Chan Long Fung, Lazarus

Chan Long Fung, Lazarus, is a new media artist who explores the intersections of science, technology, humanities, and art. Born in Hong Kong in 1996, he currently resides there. His works often blend various elements, including soundscapes, electronic devices, algorithmic art, data visualization, and generative art. Chan excels in transcending disciplinary limitations and engaging in interdisciplinary creation to integrate knowledge and ideas from diverse fields. Chan’s works delve deeply into observing and contemplating natural phenomena in life. His creative inspirations are rooted in a profound understanding of technology, sensitivity to nature, and passion for science. Chan creates art to pose questions and represent these themes.

Chan believes that, as an artist, he can maintain a unique perspective and approach to the long-term observation of the intricate details of scientific and technological developments, continually raising questions from a humanistic and philosophical standpoint and then transforming these questions into visual art experiences. He holds the belief that art can help people gain a deeper understanding and exploration of issues through simulation, representation, and experience. Although works of art do not necessarily need to explain problems, artists can pose questions that stimulate the audience’s conscious engagement, thereby clarifying the direction of the issues at hand.

Stochastic Camera (version 0.1)

“Stochastic Camera (version 0.1)” is an image generation and photography software. Inspired by slit-scan photography, 3D rendering engine logic and stochastic processes, Chan intervenes in the digital camera’s image capture process to render images from the real world. In Chan’s view, digital cameras have made the image capture process more convenient and instantaneous. However, as the process becomes easier, so have the limitations of capturing abstract images. The software allows artists to manipulate image capture methods at the pixel level, creating unpredictable results based on the actual environment and algorithms. “Stochastic Camera (version 0.1)” divides the image capture area into a two-dimensional array. Artists can determine the size of the array and sequentially capture the individual pixel colours within the array to capture the surrounding environment. When artists use this software to create, they wait, observe and repeatedly modify the code to achieve the image results.

Stochastic Camera (version 0.2) – the melting crystal ball

When light travels through a lens and lands on the digital image sensor, that light will transform into digital signals as pixels. The digital camera processes those pixels in a minimal time difference, but the time distinction still exists; thus, the contrast creates a concept of a pixel as an individual.

“Stochastic Camera” is a continuous art project that began in 2018. In version 0.2, re-examines and rethinks machine learning image processing algorithmic logic. The project starts with recording the melting of different ice balls over three hours. It continues for eight days, capturing approximately 24 hours of ice ball melting footage and converting it into raw pixel data for further processing. The melting ice ball symbolises a future world where crystal balls used for divination may no longer be needed while also existing as a refractive object to extend the camera’s lens. The “Stochastic Camera (version 0.2)” utilises two independent formulas representing overall stability and individual autonomy. These formulas are inspired by ensemble averaging and stochastic processes, calculating the data’s overall mean and predicting each pixel’s momentum, which results in two sets of pixel data and moving images.

How many visual variations will occur when a melting ice ball is placed inside a black box and exposed to a stable light source? The answer should be infinite and unpredictable. If there were a machine that could predict the future and show all the possibilities, then the answer would become concrete. In the meantime, how should the pixel’s autonomy be maintained?

Stochastic Camera (version 0.3) – the boiling terra

To know the laws of the universe from everyday life.

Late one night, I had an idea while cooking a pot of noodles: Can the process of heating and evaporating water simulate the birth and death of stars? Galaxies are mainly composed of hydrogen and oxygen, and stars form after the collapse of a molecular cloud, converting their potential energy into thermal energy. The phase change of water molecules can thus be linked to the imagination of stellar-scale energy conversion and morphological changes. From molecular change to planet generation, from an individual pixel to an overall image, there may be a similar set of laws.

Al image processing has recently attracted a lot of public attention. However, what puzzles me most is the neural network algorithm’s thinking process, research goal and sources of image data. Inspired by cell communication, researchers have recently developed neural cellular automata on the foundation of classical cellular automata, expanding from a zero-and-one pixel generation to a zero-to-one floating point number generation. The program can be trained to regenerate an image after its destruction. Taking image processing as its starting point, this technology looks towards applications in synthetic biology.

“Stochastic Camera (version 0.3) – the boiling terra “intends to apply these computer science studies on tissue texture to produce landscape images where a planet appears capable of self-growth and self-repair. Recording the process of boiling water with an infrared thermal imager and running neural cellular automata on flat images, the work generates hypsometric maps and stellar spectra to explore immortal stars from everyday details.