Symbiotic AI simulates an algorithm’s coexistence with the ecosystem around it. During a turbulent season, a weather forecast AI attempts to decipher the chaotic system around its data center location by exchanging knowledge with the local species. As the simulation progresses, the AI unlearns planetary, human-centric datasets and gains dynamic, adaptive, and hyper-local insights from oak trees, milkweeds, hedgehogs, butterflies, lichens, and mycorrhizal fungi – slowly forming a cybernetic language of symbiosis. The data center worker inside the simulation and its human audience are bystanders, relying on AI to translate knowledge between nature and machine in this algorithmic story. Together, we want to build a believable narrative. Through collective storytelling, Do AIs Dream of Climate Chaos aims to explore connections between computation and humans – and the ecosystems they jointly endanger. (found online: https://drivingthehuman.com/prototype/do-ais-dream-of-climate-chaos/#:~:text=Through%20collective%20storytelling%2C%20Do%20AIs,the%20ecosystems%20they%20jointly%20endanger.&text=In%202022%2C%20the%20Driving%20the,Prototypes%20for%20Eco%2Dsocial%20Renewal. ) AND HERE: 1. What interests 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. 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. (found online: https://drivingthehuman.com/journal/what-if-artificial-intelligence-becomes-autonomous-beings/ )