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Updated Oct 01, 2021 | 12:56 IST
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  • MUM, reportedly, builds on Google's previous algorithm BERT (Bidirectional Encoder Representations from Transformers). In May, the company noted that MUM was 1,000 times more powerful than BERT
  • Google is looking to eliminate the need for users to undertake multiple searches to gain context, knowledge and understanding of a particular topic, by framing answers based not just on textual content but interpretations of images, videos and audios in ways never before seen
  • Users may now be able to take a picture of something using their smartphone camera and then formulate a search using the image along with textual content

Applying its latest learnings in artificial intelligence, Google is planning a redesign of its search to facilitate more intuitive and context-rich responses to users' queries. ,bet on it

Unveiling its Multitask Unified Model (MUM) at its Search On livestream event earlier this week, the tech giant shared critical details into how its new algorithm will impact its ubiquitous search capabilities. The company had originally previewed MUM at its flagship developer conference in May this year. ,spin palace nz

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MUM, reportedly, builds on Google's previous algorithm BERT (Bidirectional Encoder Representations from Transformers). In May, the company noted that MUM was 1,000 times more powerful than BERT. ,live tennis eu

It intends to leverage MUM to recognise an assemblage of topics related to a user's search query and present responses in an organised fashion. More specifically, Google is looking to eliminate the need for users to undertake multiple searches to gain context, knowledge and understanding of a particular topic, by framing answers based not just on textual content but interpretations of images, videos and audios in ways never before seen. Users' search experience will also be made more holistic through MUM's integration of 75 different languages. ,dafabet 188bet

find free slot games,For some perspective on how this may materialise, imagine a user inputs a simple question like, 'What is Nikola Tesla known for?' MUM will create a map out of the constellation of topics that may relate to the question and incorporate these into Google's search results even if the content does explicitly include any of the search keywords. 

Essentially, broad subjects are sliced into hundreds of subtopics that, Google hopes, will provide users with a more inclusive search experience without compromising convenience. ,casino royale quotes

formula 1 gifs,But MUM's ability to understand images, video and audio may be the real gamechanger. Google is hopeful that this feature will ignite use of its Google Lens search tool. Users may now be able to take a picture of something using their smartphone camera and then formulate a search using the image along with textual content. 

For instance, if you're looking to repair your car engine on your own, you could take a photograph of a specific component, and add a question like, 'how do I fix this?' As one can imagine, the Google Lens feature may be of some use when shopping as well. MUM's sophisticated video decoding capabilities will also allow Google searches to identify specific videos relating to a search query and present them to a user. 

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22bet,Kind of. It is already well-known that the machine learning models that Google has integrated in the past have a propensity to propagate racial and gender biases. These models are trained by scouring the web for content and, as such, tend to pick up nasty attitudes and ways of talking. Google has discussed this in various research papers, acknowledging the dark side of these models. 

free live dealer blackjack,Speaking to Fast Company, the company's VP of Search Liz Reid said, “Any time you're training a model based on humans, if you're not thoughtful, you'll get the best and worst parts.” She was quick to stress though that Google's human raters are acutely aware of these biases taking shape in models. “Our raters help us understand what is high quality content, and that's what we use as the basis. But even after we've built the model, we do extensive testing, not just on the model overall, but trying to look at slices so that we can ensure that there is no bias in the system.” 

Another problem could arise from the objectivity versus subjectivity conundrum. Multiple perspectives are often required to understand complex topics with any real degree of nuance so the question of from which point of view Google is presenting answers also emerges. ,dafabet ownership

football world cup betting,Addressing this 'one true answer' problem, Google SVP Prabhakar Ragavan is quoted by The Verge as saying, “Almost all language models, if you look at them, are embeddings in a high dimension space. There are certain parts of these spaces that tend to be more authoritative, certain portions that are less authoritative. We can mechanically assess those things pretty easily.” The challenge, he says, is to present a level of complexity that does not overwhelm a user. 

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