Digitization was always about control. When those early computer scientists converted the analog waves to binary code, they got precision, storage, and scalability-but lost texture and spontaneity in the process. Deep learning continues that trade-off today: it detects invisible patterns to the human eye but cannot yet understand why.
Historically, this pattern echoes earlier technologies: Photography captured light but lost the painter’s interpretation; recorded music captured sound but lost the concert’s emotion. Today’s neural networks capture vast knowledge but lose human context. The power of digital representation is its reproducibility; the limitation is its detachment. And now, our challenge is to bridge that gap-to design systems that can compute and comprehend.