Robotic technology can support the creation of new tools that improve the creative process of cinematography. It is crucial to consider the specific requirements and perspectives of industry professionals when designing and developing these tools. In this paper, we present the results from exploratory interviews with three cinematography practitioners, which included a demonstration of a prototype robotic system. We identified many factors that can impact the design, adoption, and use of robotic support for cinematography, including: (1) the ability to meet requirements for cost, quality, mobility, creativity, and reliability; (2) the compatibility and integration of tools with existing workflows, equipment, and software; and (3) the potential for new creative opportunities that robotic technology can open up. Our findings provide a starting point for future co-design projects that aim to support the work of cinematographers with collaborative robots.

Misinformation is considered a threat to our democratic values and principles. The spread of such content on social media polarizes society and undermines public discourse by distorting public perceptions and generating social unrest while lacking the rigor of traditional journalism. Transformers and transfer learning proved to be state-of-the-art methods for multiple well-known natural language processing tasks. In this paper, we propose MisRoB{\AE}RTa, a novel transformer-based deep neural ensemble architecture for misinformation detection. MisRoB{\AE}RTa takes advantage of two transformers (BART \& RoBERTa) to improve the classification performance. We also benchmarked and evaluated the performances of multiple transformers on the task of misinformation detection. For training and testing, we used a large real-world news articles dataset labeled with 10 classes, addressing two shortcomings in the current research: increasing the size of the dataset from small to large, and moving the focus of fake news detection from binary classification to multi-class classification. For this dataset, we manually verified the content of the news articles to ensure that they were correctly labeled. The experimental results show that the accuracy of transformers on the misinformation detection problem was significantly influenced by the method employed to learn the context, dataset size, and vocabulary dimension. We observe empirically that the best accuracy performance among the classification models that use only one transformer is obtained by BART, while DistilRoBERTa obtains the best accuracy in the least amount of time required for fine-tuning and training. The proposed MisRoB{\AE}RTa outperforms the other transformer models in the task of misinformation detection. To arrive at this conclusion, we performed ample ablation and sensitivity testing with MisRoB{\AE}RTa on two datasets.

Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex and entangled data. Disentangling these latent factors to uncover their underlying representation can improve the robustness, interpretability, and controllability of recommendation models. This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The CaD-VAE method considers the causal relationships between semantically related factors in real-world recommendation scenarios, rather than enforcing independence as in existing disentanglement methods. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors. The results demonstrate that CaD-VAE outperforms existing methods, offering a promising solution for disentangling complex user behavior data in recommendation systems.

The scientific community discourages authors of research papers from citing papers that did not influence them. Such "rhetorical" citations are assumed to degrade the literature and incentives for good work. While a world where authors cite only substantively appears attractive, we argue that mandating substantive citing may have underappreciated consequences on the allocation of attention and dynamism in scientific literatures. We develop a novel agent-based model in which agents cite substantively and rhetorically. Agents first select papers to read based on their expected quality, read them and observe their actual quality, become influenced by those that are sufficiently good, and substantively cite them. Next, agents fill any remaining slots in the reference lists by (rhetorically) citing papers that support their narrative, regardless of whether they were actually influential. By turning rhetorical citing on-and-off, we find that rhetorical citing increases the correlation between quality and citations, increases citation churn, and reduces citation inequality. This occurs because rhetorical citing redistributes some citations from a stable set of elite-quality papers to a more dynamic set with high-to-moderate quality and high rhetorical value. Increasing the size of reference lists, often seen as an undesirable trend, amplifies the effects. In sum, rhetorical citing helps deconcentrate attention and makes it easier to displace incumbent ideas, so whether it is indeed undesirable depends on the metrics used to judge desirability.

With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages, it also increases the risk of spreading disinformation, misinformation, and malformation through the use of fake news. The emergence of this harmful phenomenon has managed to polarize society and manipulate public opinion on particular topics, e.g., elections, vaccinations, etc. Such information propagated on social media can distort public perceptions and generate social unrest while lacking the rigor of traditional journalism. Natural Language Processing and Machine Learning techniques are essential for developing efficient tools that can detect fake news. Models that use the context of textual data are essential for resolving the fake news detection problem, as they manage to encode linguistic features within the vector representation of words. In this paper, we propose a new approach that uses document embeddings to build multiple models that accurately label news articles as reliable or fake. We also present a benchmark on different architectures that detect fake news using binary or multi-labeled classification. We evaluated the models on five large news corpora using accuracy, precision, and recall. We obtained better results than more complex state-of-the-art Deep Neural Network models. We observe that the most important factor for obtaining high accuracy is the document encoding, not the classification model's complexity.

Personalized recommender systems suffuse modern life, shaping what media we read and what products we consume. Algorithms powering such systems tend to consist of supervised learning-based heuristics, such as latent factor models with a variety of heuristically chosen prediction targets. Meanwhile, theoretical treatments of recommendation frequently address the decision-theoretic nature of the problem, including the need to balance exploration and exploitation, via the multi-armed bandits (MABs) framework. However, MAB-based approaches rely heavily on assumptions about human preferences. These preference assumptions are seldom tested using human subject studies, partly due to the lack of publicly available toolkits to conduct such studies. In this work, we conduct a study with crowdworkers in a comics recommendation MABs setting. Each arm represents a comic category, and users provide feedback after each recommendation. We check the validity of core MABs assumptions-that human preferences (reward distributions) are fixed over time-and find that they do not hold. This finding suggests that any MAB algorithm used for recommender systems should account for human preference dynamics. While answering these questions, we provide a flexible experimental framework for understanding human preference dynamics and testing MABs algorithms with human users. The code for our experimental framework and the collected data can be found at https://github.com/HumainLab/human-bandit-evaluation.

Entrepreneur Elon Musk is preparing to launch a startup that will compete with Openai, the creator of Chatgpt, a media report unveiled. According to quoted knowledgeable sources, the owner of Twitter and Tesla is already assembling a team of developers and talking to investors. Tech investor Elon Musk is putting effort into founding a startup that will rival the company behind the Chatgpt artificial intelligence (AI) assistant, Openai, the Financial Times revealed on Friday, citing people familiar with the billionaire's intentions. The publication claims Musk is now recruiting AI engineers while also holding talks with some investors in Spacex and Tesla, two of his best known business enterprises along with Twitter, about backing the new venture, Reuters quoted the report. Companies like Microsoft-funded Openai and Google's parent, Alphabet, have been working to incorporate AI into their offerings despite calls from regulators to introduce comprehensive rules for the technology before its widely implemented.

No one wants to be falsely accused of saying or doing something that will destroy their reputation. Even more nightmarish is a scenario where, despite being innocent, the fabricated "evidence" against a person is so convincing that they are unable to save themselves. Yet thanks to a rapidly advancing type of artificial intelligence (AI) known as "deepfake" technology, our near-future society will be one where everyone is at great risk of having exactly that nightmare come true. Deepfakes -- or videos that have been altered to make a person's face or body appear to do something they did not in fact do -- are increasingly used to spread misinformation and smear their targets. Political, religious and business leaders are already expressing alarm by the viral spread of deepfakes that maligned prominent figures like former US President Donald Trump, Pope Francis and Twitter CEO Elon Musk.

By now you've probably heard the deepfake AI-generated verses mimicking rappers like Drake, Kendrick Lamar, Nas, and Jay-Z. While the technology was shaky at first, these days it's so eerily good it's almost impossible to even tell you're listening to a fake -- especially when the creator has the cadence and delivery of your favorite rapper down pat. As the world continues to immerse itself in AI -- from sophisticated language models like ChatGPT and Bing (or Sydney if you ask it the right way) to AI image generators, and more -- the lightening-fast pace of the technology seems to be outrunning society's ability to adapt to the new future. Deepfakes can be entertaining -- we all loved Kendrick Lamar's "The Heart Part 5" video, but they can also be scary, even dangerous. As Axios pointed out in February, right now generative AI is a legal minefield.

Kurt'The CyberGuy' Knutsson joined'Fox & Friends Weekend' to discuss smart guns and its ability to lock when being handled by an unauthorized user. The Biofire smart gun is expected to hit the market in 2024. Some believe that when it does, it could significantly help curb the gun crisis we're facing in this country. One of the main advantages of Biofire's smart guns is that they can dramatically reduce accidental shootings at home. Last year, the New England Journal of Medicine report revealed that firearm-related accidents, homicides and suicides are the primary cause of death for children and teenagers in the U.S. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER In addition, smart guns can also help reduce gun theft.