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Google and Meta are using artificial intelligence to mitigate problems such as suicide, violence and machismo

Google presented innovations in the artificial intelligence systems that feed its search engine. These enhancements are aimed at optimizing the content users access when searching for information on suicide, sexual assault, substance abuse, and domestic violence.


More precision when searching for information in critical situations

Contact information for relevant national hotlines will now be more prominently displayed along with the most important and high-quality results available.


To achieve greater precision in the results, the machine learning system was improved to understand the language of the searchesas explained by the company at a press conference attended by TechMarkup.

“Now, using our latest AI model, MUM, we can automatically and more accurately detect a broader range of searches about personal crises. MUM is able to better understand the intent behind people’s questions to detect when a person is in need. This helps us more reliably display reliable and actionable information at the right time. We will start using MUM to make these improvements in the coming weeks.”


Safe Search Improvements: What It Is

The search engine has, for some time, with the tool Safe Search or SafeSearch, which gives users the ability to filter explicit results. This is the default setting for Google accounts for those under 18 years of age. You can choose to disable this option, but artificial intelligence systems still reduce the appearance of unexpected content in searches.

To further limit such unwanted content, the company announced new updates behind BERT (Bidirectional Encoder Representations of Transformers)a technique used at Google for pre-training in natural language processing.


The great contribution of this technique is that it allows a bidirectional interpretation, that is to say that to interpret a term in context, both the word that precedes it and the one that follows it are taken into account.

Now, BERT improved comprehension and can better understand the intent of searches, This further reduces the chances of the user encountering unexpected search results.

“This is a complex challenge that we have been tackling for years, but in just the last year, this BERT improvement has reduced the occurrence of unexpected results by 30%. It has had a particular impact in reducing explicit content for searches related to ethnicity, sexual orientation, and genderwhich can disproportionately affect women and especially women of color.

MUM can transfer knowledge through the 75 languages ​​in which it is trained, This allows security protections to be scaled around the world more efficiently. AI is used to help reduce unhelpful and sometimes dangerous spam pages that might show up in search results.

“In the coming months, we will use MUM to improve the quality of our spam protections and expand to languages ​​where we have very little training data. We will also be able to better detect inquiries about personal crises around the world, working with trusted local partners to show practical information in several more countries”, they announced.


From Meta they announced the development of an AI system that can research and write the first drafts of biographical publications in the style of Wikipedia. The objective of this model is to solve the lack of representation that exists on this site and others like it. Barely 20% of the biographies on Wikipedia are of women.reported from the company when making this announcement.

The developer of this project is Angela Fan, a researcher at Meta AI. “There is still work to be done, butWe hope this new system will one day help Wikipedia editors create thousands of accurate and engaging biographical entries about important people not currently on the site.”, highlighted the scientist.

Women are underrepresented on that platform, despite the impact they have had on science and other fields. To illustrate this idea, Fan shares the case of Canadian physics, donna strickland. She won the Nobel Prize in Physics in 2018, however, as soon as she won the award, no one would have been able to find information about her on Wikipedia.because there just wasn’t. A publication was recently made on that site a few days after that award, the most important in his field of study.

“Our work is purely research at this point, and we hope that the AI ​​research community will take advantage of our model and dataset as a starting point to develop and move forward. The idea is to one day be able to use AI to compensate for gender imbalances in the biographical content of Wikipedia, one of the most notable informative references on the web.. Women have been and are fundamental in many aspects of society, but their contributions are not given as much visibility as is seen in the contributions made by men. Representation matters, and we want to contribute to it with this research”, Fan highlighted when asked by TechMarkup about the scope of this development.

How the model works

The developed model first retrieves relevant information from the Internet to introduce the topic. Next, the generation module creates the text, while in the third step, the citation module builds the bibliography with links to the sources used. The process then repeats, with each section covering all the elements that are present in a full Wikipedia biography.


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