This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, images, video and audio, to enable complex AI systems for cross-modal understanding and generation. It covers essential topics such as training methods, architectural components, and practical applications in various fields, from visual storytelling to enhanced accessibility. Through detailed case studies and technical analysis, the text examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. Concluding with a discussion of ethical considerations, responsible AI development, and future directions, this authoritative resource provides both theoretical frameworks and practical insights. It offers a balanced perspective on the opportunities and challenges in the development and deployment of MLLMs, and is highly valuable for researchers, practitioners, and students interested in the intersection of natural language processing and computer vision.

Retrieval-augmented generation (RAG) offers an effective approach for addressing question answering (QA) tasks. However, the imperfections of the retrievers in RAG models often result in the retrieval of irrelevant information, which could introduce noises and degrade the performance, especially when handling multi-hop questions that require multiple steps of reasoning. To enhance the multi-hop reasoning ability of RAG models, we propose TRACE. TRACE constructs knowledge-grounded reasoning chains, which are a series of logically connected knowledge triples, to identify and integrate supporting evidence from the retrieved documents for answering questions. Specifically, TRACE employs a KG Generator to create a knowledge graph (KG) from the retrieved documents, and then uses an Autoregressive Reasoning Chain Constructor to build reasoning chains. Experimental results on three multi-hop QA datasets show that TRACE achieves an average performance improvement of up to 14.03% compared to using all the retrieved documents. Moreover, the results indicate that using reasoning chains as context, rather than the entire documents, is often sufficient to correctly answer questions.

Online games are dynamic environments where players interact with each other, which offers a rich setting for understanding how players negotiate their way through the game to an ultimate victory. This work studies online player interactions during the turn-based strategy game, Diplomacy. We annotated a dataset of over 10,000 chat messages for different negotiation strategies and empirically examined their importance in predicting long- and short-term game outcomes. Although negotiation strategies can be predicted reasonably accurately through the linguistic modeling of the chat messages, more is needed for predicting short-term outcomes such as trustworthiness. On the other hand, they are essential in graph-aware reinforcement learning approaches to predict long-term outcomes, such as a player's success, based on their prior negotiation history. We close with a discussion of the implications and impact of our work. The dataset is available at https://github.com/kj2013/claff-diplomacy.

Abstract: The release of toxic gases by industries, emissions from vehicles, and an increase in the concentration of harmful gases and particulate matter in the atmosphere are all contributing factors to the deterioration of the quality of the air. Factors such as industries, urbanization, population growth, and the increased use of vehicles contribute to the rapid increase in pollution levels, which can adversely impact human health. This paper presents a model for forecasting the air quality index in Nigeria using the Bi-directional LSTM model. The air pollution data was downloaded from an online database (UCL). The dataset was pre-processed using both pandas tools in python.

The release of toxic gases by industries, emissions from vehicles, and an increase in the concentration of harmful gases and particulate matter in the atmosphere are all contributing factors to the deterioration of the quality of the air. Factors such as industries, urbanization, population growth, and the increased use of vehicles contribute to the rapid increase in pollution levels, which can adversely impact human health. This paper presents a model for forecasting the air quality index in Nigeria using the Bi-directional LSTM model. The air pollution data was downloaded from an online database (UCL). The dataset was pre-processed using both pandas tools in python. The pre-processed result was used as input features in training a Bi-LSTM model in making future forecasts of the values of the particulate matter Pm2.5, and Pm10. The Bi-LSTM model was evaluated using some evaluation parameters such as mean square error, mean absolute error, absolute mean square, and R^2 square. The result of the Bi-LSTM shows a mean square error of 52.99%, relative mean square error of 7.28%, mean absolute error of 3.4%, and R^2 square of 97%. The model. This shows that the model follows a seamless trend in forecasting the air quality in Port Harcourt, Nigeria.

Stock Market can be easily seen as one of the most attractive places for investors, but it is also very complex in terms of making trading decisions. Predicting the market is a risky venture because of the uncertainties and nonlinear nature of the market. Deciding on the right time to trade is key to every successful trader as it can lead to either a huge gain of money or totally a loss in investment that will be recorded as a careless trade. The aim of this research is to develop a prediction system for stock market using Fuzzy Logic Type2 which will handle these uncertainties and complexities of human behaviour in general when it comes to buy, hold or sell decision making in stock trading. The proposed system was developed using VB.NET programming language as frontend and Microsoft SQL Server as backend. A total of four different technical indicators were selected for this research. The selected indicators are the Relative Strength Index, William Average, Moving Average Convergence and Divergence, and Stochastic Oscillator. These indicators serve as input variable to the Fuzzy System. The MACD and SO are deployed as primary indicators, while the RSI and WA are used as secondary indicators. Fibonacci retracement ratio was adopted for the secondary indicators to determine their support and resistance level in terms of making trading decisions. The input variables to the Fuzzy System is fuzzified to Low, Medium, and High using the Triangular and Gaussian Membership Function. The Mamdani Type Fuzzy Inference rules were used for combining the trading rules for each input variable to the fuzzy system. The developed system was tested using sample data collected from ten different companies listed on the Nigerian Stock Exchange for a total of fifty two periods. The dataset collected are Opening, High, Low, and Closing prices of each security.

We are used to looking only at well-defined and delimited fields, where business thrives and goes on, and where economic resources and technological availability make the road to innovation more straightforward. However, in my opinion, we never stop to analyse what Shakir Mohamed, in his "Decolonial AI", defines as the "peripheries", shifting our "ictu oculi" from the centre towards new paradigms, still unexplored, if not ignored. Therefore, I found this study by Agunbiade Akintunde Ifeanyichukwu, whose name already says it all, since he signs himself Agunbiade A.I., which analyses the relationship between Artificial Intelligence (AI) and the Nigerian legal system, entitled "Artificial Intelligence and Law, a Nigerian Perspective", really interesting. The aim was to explore the ways in which they can influence each other, capturing new and half-known aspects of little-discussed legal systems. This book proposed the development of an indigenous AI system, coupled with ADR mechanisms, that would have the power to reduce the incidence of court congestion, while analysing a comprehensive legal framework of how it would work.

How is AI Ethics and Responsible AI currently being taught in Computer Science and Engineering Curriculums across Africa? What issues related to this topic are relevant to students and faculty? And what roadblocks or challenges are instructors facing to bring more discussion of AI ethics to classrooms? The goal of this workshop is to foster a discussion on how to effectively integrate AI Ethics into Computer Science/Engineering programs at African Universities. This is an initial step to gather perspectives on the current situation at representative universities in different countries in Africa, and to initiate a discussion on how we can better support each other with lessons learned and share materials/curriculums to further develop AI ethics programs in higher education. After identifying the current state, the interests of students and faculty and the needs of departments in this workshop session, the goal is to continue the series with more in-depth workshops on specific topics.

ICT industry veteran Andile Ngcaba's inq., a Pan-African digital service provider, wants to be Africa's number one artificial intelligence (AI) service provider. The company has points of contacts in 12 African cities, Johannesburg, Gaborone, Lusaka, Ndola, Blantyre, Lilongwe, Mzuzu, Lagos, Abuja, Port Harcourt, Kanu and Abidjan. It has concluded the 100% acquisition of Vodacom Business Africa's operations in Nigeria, Zambia and Cote d'Ivoire with a further planned acquisition in Cameroon pending regulatory approvals. At the time of the announcement of the transaction last June, inq. said this deals represents a significant milestone to its vision to be a leading provider of cloud and digitally based services in key markets across sub-Saharan Africa and provides additional vital assets in its build-out of a regional footprint. Today, inq. said this landmark transaction grows inq.'s regional footprint to 13 cities in 7 countries across Africa including its existing operations in Botswana, Malawi and Mozambique.

Minister of Science and Technology, Ogbonnaya Onu said President Muhammadu Buhari has approved the establishment of an agency on robotics and artificial intelligence, AI, for the South East. Mr Onu revealed this at a grand rally of the All Progressives Congress in Abakaliki, capital of Ebonyi state. The rally which received defectors from the Peoples Democratic Party, was attended by the national chairman of the party, Adams Oshiomhole. Some of the defectors who were received include, Sonni Ogbuoji, former Minister of Power and Steel, Goddy Ogbaga, former Secretary to the State Government, Bernard Odoh and former attorney general and commissioner for justice, Augustine Nwankwegu. Mr Onu told the rally that in 2015 he pleaded with South-East not to put its eggs in one basket but his pleadings fell on deaf ears.