Generative & Agentic AI

Description

Generative Artificial Intelligence (GenAI) represents an advanced category of artificial intelligence, characterized by the ability of its models to generate original content, including text, images, videos, audio tracks, and even software code. This innovative AI paradigm uses deep learning algorithms to simulate the human creative process, finding applications in a multitude of fields, from literary production to biological research, from manufacturing to the financial sector. Recent developments indicate that GenAI is exerting a transformative influence on the economy and the business landscape at an extraordinary pace: projections from the McKinsey Global Institute anticipate an increase in the global economic impact of artificial intelligence ranging from 15% to 40% over the coming decades. GenAI-based tools, such as ChatGPT, DALL-E, DeepSeek, and Claude, are reshaping the operational methods of numerous professions. In parallel, recent research has increasingly explored the integration of generative models within agentic systems, where AI models are capable not only of generating content, but also of reasoning, planning, and interacting with external tools and environments to accomplish complex tasks. These Agentic AI systems extend the capabilities of generative models towards more autonomous and interactive settings, opening new challenges in terms of reliability, control, and evaluation. While generative models already raise important ethical and societal concerns, the emergence of agentic systems further amplifies these challenges, making it crucial to address issues such as bias, reliability, and their impact in real-world decision-making contexts. The workshop’s objective is to bridge the gap between academia and industry, fostering dialogue and reflection on both the technological aspects and the societal impacts that Generative and Agentic AI are bringing about.

Topics of Interest

The topics of interest for the workshop include, but are not limited to:

• Generative AI for building symbiotic systems;
• Generative AI, creativity, and art;
• Generative AI and discovery and recommendation systems;
• Development of conversational systems using generative AI;
• The impact of generative AI on software development;
• The impact of generative AI on education;
• Fairness of generative AI models;
• Generative AI for medicine;
• Personalization in the retail sector using generative AI;
• Generative AI for the entertainment industry;
• Large Language Models (LLMs) and Large Multimodal Models (LMMs);
• Large Concept Models
• Human-AI collaboration and human-in-the-loop agentic systems;
• Agentic AI and autonomous decision-making systems;
• Evaluation and benchmarking of agentic AI systems;
• Multi-agent systems;