International Conference on Modern Artificial Intelligence & Data Science Systems

Abstract

This talk will cover the most advanced digitization of smart factory automation in comparison to traditional methods in Industries 4.0 or 5.0. While considering smart factories and digital transformation through SAP Business AI, artificial intelligence, generative and agentic AI, quantum artificial intelligence, and quantum machine learning play a vital role in addressing critical production issues, and they will connect multi-legacy systems to perform better battery cell industrial solutions. These innovations not only enhance efficiency but also enable real-time data analysis and predictive maintenance, significantly reducing downtime. As industries continue to evolve, the integration of these technologies will pave the way for more sustainable and responsive manufacturing processes. In this talk, I will present advanced technologies, including quantum technologies, quantum artificial intelligence, and quantum machine learning models that can support battery production at industrial manufacturing sites. Additionally, I will discuss several business and technological advancement concepts that contribute to digital innovations and transformations, supporting supply chain solutions for addressing critical production capacity and resource management in smart factories. These advancements enhance operational efficiency and pave the way for sustainable practices within the industry. By integrating these cutting-edge technologies, we can significantly improve decision-making processes and drive innovation across various sectors.

This talk explores how neuroscience can inform general AI design—and how AI can in turn assist neuroscience. Their intersection creates powerful opportunities for innovation, especially in fields like machine learning, cognitive science, and medical technology.

Abstract

This talk will address some key decisional issues that are necessary for a cognitive and interactive robot, which shares space and tasks with humans. We adopt a constructive approach based on the identification and the effective implementation of individual and collaborative skills.

The system is comprehensive, aiming to support a full set of abilities so that the robot controller can perform human-robot joint actions as collaborative problem solving. These abilities include geometric reasoning, perspective-taking, managing human and robot knowledge in separate cognitive models, and human-aware planning.

We’ll also explore how the robot’s behavior must be pertinent and acceptable to the human, and how that influences the robot’s decision-making, planning, and communication processes.

Abstract

The proliferation of open-source Large Language Models (LLMs) presents a complex systems challenge for the research community.

In this talk we introduce a systematic framework for the end-to-end deployment of these models, offering a principled methodology for navigating the ecosystem.

The framework provides a taxonomy of performance optimisation techniques - spanning efficient inference, architectural modification, and quantisation - and a comparative analysis of customisation strategies, from parameter-efficient fine-tuning (PEFT) to Retrieval-Augmented Generation (RAG). We conclude by identifying crucial evaluation metrics and open research questions to guide future academic inquiry.

Abstract

Recently, eXplainable AI (XAI) has gained significant traction in both academia and industry, offering insights into the decision-making processes of black-box machine learning models. This talk introduces a landscape of XAI methods and the strategies for building explainable systems.

Research shows that effective explanations increase user trust—but overwhelming users with too much data can backfire. Thus, parsimony, or the art of delivering just enough explanation, is key. This presentation introduces HAExA, a human-agent explainability framework that provides goal-oriented, parsimonious explanations for remote robotic agents.