The development of artificial intelligence technologies has brought forth the era of autonomous cars, realistic robots, intelligent chatbots, and AI YouTubers. And now, researchers have created an artificial intelligence tool that utilizes language models to identify'fake news'. This tool has been developed to stop the spread of misinformation through stance detection. Based on deep-learning, the AI tool can verify the information provided in posts made on various platforms by comparing it to other posts available on the subject. With the tool, researchers want to eliminate the deceptive posts that have plagued the internet.

In this work we aim to understand and estimate the importance that a neural network assigns to various aspects of the data while learning and making predictions. Here we focus on the recognizing textual entailment (RTE) task and its application to fact verification. In this context, the contributions of this work are as follows. We investigate the attention weights a state of the art RTE method assigns to input tokens in the RTE component of fact verification systems, and confirm that most of the weight is assigned to POS tags of nouns (e.g., NN, NNP etc.) or their phrases. To verify that these lexicalized models transfer poorly, we implement a domain transfer experiment where a RTE component is trained on the FEVER data, and tested on the Fake News Challenge (FNC) dataset. As expected, even though this method achieves high accuracy when evaluated in the same domain, the performance in the target domain is poor, marginally above chance.To mitigate this dependence on lexicalized information, we experiment with several strategies for masking out names by replacing them with their semantic category, coupled with a unique identifier to mark that the same or new entities are referenced between claim and evidence. The results show that, while the performance on the FEVER dataset remains at par with that of the model trained on lexicalized data, it improves significantly when tested in the FNC dataset. Thus our experiments demonstrate that our strategy is successful in mitigating the dependency on lexical information.

It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us. One algorithm meant to shine a light in the darkness is AdVerif.ai,

The animated face of prototype robot GRACE, Graduate Robot Attending Conference, is tested by Carnegie Mellon University computer scientist Reid Simmons, right, in the lab at the school in Pittsburgh Tuesday, July 9, 2002. It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us.

It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us. One algorithm meant to shine a light in the darkness is AdVerif.ai,

Here's some news that's not fake: Not everything you can read on the internet is true. Trouble is, it can be hard to know truths from untruths, and there's evidence untruths travel faster. Many hands have been wrung in recent months over what to do about made-up news stories created to convert social media shares into page views, ad dollars, and perhaps even political traction. The modest first results from an effort to crowdsource machine learning technology to help stem the flood of falsity are a reminder that machines may help us grapple with fake news--but only if humans take the lead. Late last year, Facebook's director of AI research Yann LeCun told journalists that machine learning technology that could squash fake news "either exists or can be developed."

Here's some news that's not fake: Not everything you can read on the internet is true. Trouble is, it can be hard to know truths from untruths, and there's evidence untruths travel faster. Many hands have been wrung in recent months over what to do about made-up news stories created to convert social media shares into page views, ad dollars, and perhaps even political traction. The modest first results from an effort to crowdsource machine learning technology to help stem the flood of falsity are a reminder that machines may help us grapple with fake news--but only if humans take the lead. Late last year, Facebook's director of AI research Yann LeCun told journalists that machine learning technology that could squash fake news "either exists or can be developed."

Long before Nov. 8, 2016, research scientist Dean Pomerleau was concerned about fake news. His Facebook News Feed had been filled with political disinformation during the presidential campaign, and he saw that the stories appeared to be influencing reader's attitudes toward the candidates. Though skeptical, he wondered whether it were possible to create a machine learning tool that could flag fake news stories, and discussed the idea with colleagues on Twitter. Then he issued a challenge: he bet that it were not possible to do, and asked his colleagues could prove him wrong. Delip Rao, the founder of Joostware, which builds Artificial Intelligence products, contacted Pomerleau and offered his help – and thus began the Fake News Challenge.

With the rise of email came the rise of spam filling inboxes. Email has become sophisticated faster than spamming technology and now, the internet's junk mail is often caught in a folder; out of sight and out of mind are messages with the subject line "Kindly get back to me urgently" and the greeting "Dear Beneficiary." There's good news for anybody who sees fake news -- not the sort that's simply true but politically difficult for the president; but actual, fake, conspiracy theory-baiting chum -- as another form of spam. At least that's what Dean Pomerleau, research scientist at Carnegie Mellon University's Robotics Institute, said recently during a panel in New York on the proliferation of fake news. We solved the spam problem using artificial intelligence, he argued, and with A.I., we can solve the problem of fake news by filtering out credible news from the misinformation.

Autonomous 18-wheelers are now driving the highways. Coffee table gadgets are recognizing spoken English nearly as well as humans. Smartphones apps instantly translate conversations between people speaking as many as nine different languages. But for Dean Pomerleau, none of this is all that surprising. Pomerleau built a self-driving car way back in 1989, when the first George Bush was president, and it navigated private roads using a neural network, the same AI technology that underpins modern gadgetry like the Amazon Echo and Microsoft Translator.