Our paper aims to analyze political polarization in US political system using Language Models, and thereby help candidates make an informed decision. The availability of this information will help voters understand their candidates views on the economy, healthcare, education and other social issues. Our main contributions are a dataset extracted from Wikipedia that spans the past 120 years and a Language model based method that helps analyze how polarized a candidate is. Our data is divided into 2 parts, background information and political information about a candidate, since our hypothesis is that the political views of a candidate should be based on reason and be independent of factors such as birthplace, alma mater, etc. We further split this data into 4 phases chronologically, to help understand if and how the polarization amongst candidates changes. This data has been cleaned to remove biases. To understand the polarization we begin by showing results from some classical language models in Word2Vec and Doc2Vec. And then use more powerful techniques like the Longformer, a transformer based encoder, to assimilate more information and find the nearest neighbors of each candidate based on their political view and their background.

It happens when Google Speech-To-Text and Amazon Transcribe, both popular automatic speech recognition (ASR) systems, erroneously give such age-inappropriate subtitles on YouTube videos for children. This is the key finding of a study titled'Beach to bitch: Inadvertent Unsafe Transcription of Kids Content on YouTube' which covered 7,013 videos from 24 YouTube channels. Ten per cent of these videos contained at least one "highly inappropriate taboo word" for children, says US-based Ashique KhudaBukhsh, an assistant professor at Rochester Institute of Technology's software engineering department. KhudaBukhsh, assistant professor Sumeet Kumar of Indian School of Business in Hyderabad and Krithika Ramesh of Manipal University, who conducted the study, have termed the phenomenon "inappropriate content hallucination". "We were mind-boggled because we knew that these channels were watched by millions of children. We understand this is an important problem because it is telling us that the inappropriate content may not be present in the source but it can be introduced by a downstream AI (Artificial Intelligence) application. So on the broader philosophical level, people generally have checks and balances for the source, but now we have to be more vigilant about having checks and balances if an AI application modifies the source. It can inadvertently introduce inappropriate content," KhudaBukhsh, who has a PhD in machine learning and is from Kalyani in West Bengal, told The Sunday Express.

India and Pakistan have fought four wars in the past few decades, but when India faced an oxygen shortage in its hospitals during its recent COVID-19 surge, Pakistan offered to help. Finding these positive tweets, however, was not as easy as simply browsing the supportive hashtags or looking at the most popular posts. And Twitter's algorithm isn't tuned to surface the most positive tweets during a crisis. Ashique KhudaBukhsh of Carnegie Mellon University's Language Technologies Institute led a team of researchers who used machine learning to identify supportive tweets from Pakistan during India's COVID crisis. In the throes of a public health crisis, words of hope can be welcome medicine.

ORGINIALLY PUBLISHED AT - CMU.EDU Babies whose births were depicted in Bollywood films from the 1950s and 60s were more often than not boys; in today's films, boy and girl newborns are about evenly split. In the 50s and 60s, dowries were socially acceptable; today, not so much. And Bollywood's conception of beauty has remained consistent through the years: beautiful women have fair skin. Fans and critics of Bollywood -- the popular name for a $2.1 billion film industry centered in Mumbai, India -- might have some inkling of all this, particularly as movies often reflect changes in the culture. But these insights came via an automated computer analysis designed by Carnegie Mellon University computer scientists.

Last June, Antonio Radić, the host of a YouTube chess channel with more than a million subscribers, was live-streaming an interview with the grandmaster Hikaru Nakamura when the broadcast suddenly cut out. Instead of a lively discussion about chess openings, famous games, and iconic players, viewers were told Radić's video had been removed for "harmful and dangerous" content. Radić saw a message stating that the video, which included nothing more scandalous than a discussion of the King's Indian Defense, had violated YouTube's community guidelines. It remained offline for 24 hours. Exactly what happened still isn't clear.

For Bollywood, beautiful women have fair skin, according to an Artificial Intelligence (AI)-based computer analysis which reveals that conception of beauty has remained consistent through the years in the film industry centred in Mumbai. The automated computer analysis was led by Indian-origin researchers at Carnegie Mellon University (CMU) in the US. The research revealed that babies whose births were depicted in Bollywood films from the 1950s and 60s were more often than not boys; in today's films, boy and girl newborns are about evenly split. In the 50s and 60s, dowries were socially acceptable; today, not so much. The researchers, led by Kunal Khadilkar and Ashiqur KhudaBukhsh of CMU's Language Technologies Institute (LTI), gathered 100 Bollywood movies from each of the past seven decades along with 100 of the top-grossing Hollywood moves from the same periods.

Liberals and conservatives often seem to speak different languages. A new study using artificial intelligence says that is now literally true. Researchers at Carnegie Mellon University collected more than 86.6 million comments from more than 6.5 million users on 200,000 YouTube videos, then analyzed them using an AI technique normally employed to translate between two languages. The researchers found that people on each side of the political divide often use different words to express similar ideas. For instance, the term "mask" among liberal commenters is roughly equivalent to the term "muzzle" for conservatives.

You are free to share this article under the Attribution 4.0 International license. A new system leverages artificial intelligence to rapidly analyze hundreds of thousands of comments on social media and identify the fraction that defend or sympathize with disenfranchised minorities such as the Rohingya community. The Rohingyas began fleeing Myanamar in 2017 to avoid ethnic cleansing. Human social media moderators, who couldn't possibly manually sift through so many comments, would then have the option to highlight this "help speech" in comment sections. "Even if there's lots of hateful content, we can still find positive comments," says Ashiqur R. KhudaBukhsh, a postdoctoral researcher in the Language Technologies Institute (LTI) at Carnegie Mellon University who conducted the research with alumnus Shriphani Palakodety.