The hour-predicting model is fed by a variety of data, such as the business hours of other nearby shops, information from a business's website, and even Google Street View images of storefronts and any visible window signs that list its hours. It also considers when a business owner last manually changed hours and when its popular times--aggregated and anonymized location history data of customers visiting the business--are busiest. So one way Google can tell that business hours are inaccurate is if the busiest times occur when a business is supposedly closed.

As Google continues to leverage more artificial intelligence and machine learning in Google Search, one may wonder in what ways does AI and machine learning help Google Search perform its daily tasks. Since 2015, when Google introduced its first AI into search named RankBrain, Google has continued to deploy AI systems to better understand language and thus improve the search results Google presents to its searches. Several months ago we sent Google a number of questions around how Google uses its AI in search, including RankBrain, neural matching, BERT and Google's latest AI breakthrough – MUM. We've come up with more of an understanding of when Google uses AI, which AI does what in Google Search, how these various AI algorithms may work together, how they have changed over the years and what, if anything, search marketers need to know when it comes to how Google uses AI in search. We spoke with Danny Sullivan, the Public Liaison for Google Search, to help with the answers to many of these questions.

Today, Google is working to improve its search engine to be better and able to meet the needs of users. For example, when a user wants to browse pages related to a specific topic such as technology, artificial intelligence will display the best pages whose content is about technology. If the user has any questions, the artificial intelligence will answer these questions. Pandu Nayak, Vice President of Search at Google, gave an example with a sentence like, "Can you get someone's medicine from the pharmacy?" And that question was a challenge to Google's search engine, as it was unable to understand it.

Every time you search for something in Google, artificial intelligence is working behind the scenes to generate responses to your query. A deep learning system called RankBrain has changed the way the search engine functions. In many cases, RankBrain handles search queries better than traditional algorithmic rules that were hand-coded by human engineers, and Google realized a long time ago that AI is the future of their search platform. AI will try to understand exactly what we are searching for and then deliver personalized results to us, based on what it knows about us. You may not realize it, but AI is already deeply integrated into many of the Google products you are using today.

Labor Day weekend typically spurs highway and neighborhood traffic. The AAA didn't offer travel estimates this year based on the COVID-19 pandemic, but last year 43 million Americans traveled for Memorial Day Weekend, the second-highest travel volume on record since the company began tracking holiday travel volumes in 2000. Google estimates that people use Google Maps to drive more than 700 billion miles daily in more than 220 countries and territories worldwide. The app shows the driver which the direction to travel, whether traffic along route is heavy or light, an estimated travel time, and an estimated time of arrival. It may appear simple, but the technology behind the app is quite complex because conditions such as an accident or rockslide in a canyon can change the directions in a matter of seconds.

Last year, Microsoft Corp.'s Azure security team detected suspicious activity in the cloud computing usage of a large retailer: One of the company's administrators, who usually logs on from New York, was trying to gain entry from Romania. A hacker had broken in. Microsoft quickly alerted its customer, and the attack was foiled before the intruder got too far. Inc. and various startups are moving away from solely using older "rules-based" technology designed to respond to specific kinds of intrusion and deploying machine-learning algorithms that crunch massive amounts of data on logins, behavior and previous attacks to ferret out and stop hackers. "Machine learning is a very powerful technique for security--it's dynamic, while rules-based systems are very rigid," says Dawn Song, a professor at the University of California at Berkeley's Artificial Intelligence Research Lab. "It's a very manual intensive process to change them, whereas machine learning is automated, dynamic and you can retrain it easily."

Last year, Microsoft Corp.'s Azure security team detected suspicious activity in the cloud computing usage of a large retailer: One of the company's administrators, who usually logs on from New York, was trying to gain entry from Romania. A hacker had broken in. Microsoft quickly alerted its customer, and the attack was foiled before the intruder got too far. Inc. and various startups are moving away from solely using older "rules-based" technology designed to respond to specific kinds of intrusion and deploying machine-learning algorithms that crunch massive amounts of data on logins, behavior and previous attacks to ferret out and stop hackers. "Machine learning is a very powerful technique for security--it's dynamic, while rules-based systems are very rigid," says Dawn Song, a professor at the University of California at Berkeley's Artificial Intelligence Research Lab. "It's a very manual intensive process to change them, whereas machine learning is automated, dynamic and you can retrain it easily."

Last year, Microsoft Corp.'s Azure security team detected suspicious activity in the cloud computing usage of a large retailer: One of the company's administrators, who usually logs on from New York, was trying to gain entry from Romania. A hacker had broken in. Microsoft quickly alerted its customer, and the attack was foiled before the intruder got too far. Inc. and various startups are moving away from solely using older "rules-based" technology designed to respond to specific kinds of intrusion and deploying machine-learning algorithms that crunch massive amounts of data on logins, behavior and previous attacks to ferret out and stop hackers. "Machine learning is a very powerful technique for security--it's dynamic, while rules-based systems are very rigid," says Dawn Song, a professor at the University of California at Berkeley's Artificial Intelligence Research Lab. "It's a very manual intensive process to change them, whereas machine learning is automated, dynamic and you can retrain it easily."

Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to "learn" by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains. These techniques include "computer vision" – training computers to recognize images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal.

Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to "learn" by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains. These techniques include "computer vision" – training computers to recognize images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal.