Smart manufacturing has become a global consensus in the manufacturing industry due to the rapid changes in consumer market demand. The implementation scenarios that incorporate generative AI technology are anticipated to further enhance the efficiency of production lines.
At the "2025 AI Wave: Taiwan Generative AI Applications Hackathon," the victor of Chang Chun Group's "Smart Petrochemical Manufacturing" category, "Cloud Ninjas," adeptly developed an intelligent assistant system capable of promptly processing natural voice commands from on-site operators by integrating three key technologies: speech recognition, semantic comprehension, and task scheduling. This unique approach digitizes important operational experience and significantly enhances the natural fluency of human-machine communication via voice-driven operations.
The "Cloud Ninjas" proposal's acknowledgment by the judges is ascribed to three primary features: "intuitive," "real-time," and "expandable." This transformation of human-computer interaction from a conventional one-way operation to a reciprocal comprehension and immediate response is facilitated by these attributes, which render the machine that "understands," "delivers," and "learns rapidly."
The team meticulously defined a variety of command types, situational actions, and operational scenarios that were derived from real-world industrial contexts during the system design process. These scenarios included traversing from point A to point B, retrieving object A, activating button A, and articulating and reiterating content A. The scenario setting program simulates human operational modes through three state transitions: standby, pondering, and speaking. By employing the aforementioned rigorous designs, this AI machine assistant is capable of not only comprehending language but also autonomously executing the most appropriate action options based on the context.
The "Cloud Ninjas", which was initially unfamiliar with one another, resolved to form a group to compete after attending a generative AI workshop from Amazon Web Services (AWS). They specifically targeted the manufacturing industry, which was more familiar to them. Despite the fact that all members possess a level of programming expertise, they are not acquainted with cloud tools, artificial intelligence, or other related technologies. Fortunately, they were able to demonstrate their results in the "2025 AI Wave: Taiwan Generative AI Applications Hackathon" after completing a series of pre-competition training courses from AWS and becoming more familiar with the use of a variety of tools.
Throughout the development phase, "Cloud Ninjas" utilized several AWS generative AI services, including the generative AI-powered assistant, Amazon Q Developer to expedite program writing for software development, and Amazon Nova Pro, the multimodal model on Amazon Bedrock —a fully managed service that makes high-performing foundation models available—to attain precise semantic comprehension, response generation, and function calling. Simultaneously, it integrates the automatic speech recognition service, Amazon Transcribe, to convert speech to text, and the AI voice generator and text-to-speech tool, Amazon Polly, to provide natural and fluent vocal responses, thus creating an intelligent voice conversational robot.
"Cloud Ninjas" also integrates a real-time query response demo to autonomously search the Internet for user questions and return detailed information and related links in terms of generative AI technology. The team anticipates that these capabilities will expand to include high-value applications, such as immediate decision-making recommendations and operation manual searches.
The global manufacturing industry is anticipating the attainment of more flexible operational objectives and increased efficiency through smart manufacturing as the world undergoes a tide of digital transformation. The "Cloud Ninjas" solution is a prime example of how generative AI is employed to develop AI assistants, serving as a key engine driving digital transformation in manufacturing.
Article edited by Sherri Wang
