One of the latest collaborations between artificial intelligence and humans is further evidence of how machines and humans can create better results when working together. Artificial intelligence (AI) is now on the job to combat the spread of misinformation on the internet and social platforms thanks to the efforts of start-ups such as Logically. While AI is able to analyze the enormous amounts of info generated daily on a scale that’s impossible for humans, ultimately, humans need to be part of the process of fact-checking to ensure credibility. As Lyric Jain, founder and CEO of Logically, said, toxic news travels faster than the truth. Our world desperately needs a way to discern truth from fiction in our news and public, political and economic discussions, and artificial intelligence will help us do that.
The Fake News “Infodemic”
People are inundated with info every single day. Each minute, there are 98,000 tweets, 160 million emails sent, and 600 videos uploaded to YouTube. Politicians. Marketers. News outlets. Plus, there are countless individuals spewing their opinions since self-publishing is so easy. People crave a way to sort through all the information to find valuable nuggets they can use in their own life. They want facts, and companies are starting to respond often by using machine learning and AI tools.
Logically Verifies Fact from Fiction
To make critical decisions in life, we require facts. However, since anyone has the ability to publish information on the internet, false news travels fast and can have dire consequences. UK start-up Logically was founded in 2017 by Lyric Jain, an alum of MIT and Cambridge. He set out to develop a solution that combined artificial and human intelligence to verify the veracity of news, social discussion, and images.
Through a free app that can be downloaded from the Apple Store or Google Play, readers can verify content by sharing an article with Logically. The company also has a Chrome browser extension that works on more than 160,000 social platforms and news sites to fact-check news stories. The company deployed first in the UK and India and then spread to the United States ahead of the 2020 election cycle to fact-check for consumers as well as government agencies.
How Does Logically Work?
Logically’s AI algorithms use natural language processing to understand and analyze text. The AI models label the credibility of the source of the content with a rating of low, medium, high, and an article as reliable or unreliable based on comparisons of similar content from more than 100,000 sources. The algorithms are checking not only content, but metadata and images too.
During India’s last election campaign, Logically analyzed more than 1 million articles. They found 50,000 to be fake. The algorithms also check the toxicity of content and can block out profane and obscene content. This is an area where the algorithms are a bit less proficient as they are with fact verification, where they might mistakenly flag tongue-in-cheek communication as toxic.
Machines are adept at quickly analyzing volumes of content. They can flag questionable items for review by a human fact-checker as well as become smarter over time with feedback from results.
On a Fact-Finding Mission
When there is a need as great as we have with fighting fake news to safeguard our communities and commerce from the damage lies can inflict, it’s not hard to imagine that several innovators have stepped up to help develop solutions.
Another organization that is working to rid the world of misinformation is Full Fact, a team of independent fact-checkers that is also building automated fact-checking tools. Since 2015, they have been developing tech to increase their fact-checking impact. Full Fact collects and monitors data, identifies and labels claims, and often takes the fact-checking process offline with humans.
AdVerif.ai is an AI company that aims to keep users safe from inappropriate and deceiving content, spam, and fake news. The company’s FakeRank algorithms help advertisers, publishers, and ad networks to moderate content and ensure compliance with company policies to ultimately protect users and keep the reputations of brands safe.
As the world’s largest social media networks have come under fire for the part they play in spreading fake news, companies such as Facebook, Twitter, and others will rely on technology and AI tools to help them address the public’s demands. Facebook worked with independent fact-checking organizations Snopes, Politifact, ABC News, and FactCheck.org to verify viral stories. They also announced initiatives such as applying machine learning and building new products to identify and limit the spread of false news.
As the pursuit of fighting fake news becomes more sophisticated, technology leaders will continue to work to find even better ways to sort out fact from fiction also well as refine the AI tools that can help fight disinformation. Deep learning can help automate some of the steps in fake news detection, according to a team of researchers at DarwinAI and Canada’s University of Waterloo. They are segmenting fact-checking into various sub-tasks, including stance detection where the system is given a claim on a news story plus other stories on the same subject to determine if those other stories support or refute the claim in the original piece.
Published at Mon, 25 Jan 2021 05:25:59 +0000
New York-based Moody’s Analytics has enjoyed considerable success across a number of WatersTechnology’s awards programs over the years—for example, in 2020 it won the best credit risk solution provider category in the Waters Rankings—although a win in the AFTAs has always eluded the financial intelligence and analytical tools specialist. Until the 2020 AFTAs that is: This year, Moody’s Analytics walks away with a pair of wins, the first of them coming in the best artificial intelligence (AI) technology initiative category, thanks to its QUIQspread offering, an AI-based financial spreading tool unveiled in 2020, designed to help institutions automate the spreading of financial statements.
Financial spreading is the manually intensive process through which lenders extract key data from unstructured financial statements from the purposes of conducting credit risk analysis on borrowers. According to Eric Grandeo, senior director, product manager at Moody’s Analytics, QUIQspread uses machine learning technology to automate the financial spreading process, resulting in normalized datasets and allowing lenders to make faster and more judicious lending and credit decisions. “It’s a process that can be cumbersome and inconsistent, potentially resulting in costly mistakes,” Grandeo explains. “Lenders want to empower their relationship managers and analysts to focus more on high-value credit risk analysis tasks and increase their throughput in the most efficient way possible, and QUIQspread helps them do that.”
Given the unstructured nature of financial statements, incumbent rules-based applications tend to struggle when it comes to accounting for the variety of information/data formats presented in statements. Machine learning, Grandeo explains, is the ideal technology to automate that process. Machine learning technology learns from “previous practices and behaviors and can adapt to change over time without any development work,” he says. “Spreading is an evolving practice and needs a technology that evolves with it. Today, QUIQspread is processing thousands of spreads for customers in production who are now benefiting from significant time savings and efficiencies.”
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Published at Mon, 25 Jan 2021 05:15:00 +0000