![]() Google also announced a partnership with MediaWise at the Poynter Institute for Media Studies and PBS NewsHour Student Reporting Labs to develop information literacy lesson plans for teachers of middle and high school students. Google is also adding more context to 'About this result' feature, such as how widely a source is circulated, online reviews about a source or company, whether a company is owned by another entity, or even when our systems can't find much info about a source. ![]() "We've reduced the triggering of featured snippets in these cases by 40 per cent with this update," said Google. In his May 2021 introduction to MUM, Pandu Nayak, Google fellow and vice president of Search, made it clear that MUM technology isn’t yet in play: Today’s search engines aren’t quite. The company said it has trained systems to get better at detecting false premises, which are not very common, but are cases where it's not helpful to show a featured snippet. "Our systems can check snippet callouts (the word or words called out above the featured snippet in a larger font) against other high-quality sources on the web, to see if there's a general consensus for that callout, even if sources use different words or concepts to describe the same thing," explained Nayak. Google also introduced latest AI model, called Multitask Unified Model (MUM), to improve search result quality in 'snippets' which are shown on top of the page for searches. "We have deeply invested in both information quality and information literacy on Google Search and News, and today we have a few new developments about this important work," said Nayak. ![]() "These notices provide context about the whole set of results on the page, and you can always see the results for your query, even when the advisory is present," he said in a blog post late on Thursday. Pandu Nayak, Google Fellow and Vice President, Search, said that this doesn't mean that no helpful information is available, or that a particular result is low-quality. “Some of the models we can build with BERT are so complex that they push the limits of what we can do using traditional hardware, so for the first time we’re using the latest Cloud TPUs (Tensor Processing Unit) to serve search results and get you more relevant information quickly,” said Nayak.Google has announced to expand content advisories to searches where its AI systems don't have high confidence in the overall quality of the results available for the search. On average, Google sees billions of searches every day, and 15 per cent of those queries are ones it has not seen before.īERT models can consider the context of a word by looking at the words that come before and after it-particularly useful for understanding the intent behind search queries. “By applying BERT models to both rankings and featured snippets in search, we’re able to do a much better job helping you find useful information,” Pandu Nayak, Google Fellow and Vice President, Search, said in a blog post on Friday. The technology behind the new neural network is called “Bidirectional Encoder Representations from Transformers” (BERT), which Google first introduced last year. The new algorithms are now able to understand the context of the words like ‘for’ and ‘to’ to realise what the query actually mean. Therefore, it looked at the important words like pharmacy, or medicines, and simply returned local results. Previously, the search algorithm treated a sentence as a bag of words. In essence, Google is claiming that it is working on improving the search results by making the system better understand how each word in a sentence is related to others. Google has announced a change to its core search algorithms that it claims can better understand conversational queries and throw more relevant results.īy applying improved natural language analysis, the tech giant claims that it has improved its ability to analyse search queries and offer relevant results for as many as one in ten queries in the US English and support for other countries will come later.
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