Google is a company built on PageRank, an algorithm created from research by company cofounders Larry Page and Sergey Brin in the late 1990s. It relies on indexing, a process using algorithms to sort and evaluate websites. Over time, Google incorporated its Knowledge Graph—a huge reservoir of facts—into search results.

More recently, Google incorporated language models into its search replies. In 2019, the company injected a model it calls BERT into search to answer conversational search queries, suggest searches, and summarize the text that appears below a search result. At the time, Google VP Pandu Nayak called it the biggest advance in search in five years and “one of the biggest leaps forward in the history of search.” BERT also powers search results for Microsoft’s Bing.

In the “Rethinking Search” paper, the Google researchers call indexing the workhorse of modern search. But they envision doing away with indexing by using ever-larger language models that can understand more queries.

The Knowledge Graph, for example, may serve up answers to factual questions, but it’s trained on only a small portion of the web. Using a language model built from more of the web would allow a search engine to make recommendations, retrieve documents, answer questions, and accomplish a wide range of tasks. The authors of the Rethinking Search paper say the approach has the potential to create a “transformational shift in thinking.”

Such a model doesn’t exist. In fact the authors say it may require the creation of artificial general intelligence or advances in fields like information retrieval and machine learning. Among other things, they want the new approach to supply authoritative answers from a diversity of perspectives, clearly reveal its sources, and operate without bias.

In a paper titled “Rethinking Search: Making Experts Out of Dilettantes,” published last month, four engineers from Google Research envisioned search as a conversation with human experts. An example in the paper considers the search “What are the health benefits and risks of red wine?” Today, Google replies with a list of bullet points. The paper suggests a future response might look more like a paragraph saying red wine promotes cardiovascular health but stains your teeth, complete with mentions of—and links to—the sources for the information. The paper shows the reply as text, but it’s easy to imagine oral responses as well, like the experience today with Google Assistant.

But relying more on AI to decipher text also carries risks, because computers still struggle to understand language in all its complexity. The most advanced AI for tasks such as generating text or answering questions, known as large language models, have shown a propensity to amplify bias and to generate unpredictable or toxic text. One such model, OpenAI’s GPT-3, has been used to create interactive stories for animated characters but also has generated text about sex scenes involving children in an online game.

As part of a paper and demo posted online last year, researchers from MIT, Intel, and Facebook found that large language models exhibit biases based on stereotypes about race, gender, religion, and profession.

Rachael Tatman, a linguist with a PhD in the ethics of natural language processing, says that as the text generated by these models grows more convincing, it can lead people to believe they’re speaking with AI that understands the meaning of the words that it’s generating—when in fact it has no common-sense understanding of the world. That can be a problem when it generates text that’s toxic to people with disabilities or Muslims or tells people to commit suicide. Growing up, Tatman recalls being taught by a librarian how to judge the validity of Google search results. If Google combines large language models with search, she says, users will have to learn how to evaluate conversations with expert AI.