In a blog post, Google outlines the various factors its AI analyzes to determine whether it should do these updates. First, it looks at when the business profile was last updated, other similar shops’ hours, and Popular Times data to decide how likely it is that the hours are incorrect. For example: if Google sees that a lot of people visit the shop when it’s supposedly closed, that may be a red flag.

Google’s post says that its AI looks at even more data if it determines the hours should be updated. It’ll take into account information from the business’s website and can even scrape street view images (which may show business hours signs) to try and figure out when the business is open. Google says it’ll also check with actual humans, including Google Maps users and business owners, to verify the AI’s predictions — the company says it will even use Duplex in some countries to ask businesses about their hours directly.

Google spokesperson Genevieve Park told The Verge that Google will “only publish business hours when we have a high degree of confidence that they’re accurate.” If the AI thinks the hours may be incorrect but doesn’t have a solid prediction, it adds a notice that the hours may have changed.

Park also said that Google doesn’t explicitly tell users when hours were updated by its AI and explained that AI is used pretty much everywhere else in Google Maps. It seems like Google’s pretty bullish on its AI-driven approach. In its post, the company says it’s “on track to update the hours for over 20 million businesses around the globe in the next six months.”

Google also says it’s piloting another use of AI in Maps to help keep speed limits up to date. In the US, it’ll try to see if its partners have taken images of stretches of road that have speed limit signs and will have AI help its operations team identify the sign and the speed limit posted on it.

While it’s no surprise that Google’s using AI for these problems, it is interesting to see how many interlocking systems are involved. There’s computer vision, pattern recognition in location trends, and analyzing data about similar locations (which, of course, also involves figuring out what the similar locations even are), all to quietly try and keep up with how often businesses change their hours and make sure it knows the speed limit on certain stretches of road.