Abstract

The rapid advancement of deep learning (DL) technology has catalyzed the proliferation of AI applications across diverse domains such as computer vision (CV), natural language processing (NLP), recommendation systems (RecSys), and reinforcement learning (RL). These advancements have profoundly influenced various facets of daily life, fueling competition in self-driving car development, facilitating customer service through chatbots, generating creative content, and enhancing e-commerce experiences through personalized recommendations.

Despite these strides, the successful integration of machine learning (ML) techniques into industrial sectors has been limited, and hindered by numerous challenges. Manufacturing environments, for instance, are characterized by data drift and stringent accuracy requirements, posing obstacles for conventional ML methods. Additionally, industrial applications often lack accurate labels or rely on subjective human judgments, further complicating the deployment of AI solutions.

This seminar introduces industrial AI as a burgeoning field with vast potential to deliver tangible business benefits and streamline engineering processes. Through case studies, we illustrate successful ML implementations in challenging industrial contexts, highlighting strategies for overcoming barriers. We delve into time-series ML algorithms and their pivotal role in manufacturing, emphasizing their relevance in handling the ubiquitous time-series data generated on production lines.

Furthermore, we address the dichotomy between domain-specific applicability and the genericity and reusability of ML algorithms and software components. Balancing these factors is essential for both our research endeavors and the timely delivery of value to customers. We underscore the importance of aligning customer needs, product roadmaps, software systems, and ML algorithm development for the effective development and deployment of industrial AI solutions.

In conclusion, this seminar emphasizes the imperative of aligning customer-centric values, product strategies, and technical development efforts to drive the successful implementation of industrial AI solutions.