Financial implications of written reviews provide great incentives for businesses to pay fraudsters to write or use bots to generate fraud reviews. The promising performance of Deep Neural Networks (DNNs) in text classification, has attracted research to use them for fraud review detection. However, the lack of trusted labeled data has limited the performance of the current solutions in detecting fraud reviews. Unsupervised and semi-supervised methods are among the most applicable methods to deal with the data scarcity problem. Generative Adversarial Network (GAN) as a semi-supervised method has demonstrated to be effective for data augmentation purposes. The state-of-the-art solution utilizes GAN to overcome the data limitation problem. However, it fails to incorporate the behavioral clues in both fraud generation and detection. Besides, the state-of-the-art approach suffers from a common limitation in the training convergence of the GAN, slowing down the training procedure. In this work, we propose a regularised GAN for fraud review detection that makes use of both review text and review rating scores. Scores are incorporated through Information Gain Maximization in to the loss function for two reasons. One is to generate near-authentic and more human like score-correlated reviews. The other is to improve the stability of the GAN. Experimental results have shown better convergence of the regulated GAN. In addition, the scores are also used in combination with word embeddings of review text as input for the discriminators for better performance. Results show that the proposed framework relatively outperformed existing state-of-the-art framework; namely FakeGAN; in terms of AP by 7%, and 5% on the Yelp and TripAdvisor datasets, respectively.

Reddit has announced its replacement for Alexis Ohanian, who stepped down from the company's board of directors last week. Ohanian said he wanted his position filled by a black candidate. That person is Michael Seibel, the CEO of startup accelerator Y Combinator. Seibel was also the founder of Justin.tv, He was Y Combinator's first black partner before he became CEO, and has supported donating to causes that promote the Black Lives Matter movement.

The feature uses Artificial Intelligence (AI) to cancel out the noise in the background for an enhanced video calling experience.

Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly. However, the usual supervised approach to debunking via misinformation classification requires human-annotated data and is not suited to the fast time-frame of newly emerging events such as the COVID-19 outbreak. In this paper, we postulate that misinformation itself has higher perplexity compared to truthful statements, and propose to leverage the perplexity to debunk false claims in an unsupervised manner. First, we extract reliable evidence from scientific and news sources according to sentence similarity to the claims. Second, we prime a language model with the extracted evidence and finally evaluate the correctness of given claims based on the perplexity scores at debunking time. We construct two new COVID-19-related test sets, one is scientific, and another is political in content, and empirically verify that our system performs favorably compared to existing systems. We are releasing these datasets publicly to encourage more research in debunking misinformation on COVID-19 and other topics.

Sound modelling is the process of developing algorithms that generate sound under parametric control. There are a few distinct approaches that have been developed historically including modelling the physics of sound production and propagation, assembling signal generating and processing elements to capture acoustic features, and manipulating collections of recorded audio samples. While each of these approaches has been able to achieve high-quality synthesis and interaction for specific applications, they are all labour-intensive and each comes with its own challenges for designing arbitrary control strategies. Recent generative deep learning systems for audio synthesis are able to learn models that can traverse arbitrary spaces of sound defined by the data they train on. Furthermore, machine learning systems are providing new techniques for designing control and navigation strategies for these models. This paper is a review of developments in deep learning that are changing the practice of sound modelling.

This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management. Our Natural Language Expression package allows seamless integration of pattern matching, custom NLP modules, and database querying, that makes the workflows much more efficient. As a user study, we adopt this framework to an interdisciplinary undergraduate course where students with both technical and non-technical backgrounds are able to develop creative dialogue managers in a short period of time.

Microsoft News has shed dozens of editorial workers this past week as it moves to an AI-driven system of picking news and away from human editors for MSN.com, one of the world's biggest news destinations. People close to the situation said the layoffs impacted all its contractors in the US, numbering around 50, all of whom are employed by staffing agencies Aquent and MAQ Consulting. Calls and emails to those agencies seeking comment weren't returned. A Microsoft spokesperson said: "Like all companies, we evaluate our business on a regular basis. This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic."

At the end of May 2020, Microsoft took the decision to sack dozens of journalists in favor of replacing them with artificial intelligence (AI). The journalists and editors fired ran the MSN News website, the automatic homepage of the Microsoft Edge browser. The site doesn't write news itself, but draws news from other sources and splits advertising revenue with the original publishers. It used to be curated by humans, who would select stories adhering to their editorial guidelines and edit articles, photos, and headlines wherever necessary. Now, just a few weeks after replacing those humans with software, robots at MSN News have been accused of racism by UK band Little Mix member Jade Thirlwall, after MSN posted a story of her opening up about the racism she experienced at school, accompanied by an image of fellow Little Mix bandmate Leigh-Anne Pinnock.

Microsoft's decision to replace human journalists with robots has backfired, after the tech company's artificial intelligence software illustrated a news story about racism with a photo of the wrong mixed-race member of the band Little Mix. A week after the Guardian revealed plans to fire the human editors who run MSN.com and replace them with Microsoft's artificial intelligence code, an early rollout of the software resulted in a story about the singer Jade Thirlwall's personal reflections on racism being illustrated with a picture of her fellow band member Leigh-Anne Pinnock. Thirlwall, who attended a recent Black Lives Matter protest in London, criticised MSN on Friday, saying she was sick of "ignorant" media making such mistakes. She posted on Instagram: "@MSN If you're going to copy and paste articles from other accurate media outlets, you might want to make sure you're using an image of the correct mixed race member of the group." "This shit happens to @leighannepinnock and I ALL THE TIME that it's become a running joke," she said.

Dozens of scientists funded by Mark Zuckerberg have protested against his decision to leave inflammatory Donald Trump posts on the site. Mr Zuckerberg is allowing the president to use the social network to "spread both misinformation and incendiary statements", the researchers warn. Scientists, including 60 professors at leading US research institutions, wrote to the Facebook boss asking Mr Zuckerberg to "consider stricter policies on misinformation and incendiary language that harms people," especially during the current turmoil over racial injustice. The letter calls the spread of "deliberate misinformation and divisive language" contrary to the researchers' goals of using technology to prevent and eradicate disease, improve childhood education and reform the criminal justice system. Their mission "is antithetical to some of the stances that Facebook has been taking, so we're encouraging them to be more on the side of truth and on the right side of history as we've said in the letter," said Debora Marks of Harvard Medical School, one of three professors who organised it.