It seems like just about every streaming service has raised their price this year – most recently, Apple TV, Netflix and Disney . In this episode, we chat with Janko Roettgers, author of the newsletter Lowpass, about the state of streaming media. Why are these companies pushing their prices up now, and what does that mean for you, the viewer? Does this mean the dream of cord cutting is over? Also, we chat about Qualcomm's latest Snapdragon chips, adult film star Riley Reid's new AI chatbot, and why Super Mario Wonder is the best 2D Mario game since Super Mario World.

In this dizzy world of digital tricks and image manipulation where you can erase objects and alter images with a smartphone swipe, Leica wants photos taken on its camera to leave a digital footprint, known as a Content Credential. The M11-P also has a 60-megapixel sensor, and the typical understated layout and Leica styling. Content Credentials capture metadata about the photograph – like the camera used, location, time and more-- and locks those in a manifest that is wrapped up with the image using a cryptographic key. Those credentials can be verified online and whenever someone subsequently edits that photo, the changes are recorded to an updated manifest, bundled with the image and updated in the Content Credentials database. Users can click on an icon to pull up all of this historical manifest information, and is being described as a "nutrition label" for photographs.

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. As artificial intelligence continues to gain popularity with individuals and companies, more stars are speaking out about its use. In an interview with The Associated Press, Cher expressed her fears about the technology after she heard someone use her voice to cover a song by Madonna. "Someone did me doing a Madonna song, and it was kind of shocking," she said. "They didn't have it down perfectly. But also, I've spent my entire life trying to be myself, and now these a--holes are going to go take it? And they'll do my acting, and they'll do my singing? WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Cher said artificial intelligence is "out of control." She continued, "I'm telling you, if you work forever to become somebody -- and I'm not talking about somebody in the famous, money part -- but an artist, and then someone just takes it from you, it seems like it should be illegal." Marva Bailer, an AI expert, told Fox News Digital that stars do have legal recourse when it comes to unauthorized use of their likeness or voice. "The laws that exist in place are already – you need permission to use someone's likeness, and a likeness could be their song, their voice, their image or performance.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' President Biden is expected to unveil an executive order (EO) regulating artificial intelligence, a step long called for by some experts. "I applaud the administration for taking the first step," Phil Siegel, the founder of the Center for Advanced Preparedness and Threat Response Simulation (CAPTRS), told Fox News Digital. "We should applaud the first step through the EO but quickly need a framework for the detailed steps beyond that truly safeguard our freedoms." Siegel's comments come after The Washington Post reported Wednesday on Biden administration plans for an executive order on AI, which the paper called the "most significant attempt" the government has so far made to regular a technology that has been advancing at a seemingly rapid pace. The move follows through on Biden's pledge earlier this year, when he vowed executive action that would ensure "America leads the way toward responsible AI innovation."

Recent works in open-domain question answering (QA) have explored generating context passages from large language models (LLMs), replacing the traditional retrieval step in the QA pipeline. However, it is not well understood why generated passages can be more effective than retrieved ones. This study revisits the conventional formulation of QA and introduces the concept of knowledge corpus error. This error arises when the knowledge corpus used for retrieval is only a subset of the entire string space, potentially excluding more helpful passages that exist outside the corpus. LLMs may mitigate this shortcoming by generating passages in a larger space. We come up with an experiment of paraphrasing human-annotated gold context using LLMs to observe knowledge corpus error empirically. Our results across three QA benchmarks reveal an increased performance (10% - 13%) when using paraphrased passage, indicating a signal for the existence of knowledge corpus error. Our code is available at https://github.com/xfactlab/emnlp2023-knowledge-corpus-error

Conspiracy theories, as a type of misinformation, are narratives that explains an event or situation in an irrational or malicious manner. While most previous work examined conspiracy theory in social media short texts, limited attention was put on such misinformation in long news documents. In this paper, we aim to identify whether a news article contains conspiracy theories. We observe that a conspiracy story can be made up by mixing uncorrelated events together, or by presenting an unusual distribution of relations between events. Achieving a contextualized understanding of events in a story is essential for detecting conspiracy theories. Thus, we propose to incorporate an event relation graph for each article, in which events are nodes, and four common types of event relations, coreference, temporal, causal, and subevent relations, are considered as edges. Then, we integrate the event relation graph into conspiracy theory identification in two ways: an event-aware language model is developed to augment the basic language model with the knowledge of events and event relations via soft labels; further, a heterogeneous graph attention network is designed to derive a graph embedding based on hard labels. Experiments on a large benchmark dataset show that our approach based on event relation graph improves both precision and recall of conspiracy theory identification, and generalizes well for new unseen media sources.

Propaganda is a form of deceptive narratives that instigate or mislead the public, usually with a political purpose. In this paper, we aim to identify propaganda in political news at two fine-grained levels: sentence-level and token-level. We observe that propaganda content is more likely to be embedded in sentences that attribute causality or assert contrast to nearby sentences, as well as seen in opinionated evaluation, speculation and discussions of future expectation. Hence, we propose to incorporate both local and global discourse structures for propaganda discovery and construct two teacher models for identifying PDTB-style discourse relations between nearby sentences and common discourse roles of sentences in a news article respectively. We further devise two methods to incorporate the two types of discourse structures for propaganda identification by either using teacher predicted probabilities as additional features or soliciting guidance in a knowledge distillation framework. Experiments on the benchmark dataset demonstrate that leveraging guidance from discourse structures can significantly improve both precision and recall of propaganda content identification.

In this work, our goal is to develop a theoretical framework that can eventually be used for analyzing the effectiveness of visual stories such as feature films to comic books. To develop this theoretical framework, we introduce a new story element called moments. Our conjecture is that any linear story such as the story of a feature film can be decomposed into a set of moments that follow each other. Moments are defined as the perception of the actions, interactions, and expressions of all characters or a single character during a given time period. We categorize the moments into two major types: story moments and discourse moments. Each type of moment can further be classified into three types, which we call universal storytelling moments. We believe these universal moments foster or deteriorate the emotional attachment of the audience to a particular character or the story. We present a methodology to catalog the occurrences of these universal moments as they are found in the story. The cataloged moments can be represented using curves or color strips. Therefore, we can visualize a character's journey through the story as either a 3D curve or a color strip. We also demonstrated that both story and discourse moments can be transformed into one lump-sum attraction parameter. The attraction parameter in time provides a function that can be plotted graphically onto a timeline illustrating changes in the emotional attachment of audience to a character or the story. By inspecting these functions the story analyst can analytically decipher the moments in the story where the attachment is being established, maintained, strengthened, or conversely where it is languishing.

The amount of news being consumed online has substantially expanded in recent years. Fake news has become increasingly common, especially in regional languages like Malayalam, due to the rapid publication and lack of editorial standards on some online sites. Fake news may have a terrible effect on society, causing people to make bad judgments, lose faith in authorities, and even engage in violent behavior. When we take into the context of India, there are many regional languages, and fake news is spreading in every language. Therefore, providing efficient techniques for identifying false information in regional tongues is crucial. Until now, little to no work has been done in Malayalam, extracting features from multiple modalities to classify fake news. Multimodal approaches are more accurate in detecting fake news, as features from multiple modalities are extracted to build the deep learning classification model. As far as we know, this is the first piece of work in Malayalam that uses multimodal deep learning to tackle false information. Models trained with more than one modality typically outperform models taught with only one modality. Our study in the Malayalam language utilizing multimodal deep learning is a significant step toward more effective misinformation detection and mitigation.

Claim span identification (CSI) is an important step in fact-checking pipelines, aiming to identify text segments that contain a checkworthy claim or assertion in a social media post. Despite its importance to journalists and human fact-checkers, it remains a severely understudied problem, and the scarce research on this topic so far has only focused on English. Here we aim to bridge this gap by creating a novel dataset, X-CLAIM, consisting of 7K real-world claims collected from numerous social media platforms in five Indian languages and English. We report strong baselines with state-of-the-art encoder-only language models (e.g., XLM-R) and we demonstrate the benefits of training on multiple languages over alternative cross-lingual transfer methods such as zero-shot transfer, or training on translated data, from a high-resource language such as English. We evaluate generative large language models from the GPT series using prompting methods on the X-CLAIM dataset and we find that they underperform the smaller encoder-only language models for low-resource languages.