When we started AI Expo Africa here in South Africa back in 2018, it would be fair to say the atmosphere was one of excitement with a fair degree of hype mixed with solid doses of reality. There was still talk of "AI Winters" and that adoption would be slow. Well, 5 years on, the landscape has radically changed. Tools and techniques that were once the exclusive domain of "the developer" are now freely accessible via zero cost platforms / apps / APIs allowing business users to leverage all kinds of AI related tech, be that AI generated presentations or logos, to art, videos, music and animations to name but a few. Even in the time we have been running the show, the creativity and use cases have exploded and it would be fair to say, we are now well into the AI Spring!

Across the developing world, more children than ever are in school – but they are not learning. A recent study estimated that less than 1% of school children in Sub-Saharan Africa attend a school where the teaching meets basic standards of quality. At Rising Academies, we're changing that, and we want your help. We are a growing network of inspiring schools in West Africa. Our mission is to unleash the full potential of every student, equipping them with the knowledge, skills, and character to succeed in further study, work, and day-to-day life.

Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization Program (SP), an interpretable modular framework consisting of an (ordered) list of binary trees, each encoding the step-by-step generative process of an abstractive summary sentence from the source document. A Summarization Program contains one root node per summary sentence, and a distinct tree connects each summary sentence (root node) to the document sentences (leaf nodes) from which it is derived, with the connecting nodes containing intermediate generated sentences. Edges represent different modular operations involved in summarization such as sentence fusion, compression, and paraphrasing. We first propose an efficient best-first search method over neural modules, SP-Search that identifies SPs for human summaries by directly optimizing for ROUGE scores. Next, using these programs as automatic supervision, we propose seq2seq models that generate Summarization Programs, which are then executed to obtain final summaries. We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior. We also conduct a simulation study to show that Summarization Programs improve the interpretability of summarization models by allowing humans to better simulate model reasoning. Summarization Programs constitute a promising step toward interpretable and modular abstractive summarization, a complex task previously addressed primarily through blackbox end-to-end neural systems. Supporting code available at https://github.com/swarnaHub/SummarizationPrograms

Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac Subaveerapandiyan A Ph.D. Research Scholar Department of Library and Information Science Yenepoya (Deemed to be University), Mangalore, Karnataka, India Email: subaveerapandiyan@gmail.com ORCiD: https://orcid.org/0000-0002-2149-9897 Abstract This study aims to understand the perceptions and opinions of academicians towards ChatGPT-3 by collecting and analyzing social media comments, and a survey was conducted with library and information science professionals. The research uses a content analysis method and finds that while ChatGPT-3 can be a valuable tool for research and writing, it is not 100% accurate and should be cross-checked. The study also finds that while some academicians may not accept ChatGPT-3, most are starting to accept it. The study is beneficial for academicians, content developers, and librarians. Keywords: Conversational Generative Pre-training Transformer (ChatGPT), Artificial Intelligence in Academia, Academic Writing with ChatGPT, Library Services Introduction The OpenAI-developed GPT (Generative Pre-trained Transformer) model has a variation called ChatGPT. The GPT model was initially released in 2018 and trained using the Common Crawl, a sizable dataset of text from the internet. The Transformer design, revealed in a 2017 study by Google researchers, served as the model's foundation. Unsupervised learning was used to train the initial GPT model, which meant that it was trained on a sizable text dataset without any explicit labels or annotations.

Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variety of applications, such as Visual Question Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision Language Retrieval (VLR). Among these applications, cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task, e.g., understand, recognize, retrieve, or generate optimally. Researchers have proposed diverse methods to address these tasks. The different variants of transformer-based architectures performed extraordinarily on multiple modalities. This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks. This study summarizes the (i) recent task-specific deep learning methodologies, (ii) the pretraining types and multimodal pretraining objectives, (iii) from state-of-the-art pretrained multimodal approaches to unifying architectures, and (iv) multimodal task categories and possible future improvements that can be devised for better multimodal learning. Moreover, we prepare a dataset section for new researchers that covers most of the benchmarks for pretraining and finetuning. Finally, major challenges, gaps, and potential research topics are explored. A constantly-updated paperlist related to our survey is maintained at https://github.com/marslanm/multimodality-representation-learning.

Large language models can perform various reasoning tasks by using chain-of-thought prompting, which guides them to find answers through step-by-step demonstrations. However, the quality of the prompts depends on the demonstrations given to the models, and creating many of them by hand is costly. We introduce Synthetic prompting, a method that leverages a few handcrafted examples to prompt the model to generate more examples by itself, and selects effective demonstrations to elicit better reasoning. Our method alternates between a backward and forward process to generate new examples. The backward process generates a question that match a sampled reasoning chain, so that the question is solvable and clear. The forward process produces a more detailed reasoning chain for the question, improving the quality of the example. We evaluate our method on numerical, symbolic, and algorithmic reasoning tasks, and show that it outperforms existing prompting techniques.

By understanding the hidden locational clues in images, entirely new approaches of analyzing the natural and built environment are being opened up with profound implications for a number of fields, ranging from the recognition of weather, season, and climate patterns to rural and urban scene understanding, and improvements in navigation and self-driving car technology. Since the beginning of 2022, image geolocalization has additionally garnered extensive media coverage for becoming an immediate priority of investigative journalists and open source intelligence (OSINT) researchers in their attempt to verify information and to document war atrocities in Ukraine, extracting geolocational information from social media content. Despite high academic and public interest, image geolocalization remains an extremely challenging problem. This is because training datasets are geographically sparse, often limited to specific countries, and biased towards urban or rural scenes. The task is further complicated by the fact that geolocalization requires reasoning on multiple levels of geographic granularity (e.g.

The past years have seen a drastic rise in studies devoted to the investigation of colexification patterns in individual languages families in particular and the languages of the world in specific. Specifically computational studies have profited from the fact that colexification as a scientific construct is easy to operationalize, enabling scholars to infer colexification patterns for large collections of cross-linguistic data. Studies devoted to partial colexifications -- colexification patterns that do not involve entire words, but rather various parts of words--, however, have been rarely conducted so far. This is not surprising, since partial colexifications are less easy to deal with in computational approaches and may easily suffer from all kinds of noise resulting from false positive matches. In order to address this problem, this study proposes new approaches to the handling of partial colexifications by (1) proposing new models with which partial colexification patterns can be represented, (2) developing new efficient methods and workflows which help to infer various types of partial colexification patterns from multilingual wordlists, and (3) illustrating how inferred patterns of partial colexifications can be computationally analyzed and interactively visualized.

With news and information being as easy to access as they currently are, it is more important than ever to ensure that people are not mislead by what they read. Recently, the rise of neural fake news (AI-generated fake news) and its demonstrated effectiveness at fooling humans has prompted the development of models to detect it. One such model is the Grover model, which can both detect neural fake news to prevent it, and generate it to demonstrate how a model could be misused to fool human readers. In this work we explore the Grover model's fake news detection capabilities by performing targeted attacks through perturbations on input news articles. Through this we test Grover's resilience to these adversarial attacks and expose some potential vulnerabilities which should be addressed in further iterations to ensure it can detect all types of fake news accurately.

Neural networks-based machine learning methods are rapidly developed for various numerical problems, such as physics-informed neural networks (PINNs) [10, 11, 12], the deep Ritz method [2], and the deep Galerkin method [15]. One of the advantages of these approaches is that they show the possibility for solving high-dimensional problems. In [2], the deep learning techniques as well as the Monte-Carlo integration are used to solve eigenvalue problems, which provides a feasible strategy for high-dimensional cases. For the same eigenvalue problems, [16] applies a neural network-based low-rank tensor model, i.e. the tensor neural network (TNN), with a quadrature scheme to perform efficient numerical integration, and thus it achieves a much better result than [2]. Furthermore, [17] employs the TNN to solve the manybody Schrödinger equation, which emerges the practical value of such low-rank approximation method.