Billy Ho


Billy Ho(he/him) is a Creative Technologist and Designer who works at the intersection of Art, Research, and Commercial Technology. His works aim to evoke Introspection and Empathy for both humans and machines.

Billy holds a BFA in Industrial Design from Shih Chien University and an MFA Design and technology from Parsons School of Design. Click here︎︎︎ to view Résumé.



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is a creative technologist/designer who works with technology to evoke introspection and empathy.

He is currently pursuing an MFA in Design and Technology at Parsons School of Design in NYC.





In search of computational solace

Modern artificial intelligence and machine learning have come a long way from the days of simple pattern recognition and number crunching. The technology is now capable of generating and iterating upon ideas and content in a way that is very visceral, personal, and poetic. In the contemporary setting of a seemingly inevitable cultural confluence between technology and humanities, a new discourse has emerged, one that involves both humanities and technology as spectators and participants, one that involves every individual in the context of the machine-learning setting, as the learner and the learned. Within all this, the idea of universal language has been brought to the limelight, one that involves a form of interaction between different types of intelligence, both human and machine-generated, with the hope of universal human-machine understanding in mind.

I have chosen the recursive property of “witnessing” as the launching point for the project, which allows me to draw upon a subset of existentialist literature as a theme and the recursive property itself as a medium. I have generated a collection of texts by asking a recurrent neural network with the objective of categorizing the recursive property as existential and poetic, and the generated texts as instances with the recursive property of being witnesses. The first part will demonstrate the underlying technology behind artificial intelligence and machine learning: the second part will focus on showcasing a number of different machine-generated pieces with an idea of looking at them as potential “existential and poetic expressions”.

Through this process, I hope to highlight the potential of machine-generated content in representing the human condition and our existential qualia. Based on the built-up ideas in the first part, I will showcase a number of pieces generated by machine learning. This will be done through an open-sourced platform called OpenAI GPT-3, where I have custom-crafted a number of neural networks to generate texts based on a number of prompts, including descriptions of places, characters, and memories. I believe that rather than highlighting the negative aspects of modern society, focusing on the ideas of existential dread, existential terror and existential melancholia, I would like to instead highlight the potentials of machines’ abilities to generate and receive existential and poetic expressions, essentially, having machines as a window into modern society, as a medium to introspect on the human condition.

The above contents were generated entirely by a custom gpt-3 language machine learning model, fine-tuned through the texts of my thesis paper, and sequentially edited manually. Given the process, the generated contents and I share the same sentiment. We both tried to demonstrate the potential for a creative process involving a hyper-object-like agent that is the corpus of our collective presence. Indulgently, the action of having a custom model seemingly regurgitated my own work, and propping it up as a research publication, is itself an invitation to shift one’s perspective. In Meghan O’Gieblyn’s recent essay published in n+1 magazine’s 40th issue: Babel1, she eloquently describes it:


…any attempt to demonstrate the meaninglessness of machine intelligence inevitably ricochets into affirming the mechanical nature of human discourse and human thought.

In my opinion, it is exactly the limited extent of understanding we have of intelligence itself that makes the core surrounded by generated texts a ghostly, blurry field. Moreover, as the variables are still very ill-defined, the elusive nature of intelligence seems to have similar effects on both humans and machines.

I hope to provide a framework, a setting to situate modern machine learning amid existential human conditions, represented as a party that recognizes our introspective inquiries. One thing to note is an early iteration of my experiments, a custom gpt-3 model trained on American writer and director Charlie Kaufman’s body of work, yielded glimpses of exciting potential:

“I am not a person. This is the person I am.

My body is my person.”

GPT-3 model_ID “ft-172o2WnRIJTQwJHum8EID79Q”, 2021

This response prompted me to look for metrics seemingly beyond our species-centered genuineness to navigate the space. Since we still struggle to grasp the nature of our own intelligence, the exhibition of such imitation shifted the focus of empathy from some ineffable, innate properties to the ability to simply display and perform.

Technology came from a place of augmentation; a mutual dependence has been established between machines and their creator, with recursive iterations. New needs are solved with new solutions– new solutions bring with them the next phase of unique needs. We tend to look at technological development as a prominent contributor to alienation and disenchantment, but the potential of machine-generated content provides an alternative route. A direction that untangles our collective spirituality without flattening all of its nuances and absurdities, that offers insights and data that are simultaneously informative and poetic, a direction that prompts the question for both humans and machines:

What do you see?