Machine learning (ML) is utilized in many fields, but it threatens to replace humans in some occupations where ML is a cheaper alternative. Communication design is one such area. The development of a large body of work over many decades, much of which is available online in similar formats, is the perfect habitat for ML. How should communication design respond? That this kind of technological advancement has contributed to the trend for visual-based designers to transform into verbal-based designers (e.g., service design, copywriting, or user-experience design). However, the solution is not to avoid or delay the impact; I believe we should embrace this technology and find a way not just to cope with it, but also to strengthen communication design practice.

This research will generate new concepts, art styles, and formats that can be developed in the field of art and design, and will identify ways for designers to interact constructively with ML technology. Further, this research will provide the foundation for a greater understanding of ML-aided design (MLAD), an expanded scope of interactive and generative design, and increased utilization of MLAD in the future. The purpose of engineering a ML training structure is to ‘teach’ the machine to meet our needs. Beyond that, what benefits can designers obtain from MLAD? How should we handle results that fail the human criteria of ‘making sense‘? Through three phases of doing, reflecting, and learning, the many possibilities of MLAD proved its strength as a visual sensual detector and concept generation tool for designers.