Can we coevolve with AI?

Ecologists have been using AI in research for decades (machine-learning is a more boring name for it), and today it is not uncommon for ecology graduate students to run their statistics using iterative, problem-solving AI algorithms. At its core, AI-based prediction is simply an automated version of the scientific method, designed to be an iterative learning process that becomes more refined with each iteration based on feedback and experience. In machine learning, selection for an optimized solution occurs with every iteration, somewhat similar to how natural selection operates on each generation of a species. With each iteration, the model attempts to minimize differences between its output and what it was trained to believe should be the “correct” output. Ultimately, humans control the inputs and impose artificial selection pressures (such as model parameters, thresholds, and goals for the training) that drive evolution of the outputs in a desired direction. A relevant question is whether humans can sensibly guide this evolution in a manner that parallels evolution and adaptation by natural selection.

Those worried about AI fear that we humans will end up on the wrong side of this selection process, in a zero-sum game between biology and technology. But the reality is that selection is driving biology and computing more closely together, toward an obligate symbiosis rather than a divergence. One could argue that this coevolution has already commenced and that we are already part human, part machine. For example, many of us have instant and unrestricted access to the vast knowledge of the internet via smartphones in the palms of our hands. It is relatively easy to imagine that humans will become more integrated with and dependent on AI in the future, because AI can help humans optimize solutions for complex problems (whether for morally good or bad reasons). If a hypothetical tipping point is crossed in which AI surpasses human intelligence and gains some degree of autonomy and sentience, it is unlikely that AI will annihilate humans, because that would be akin to attacking itself.

Instead, the more likely risk is that humans are becoming, and will continue to become, something new. Who better to understand the limits than ecologists, with their understanding of the fundamental principles of adaptation and evolution? In The Origin of Species, Darwin described natural selection as a process analogous to selective breeding in domesticated pigeons and horses, and this analogy can be further generalized to our coevolution with AI. If humanity becomes entangled within a mutualistic association with AI, its outputs and capabilities will be refined and its early forms will eventually either become extinct or morph into better adapted versions. This evolution is likely to be slow, though punctuated by moments of rapid and drastic change.

Are there risks? Of course, but they are more likely of the variety that we currently face. Just as any successful technological innovation increasingly becomes a part of daily life, there will be initial winners and losers. Even the best adapted species of AI code and bioengineered networks will still be susceptible to disease and disorders, malfunctions, and inefficiencies. However, over time, selection will drive the evolution of better adapted forms of AI. We simply argue that this process will more closely resemble coevolution, rather than an existential battle, between humans and machines.

Ecologists should find comfort in knowing that we will not soon become subject to our AI-enhanced machine overlords. We should find familiarity in the idea that human–machine coevolution will likely be guided by the same principles and processes that govern the natural systems that we study. An improved understanding of how AI can help us better use, conserve, repair, and build the natural world is where we are heading. Ecologists are well-positioned and uniquely qualified to lead in this endeavor.