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As Ars Technica notes, neither Shutterstock nor Getty Images explicitly prohibits AI-generated images in their terms of service, and Shutterstock users typically make around 15 to 40 percent of what the company makes when it sells an image. Some creators have not taken kindly to this trend, pointing out that these systems use massive datasets of images scraped from the web. […] In other words, the generated works are the result of an algorithmic process which mines original art from the internet without credit or compensation to the original artists. Others have worried about the impacts on independent artists who work for commissions, since the ability for anyone to create custom generated artwork potentially means lost revenue.

It’s important to think about how AI presents itself, says Kurt Gray, a social psychologist at the University of North Carolina, Chapel Hill, who has studied human-AI interaction but was not involved in the work. “In this case, you want it to be kind of like a kid, right?” he says. Otherwise, people might think you’re a troll for asking seemingly ridiculous questions. The team “rewarded” its AI for writing intelligible questions: When people actually responded to a query, the system received feedback telling it to adjust its inner workings so as to behave similarly in the future. Over time, the AI implicitly picked up lessons in language and social norms, honing its ability to ask questions that were sensical and easily answerable.

The new AI has several components, some of them neural networks, complex mathematical functions inspired by the brain’s architecture. “There are many moving pieces […] that all need to play together,” Krishna says. One component selected an image on Instagram — say a sunset — and a second asked a question about that image — for example, “Is this photo taken at night?” Additional components extracted facts from reader responses and learned about images from them. Across 8 months and more than 200,000 questions on Instagram, the system’s accuracy at answering questions similar to those it had posed increased 118%, the team reports today in the Proceedings of the National Academy of Sciences. A comparison system that posted questions on Instagram but was not explicitly trained to maximize response rates improved its accuracy only 72%, in part because people more frequently ignored it. The main innovation, Jaques says, was rewarding the system for getting humans to respond, “which is not that crazy from a technical perspective, but very important from a research-direction perspective.” She’s also impressed by the large-scale, real-world deployment on Instagram.