Jiaxiang Zhang is a Professor of AI in the Department of Computer Science at Swansea University, UK. His research focuses on human and machine intelligence. He combines computational modelling, machine learning, and brain imaging to understand human cognition. On machine intelligence, he develops artificial agents to mimic the human capacity of cognitive operations and social interactions. More recently, he started to work on predictive analysis of electronic health records..

Title: Human decision-making in the era of AI

Abstract: As AI continues to evolve at an accelerated pace, exploring the efficient intersection between human and machine intelligence becomes critical. Human-machine collaborations occur at different levels. On the one hand, recent model-based algorithms can handle data processing in a fully automated workflow. On the other hand, those solutions are often rigid and maladaptive to the dynamics of human intentions. This talk draws from our recent work in neuroscience, psychology, and computer science. I will discuss the use of machine learning to automate medical imaging analyses and electronic health records, highlighting the challenge of deploying models in actual applications. I will also present results from experimental and computational modelling studies that aim to understand and predict the change in human intentions and preferences during decision-making.

Mike Edwards has been involved in digital trust standards for over 20 years. As well as developing and supporting ISO 27001 management systems he was involved in developing information security campaigns and running information security assurance teams in the Royal Navy. Having been with BSI for 12 years, he has a number of roles including training delivery globally in standards including ISO 27001, ISO 27701 and ISO 22301. For the last four years, Mike has been responsible for lead the Global training portfolio for BSI and has been instrumental in development of BSI's AI standards training offerings.

Title: Governance in AI. Ensuring the responsible use of artificial intelligence technologies through standards

Abstract: Governance in AI: Ensuring Responsible Use Through Standards explores how effective governance frameworks can guide the responsible development and deployment of artificial intelligence technologies. The presentation highlights the critical role of standards, such as ISO 42001, in addressing ethical concerns, transparency, and accountability. It delves into the importance of risk management, regulatory compliance, and stakeholder collaboration to mitigate biases and unintended consequences in AI systems. By promoting adherence to internationally recognized standards, organizations can enhance trust, data governance, and readiness for emerging AI regulations. The session emphasizes the need for proactive measures to balance innovation with ethical and societal considerations..

Dr. Niranjan U C obtained his Ph D in Electrical Science from Indian Institute of Science, Bangalore in 1993. He was a visiting Researcher at PARAMA Bio-monitoring Institute, Fukuoka, Japan in 1994 and 1996. He is an Adjunct Professor at E&C Department of SCEM, Adyar, former Director of Research & Training at Manipal Dot Net, Manipal and an Advisor to the sports AI startup coachbuddy.ai. He is the president of BMESI, and past Chair of IEEE Mangalore Sub-section. He was the recipient of IEEE EMBS best student paper award in 1992 at Paris and Young Achiever paper award at ISCE, Arizona in 1996. He was felicitated with Distinguished Alumnus award by NITK during their Diamond Jubilee celebrations in the year 2019.

Title: Applications of AI

Abstract: Artificial Intelligence (AI) has transformed numerous industries with its versatile applications. AI-powered chatbots and virtual assistants enhance customer experience in healthcare, finance, and retail. Predictive analytics and machine learning enable businesses to make data-driven decisions, optimize operations, and improve supply chain management. Additionally, AI-driven natural language processing facilitates language translation, sentiment analysis, and text summarization. Overall, AI's broad applications drive innovation, efficiency, and growth across sectors. Architectures of AI inference engines, training techniques and metrics used to measure performances of models will be dealt with. The parameters that express the explainability of AI models would be explored. A few case studies and tools utilized to build the AI systems will be described. The computing platforms that are amenable to AI and scaling commercial deployments would be mentioned. The wide spread adoption of AI, particularly in India can be used to solve many issues seen in the country.

Sapnil Bhatnagar is a Technical Product Manager at Insife, specializing in Digital AI SaaS products, based in London, UK, with deep expertise in product strategy development, product vision and implementation through agile methodologies. He has Led critical digital transformation projects for AIpowered SaaS platforms, from conception to delivery, using agile and rolling wave planning. He has established AI-enabled features for top 50 corporate clients, significantly enhancing product capabilities and business value. He has Implemented robust AI product discovery processes, aligning advanced machine learning solutions with user needs and business objectives. His expertise includes AI SaaS product development and deployment, Product vision crafting and strategy, Value optimization for machine learning solutions, AI feature prioritization and road-mapping, Project Management, Team leadership in Agile environments, Stakeholder management for AI initiatives, Value Creation and Integrations.

Title: Decluttering Advanced RAG in Building Federated Systems for Your Digital Products

Abstract: In the era of data-driven digital products, federated systems must handle diverse and unstructured data sources effectively. Advanced Retrieval-Augmented Generation (RAG) has emerged as a powerful tool for tackling this challenge, particularly with the integration of vector database stores, embeddings, and innovative tools like LangFlow and Astra DB. This session will focus on harnessing unstructured data sources using embeddings to create meaningful vector representations, enabling efficient querying and contextual understanding. We will explore how vector databases and Astra DB support scalable, highperformance storage and retrieval in federated architectures. Additionally, LangFlow will be highlighted for streamlining RAG workflows, from data preprocessing to model integration.Through practical examples, attendees will learn to integrate these technologies into federated systems, unlocking advanced capabilities such as contextual intelligence and real-time insights, for their digital products.

Shrikant Shenoy, Co-founder Coachbuddy AI has close to 30 years of in-depth experience across the breadth of software spectrum providing guidance, architecture & implementation. He has led & mentored multiple small to medium sized teams & architected complex, distributed, highly transactional & scalable software systems in both traditional & cloud (SaaS) formats for apps, middleware, integration, portals & platforms. Previously worked as Director of Engineering at Manipal.Net with a focus on building scalable & intelligent SaaS Products, Platform & Data Engineering on AWS Cloud and provided guidance, strategy, architecture, design & implementation. Has In-depth experience in architecture, development, integration and deployment of complex enterprise-wide business systems, on and off cloud. .

Title: Responsible AI: Navigating the Ethical, Legal and Technological Frontiers

Abstract: The increasing adoption of Artificial Intelligence across industries brings immense potential but also significant responsibility. This talk examines the multi-dimensional aspects of responsible AI, focusing on ethical, legal, societal, and technological considerations. Real-world practices across various industries are reviewed, with critical questions raised about their ethical, legal, and societal validity, as well as the influence of personal biases. These discussions provide a basis for understanding the alignment of these practices with global and Indian regulatory frameworks. The influence of these regulations on technology development is analyzed, highlighting how they shape AI evolution while exposing limitations in existing technologies. The importance of measuring and mitigating biases in AI models is emphasized, with attention given to rigorous testing, fairness optimization constraints, and adversarial debiasing. Explainable AI (XAI) and causal reasoning, including counterfactual analysis, are discussed for their roles in fostering transparency and trust. Practical applications of responsible AI in industry are explored, alongside its implications for the future of technology, emphasizing the need for ethical and sustainable AI practices. This talk aims to provide insights into navigating the challenges and opportunities in the journey toward responsible AI.