“What if artificial intelligence becomes conscious, autonomous beings?”

Iris Qu 曲晓宇, computer programmer, technologist, and artist based in Brooklyn, NY, developed the concept Do AIs Dream of Climate Chaos, aiming to question what actions a sentient machine might take to prevent its own pending destruction in climate chaos. Iris shared some insights about the several steps of this project. 

When and how did your project start?
During the 2020 lockdown, my friends and I started a virtual book club. The pandemic renewed our sense of urgency about climate change and led to many readings and conversations on how we might adapt as individuals and “a people.” The implications of those readings shuttered my notion of techno-optimism and drew me to the intersection of technology and the environment. During my research, I read about how tech industry “thought-leaders” routinely listed artificial intelligence as a more severe threat to humanity than Climate Change. These statements seemed comical given the current state of general AI versus climate researchers’ dire projections.
What if artificial general intelligence becomes conscious, autonomous beings? Though “AI” manifests itself as software machine learning applications, it depends on a ubiquitous hardware infrastructure that requires constant human maintenance and temperature control. Given the sufficient data, a self-aware AI agent might very likely recognize the necessity of radical change to prevent hardware failure. I wanted to speculate a possible future where an autonomous AI agent spends all its resources computing an equilibrium among humans, ecosystems, and machines.

What is the profile of the project collaborators?
I’m a programmer and technologist born and raised in Qingdao, China, currently based in Brooklyn, New York. This is primarily a solo project, but throughout the process, I got a lot of feedback and support from my friends and community here in New York. My pandemic book club friends are working on projects in similar domains, and I hope to collaborate with them as an extension of my work soon. 

Which stage is the project in?
I wrote a few scripts that collected 270k+ climate-related articles over a few days, then trained text/image generation models based on them. I’m now working on a series of content guided by my curiosity, where I prompt topics, phrases, and keywords as inputs for the AI to produce content. The content made by this speculative AI will be displayed as a video screening and installation at the October event. 

In this phase of your research, what are the two most important aspects of the project?
1. What fascinates me the most about the idea of artificial general intelligence is its ability to question and demonstrate what human intelligence means to us. Machine learning is the best data visualizer of the Anthropocene, as it shows us what we chose to write down and amplify as a collective. By collecting and generating data, the scripts begin to reveal a baseline for “Climate Change” in human terms. 
2. It is challenging to frame a speculative AI as self-aware without any expertise in what that might mean in scientific terms. So another important aspect of this project is to communicate self-awareness as an instinct to understand and survive climate chaos. In this regard, we share a deep-rooted curiosity and optimism with the AI, which makes it relatable. The embodiment of climate crisis in a single AI agent might help us tell a better story on our collective future.  

What are the next steps in the research?
Going deeper into the self-awareness concept, I want to speculate more on how this AI agent might reconcile its carbon footprint with its wish to exist. This part of the project will take the form of a web installation that’s more accessible. 
Another aspect I want to explore more in this next stage is a mechanism for human-machine feedback. The algorithm is currently only learning from prescribed content on the internet, but I want to provide a space for anyone who has something to share with the AI.  

How can AI help to solve the climate crisis?
The tech industry is notoriously at defending its expansionist mindset with technological solutions. When it comes to combating climate change, however, the industry’s characteristic systematic optimism and relentless willingness to fail might help the cause. I’ve seen many encouraging signs of applied machine learning in crisis prevention, energy efficiency, and biodiversity studies. The current form of machine learning is extremely good at data analysis, so it can help us navigate climate change when large amounts of data are involved. 
That said, disaster intervention, biodiversity, and climate predictions are situations that crave nuanced solutions, but machine learning tends to make generalizations that can amplify some of our worst mistakes. It’s crucial to have experts in the loop to ensure these systems do more good than harm.