Automatic Machine Learning (AutoML) tools are finding increasing applications in solving applied problems in a wide variety of domains. However, their further improvement is limited by the high computational complexity of AutoML algorithms. How can they be improved? What are the prospective directions of AutoML-related researches? Can the existing export knowledge be used? How can ChatGPT help here?
In this talk I will discuss the current state of AutoML field and most promising direction for the future development. The special attention will be taken to meta-optimisation techniques and application of AutoML for industrial tasks.

The interaction between humans and objects, known as Human-Objects Interactions (HOI), holds significant importance within human behavior. We naturally manipulate common or unknown objects in many ways and contexts to accomplish tasks of varying significance and impact. Given the recent success and impact of Artificial Intelligence (AI) and Deep/Machine Learning from health care to manufacturing, the challenging and open topic of visual analysis of HOI is attracting a lot of attention from both academia and industry. In this Lecture, we will explore ideas and techniques for a novel framework that will empower smart agents and robotic systems to visually recognize and anticipate high-level semantics of HOI based on Deep Neural Networks, Knowledge Graphs, and Visual Reasoning.

The presentation concerns the integration of artificial intelligence (AI) into modern teaching practices. Its objective is to equip future educators with a comprehensive understanding of the current and future capabilities of AI in education, with a particular focus on generative AI models such as ChatGPT, Google's Bard, and Microsoft's Bing Chat.

The presentation will commence with an exploration of the opportunities and challenges associated with incorporating AI in classrooms. It will then provide a detailed explanation of AI fundamentals and machine learning. We will discuss the necessity of reframing education in the era of AI, preparing students for an AI-driven workforce, fostering AI literacy, promoting computational thinking, and cultivating a culture of lifelong learning. Furthermore, we will examine strategies for adapting pedagogical practices to integrate AI, offering insights into learning theories, active learning approaches, and problem and project-based learning strategies with AI. We will also highlight the role of Bloom's taxonomy in an AI-enhanced classroom. Finally, we will address the significance of teacher professional development in the AI era. This will encompass the need for continuous adaptation and growth, AI competencies for educators, and the evolving nature of education with the increasing integration of AI.