Google's AI Overviews Will Always Be Broken. That's How AI Works

A week after its algorithms advised people to eat rocks and put glue on pizza, Google admitted Thursday that it needed to make adjustments to its bold new generative AI search feature. The episode highlights the risks of Google’s aggressive drive to commercialize generative AI—and also the treacherous and fundamental limitations of that technology.

Google’s AI Overviews feature draws on Gemini, a large language model like the one behind OpenAI’s ChatGPT, to generate written answers to some search queries by summarizing information found online. The current AI boom is built around LLMs’ impressive fluency with text, but the software can also use that facility to put a convincing gloss on untruths or errors. Using the technology to summarize online information promises can make search results easier to digest, but it is hazardous when online sources are contractionary or when people may use the information to make important decisions.

“You can get a quick snappy prototype now fairly quickly with an LLM, but to actually make it so that it doesn't tell you to eat rocks takes a lot of work,” says Richard Socher, who made key contributions to AI for language as a researcher and, in late 2021, launched an AI-centric search engine called You.com.

Socher says wrangling LLMs takes considerable effort because the underlying technology has no real understanding of the world and because the web is riddled with untrustworthy information. “In some cases it is better to actually not just give you an answer, or to show you multiple different viewpoints,” he says.

Google’s head of search Liz Reid said in the company’s blog post late Thursday that it did extensive testing ahead of launching AI Overviews. But she added that errors like the rock eating and glue pizza examples—in which Google’s algorithms pulled information from a satirical article and jocular Reddit comment, respectively—had prompted additional changes. They include better detection of “nonsensical queries,” Google says, and making the system rely less heavily on user-generated content.

You.com routinely avoids the kinds of errors displayed by Google’s AI Overviews, Socher says, because his company developed about a dozen tricks to keep LLMs from misbehaving when used for search.

“We are more accurate because we put a lot of resources into being more accurate,” Socher says. Among other things, You.com uses a custom-built web index designed to help LLMs steer clear of incorrect information. It also selects from multiple different LLMs to answer specific queries, and it uses a citation mechanism that can explain when sources are contradictory. Still, getting AI search right is tricky. WIRED found on Friday that You.com failed to correctly answer a query that has been known to trip up other AI systems, stating that “based on the information available, there are no African nations whose names start with the letter ‘K.’” In previous tests, it had aced the query.

Google’s generative AI upgrade to its most widely used and lucrative product is part of a tech-industry-wide reboot inspired by OpenAI’s release of the chatbot ChatGPT in November 2022. A couple of months after ChatGPT debuted, Microsoft, a key partner of OpenAI, used its technology to upgrade its also-ran search engine Bing. The upgraded Bing was beset by AI-generated errors and odd behavior, but the company’s CEO, Satya Nadella, said that the move was designed to challenge Google, saying “I want people to know we made them dance.”

Some experts feel that Google rushed its AI upgrade. “I’m surprised they launched it as it is for as many queries—medical, financial queries—I thought they’d be more careful,” says Barry Schwartz, news editor at Search Engine Land, a publication that tracks the search industry. The company should have better anticipated that some people would intentionally try to trip up AI Overviews, he adds. “Google has to be smart about that,” Schwartz says, especially when they're showing the results as default on their most valuable product.

Lily Ray, a search engine optimization consultant, was for a year a beta tester of the prototype that preceded AI Overviews, which Google called Search Generative Experience. She says she was unsurprised to see the errors that appeared last week given how the previous version tended to go awry. “I think it’s virtually impossible for it to always get everything right,” Ray says. “That’s the nature of AI.”

Even if blatant errors like suggesting people eat rocks become less common, AI search can fail in other ways. Ray has documented more subtle problems with AI Overviews, including summaries that sometimes draw on poor sources such as sites that are from another region or even defunct websites—something she says could provide less useful information to users who are hunting for product recommendations, for instance. Those who work on optimizing content for Google’s Search algorithm are still trying to understand what’s going on. “Within our industry right now, the level of confusion is on the charts,” she says.

Even if industry experts and consumers get more familiar with how the new Google search behaves, don’t expect it to stop making mistakes. Daniel Griffin, a search consultant and researcher who is developing tools to make it easy to compare different AI-powered search services, says that Google faced similar problems when it launched Featured Snippets, which answered queries with text quoted from websites, in 2014.

Griffin says he expects Google to iron out some of the most glaring problems with AI Overviews, but that it’s important to remember no one has solved the problem of LLMs failing to grasp what is true, or their tendency to fabricate information. “It’s not just a problem with AI,” he says. “It’s the web, it’s the world. There’s not really a truth, necessarily.”

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