Target Forest grew out of my experience working with artificial intelligence systems. For six months, I was involved in evaluating AI-generated responses, including computer vision projects in which models were trained to recognize visual data. Over time, my attention shifted from the results of recognition to the process itself: how a machine determines what it sees, which features it considers relevant, and what remains outside its field of attention. The forest became the starting point for this idea. It is difficult to separate an individual object from its surroundings: branches overlap, shapes merge, light alters contours, and details disappear into the larger structure. For a recognition system, this environment becomes a set of features, coordinates, and probabilities. Yet an image always contains more information than any particular model can identify. The visual structure of the series is built around this contradiction. Grids, coordinates, technical markings, text fragments, and other graphic elements are placed over a photographic background. All of these elements, apart from the forest image itself, are generated programmatically. I developed a generator that renders graphics and fragments of code directly onto the image. The algorithmic process therefore becomes part of both the concept and the construction of the work. I wanted to preserve the tension between the photographic image and the artificial system layered over it. The graphic structures resemble an interface for observation or analysis, but they never provide complete access to what lies beneath. They partially organize the image while also obscuring it, creating new connections and interference. Abrasions, overlaps, and fading elements introduce another layer of instability. The resulting image exists between several ways of seeing the same thing: human vision, machine recognition, and algorithmic reconstruction. None of them provides a complete account of the object. Something always remains outside the coordinate system, escapes recognition, and resists being assigned a name.