Highly technical agriculturalists are hoisting their pitchforks, demanding that we embrace their ideas to reshape our unsustainable food system. Whether our reality is a food desert or a farm-to-table paradise, it's time we start paying attention: In order to keep pace with a global population that's expected to surpass 9 billion by 2050, projections show food production would have to increase by 70 percent between 2005/07 and 2050. Researchers at San Francisco-based artificial intelligence company Sentient Technologies are answering the call, working with a major U.S. university to leverage data-powered technologies with the goal of creating ideal food-growing climates in a world disrupted by global warming. Since 2015, the university has been building one such technology: high-tech "food computers." These sensor-packed and actuator-controlled greenhouses were designed to test out growing conditions for various fruits and vegetables.

As climate change makes it more difficult to grow crops in outdoor farms because of heat waves, more frequent storms, and more pests and disease, the researchers envision that climate-controlled, tech-filled greenhouses (which they call "food computers") could be an increasingly useful place to grow food. The technology could also eliminate food miles: Instead of shipping avocados from Mexico to China, a Chinese greenhouse could precisely recreate a Mexican climate in Beijing–or tweak it to create a climate even better for an avocado tree. "It definitely speeds up the timescale by which we can get interesting results," says Arielle Johnson, one of the researchers at MIT Media Lab Open Agriculture Initiative, or OpenAg. "When you talk to [Caleb Harper, the director of OpenAg], he's like, 'Yeah, basil is a fast-growing plant,' but in his terminology, fast-growing is six to eight weeks," says Babak Hodjat, CEO of Sentient, a company that also designs AI to help stock traders find patterns in the market and hospitals predict infections. "That's a long time to wait just to get a data point. So we tried out this methodology where the AI itself decides what are the next set of data points to try out."