasoning" in large language models like GPT-3 (175B) Papers in this paradigm vary a lot and are usually based on small models trained on specific datasets. We list several papers here for reference (that is, the list is not complete).

For many years, photogrammetry has been the leading methodology to derive 3D metric and accurate information from imagery, at different scales (from satellite to aerial, terrestrial and under water) and from different sensors (linear, frame, panoramic). The inclusion of computer vision and robotics solutions has increased the level of automation in image processing and 3D data generation, leading to mainstream automatic solutions and massive 3D digitization processes. The recent advent of artificial intelligence methods based on machine and deep learning approaches is again changing the photogrammetric processes leading to unexpected automated solutions that can truly revolutionize the mapping and 3D documentation sector. This Special Issue wants to focus on this recent change for 3D geometric tasks, and is seeking high-quality papers that explore all the potentialities offered by AI in photogrammetric problems. Papers should report progresses in supporting, integrating and boosting key areas of photogrammetry with AI-based methods.

Clifford Seth Stein (born December 14, 1965), a computer scientist, is a professor of industrial engineering and operations research at Columbia University in New York, NY, where he also holds an appointment in the Department of Computer Science. Stein is chair of the Industrial Engineering and Operations Research Department at Columbia University. Prior to joining Columbia, Stein was a professor at Dartmouth College in New Hampshire. Stein's research interests include the design and analysis of algorithms, combinatorial optimization, operations research, network algorithms, scheduling, algorithm engineering and computational biology. Stein has published many influential papers in the leading conferences and journals in his fields of research, and has occupied a variety of editorial positions including in the journals ACM Transactions on Algorithms, Mathematical Programming, Journal of Algorithms, SIAM Journal on Discrete Mathematics and Operations Research Letters.

Saudi German Hospital had the pleasure to organize the 1st Edition of the Artificial Intelligence (AI) in Medical Imaging Conference 2022 from November the 17th till the 19th, 2022 in correlation with the Kingdom's 2030 vision.

Table of contents (ToC) extraction aims to extract headings of different levels in documents to better understand the outline of the contents, which can be widely used for document understanding and information retrieval. Existing works often use hand-crafted features and predefined rule-based functions to detect headings and resolve the hierarchical relationship between headings. Both the benchmark and research based on deep learning are still limited. Accordingly, in this paper, we first introduce a standard dataset, HierDoc, including image samples from 650 documents of scientific papers with their content labels. Then we propose a novel end-to-end model by using the multimodal tree decoder (MTD) for ToC as a benchmark for HierDoc. The MTD model is mainly composed of three parts, namely encoder, classifier, and decoder. The encoder fuses the multimodality features of vision, text, and layout information for each entity of the document. Then the classifier recognizes and selects the heading entities. Next, to parse the hierarchical relationship between the heading entities, a tree-structured decoder is designed. To evaluate the performance, both the metric of tree-edit-distance similarity (TEDS) and F1-Measure are adopted. Finally, our MTD approach achieves an average TEDS of 87.2% and an average F1-Measure of 88.1% on the test set of HierDoc. The code and dataset will be released at: https://github.com/Pengfei-Hu/MTD.

"There is no shortage of commentary on what artificial intelligence will do to human jobs. It's easy to find a multiplicity of predictions, prescriptions, or denunciations," says Thomas H. Davenport, one of the co-authors of the book. "It is not so easy, however, to find descriptions of how people work day-to-day with smart machines." Davenport joined a Conversation That Matters about our emerging and ever-expanding relationship with a technology that scares a wide range of people including, Elon Musk and Bill Gates.

Artificial intelligence (AI) is having a major impact on healthcare. While advances in the sharing and analysis of medical data result in better and earlier diagnoses and more patient-tailored treatments, data management is also affected by trends such as increased patient-centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The way in which health services are delivered is being revolutionized through the sharing and integration of health data across organizational boundaries. Via AI, researchers can provide new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at an individual and population level. This Special Issue focuses on how AI is used in healthcare, and on related topics such as data management, data integration, data sharing, patient privacy and bioethical issues.

Please follow our new YouTube channel – new content uploaded each week! Is there a place for AI design in real-world practice? asks Edward Crump Please share comments on the above, and subscribe below to get weekly updates in data driven design.

By Vincent Granville Ph.D. Published in September 2022. This book covers the foundations of machine learning, with modern approaches to solving complex problems. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques -- including logistic and Lasso -- are presented as a single method, without using advanced linear algebra. There is no need to learn 50 versions when one does it all and more.

The 35th edition of the IEA/AIE2022 (International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems) was hosted in hybrid mode from July 19th to July 22nd, 2022, in Kitakyushu, Japan. IEA/AIE is an annual conference dedicated to advances related to the theory and applications of artificial intelligence that started in 1988 and has been hosted in over twenty countries. IEA/AIE 2022 was organized in cooperation with the American Association of Artificial Intelligence (AAAI), the ACM Special Interest Group on Artificial Intelligence (SIGAI) and has received the support of Springer, the International Society of Applied Intelligence (ISAI), Kitakyushu city, Universiti Teknologi Malaysia, i-SOMET Inc., and several other international Organizations. The conference had a main track and five special sessions for emerging topics in applied intelligence, named Spatiotemporal Big Data Analytics (SBDA 2022), Intelligent Systems and e-Applications (ISeA 2022), Collective Intelligence in Social Media (CISM 2022), Multi-Agent Systems and Metaheuristics for Complex Problems (MASMCP 2022), and Intelligent Knowledge Engineering in Decision Making Systems (IKEDS 2022). All the submissions were peer-reviewed by at least three reviewers following a double-blind process.