… opened a branch in Morgantown is inviting the public to an open house discussion Wednesday about artificial intelligence and the future of the.

An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.

With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans. "We would like to know what a machine is thinking when it makes decisions, and how and why it agrees with the humans that trained it," Boukouvalas said. "We don't want to block someone's social media account because the model makes a biased decision."

This is a strange and exciting time for computer-generated music. The idea of computer-generated musical composition has captured the public imagination, as far back as Kurzweil's demonstration of a pattern-based composer on live TV in 1965[1]. Since then, improvements in technology and composition tools have created whole musical genres based around computer-generated compositions, and have resulted in a vast library of algorithmic compositional techniques. Furthermore, in the past few decades, interactive media such as games and virtual reality have resulted in a demand for music that can adapt to dynamic circumstances presented within the interactive medium. Finally, the advent of ML music models such as Google Magenta's MusicVAE[6] now allow us to extract and replicate compositional features from otherwise complex datasets. These models allow computational composers to parameterize abstract variables such as style and mood. By leveraging these models and combining them with procedural algorithms from the last few decades, it is possible to create a dynamic song that composes music in real-time to accompany interactive experiences [10]. Malakai is a tool that helps users of varying skill levels create, listen to, remix and share such dynamic songs. Using Malakai, a Composer can create a dynamic song that can be interacted with by a Listener.

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyper-relational structure. Hence, there is still room for improvement, for example, by including hyper-relational graph embeddings or type information. Many approaches also include information from Wikipedia, which is easily combinable with Wikidata and provides valuable textual information, which Wikidata lacks.

Technology and visual effects startup Monsters Aliens Robots Zombies (MARZ) has raised $5.3 million in Series A funding. The investment was led by Round13 Captial with participation from Rhino Ventures and Harlo Equity Partners. MARZ plans to use the funding to grow its core VFX business and accelerate the development of its'AI for VFX' technology solutions. The Toronto-based studio launched in 2018 and is developing AI solutions that aim to address some of the challenges faced by the entertainment industry regarding VFX capacity shortage spurred by streaming wars, the corresponding explosion of on-demand content and the importance of VFX in driving subscriber growth. "We will use the funds to accelerate research and development of our'AI for VFX' solutions, of which there are two AI products currently in development," MARZ co-founder and co-president Jonathan Bronfman told TechCrunch in an email.

'Gutfeld!' panel reacts to CNN's suspension of Chris Cuomo after texts reveal the lengths he went to aid his brother Andrew Cuomo amid sex scandal This is a rush transcript from "Gutfeld!," This copy may not be in its final form and may be updated. So, all is not well at CNN. Yes, there is more friction in the fake news factory than there is between Stelter's thighs, while wearing his favorite pair of Lulu lemons. I speak of the network home of hysterics hall monitors in one anchor who would make a great well anchor. As you know, Chris Cuomo is in more hot water than a package of ramen noodles. He just got suspended indefinitely. According to the New York Attorney General's Office, Chris was far more involved in his brother's damage control efforts than previously admitted. Fake news, CNN is totally fake. Now, as you know, Andrew Cuomo, the ex- governor was accused of sexual harassment multiple times. The guy touched more women than Pete Davidson at a wrap party. Chris admitted to helping his brother out in fighting the accusations, and who wouldn't help his brother really. But new documents reveal he was in regular touch with his bros' former top aide and his accusations piled up, Chris demanded knowing when damaging articles would come out, promising he'd uses media connections to help his sleazy sibling. So, this is turning into the best lifetime movie I've ever seen. And I've seen them all, including the 12 men of Christmas. Now, previously, Chris said he never made calls to the press about his brother. And why shouldn't we believe him? He's been so honest before. A little sweaty, just worked out happens. This is where I've been dreaming of. Now, to pull that off, you need a blind spot the size of Wendy Williams's feet. TYRUS, FOX NEWS CHANNEL CONTRIBUTOR (voice-over): Nice, that was good. TYRUS: That was -- GUTFELD: But it seems like Chris was indeed gathering Intel, including dirt on one accuser.

… enterprises are working on the automatic detection of fake news through natural language processing, machine learning and network analysis.

Yannic gave up on reading every interesting arXiv paper he could find a while ago. Today, he cites a combination of the arXiv, Reddit and Twitter as the main sources of ML-related news that he pulls from to stay up-to-date. It's no secret that media coverage of AI can be overblown (e.g. Sophia, the first android with citizenship, now wants to have a robot baby), one-dimensional and negatively slanted (Flasehoods more likely with large language models). But Yannic highlights a couple of specific tropes and themes that he's come to be particularly skeptical of.

A class or taxonomic hierarchy is often manually constructed, and part of our knowledge about the world. In this paper, we propose a novel algorithm for automatically acquiring a class hierarchy from a classifier which is often a large neural network these days. The information that we need from a classifier is its confusion matrix which contains, for each pair of base classes, the number of errors the classifier makes by mistaking one for another. Our algorithm produces surprisingly good hierarchies for some well-known deep neural network models trained on the CIFAR-10 dataset, a neural network model for predicting the native language of a non-native English speaker, a neural network model for detecting the language of a written text, and a classifier for identifying music genre. In the literature, such class hierarchies have been used to provide interpretability to the neural networks. We also discuss some other potential uses of the acquired hierarchies.