Artificial intelligence startup OpenAI has been teasing its new AI video generator, Sora, on social media in recent weeks. Last week, it revealed that it had also given actors and directors in Hollywood a first look at the technology – and a chance to try it out – before Sora is launched publicly. OpenAI published a blog post on March 24 titled Sora's First Impressions, showcasing the work that several creative studios and directors had produced using the video generator. Some media experts speculate that Sora will be extremely disruptive to the film creative industry. Al Jazeera spoke to one executive who works in Hollywood, who asked us not to reveal his identity due to the sensitive nature of the subject.

They say it's for all, but is it really?" So begins a 65-second, AI-generated animated video that touches on hot-button issues in the United States ranging from drug addiction and imprisonment rates to growing wealth inequality. As storm clouds gather over an urban landscape resembling New York City, the words "AMERICAN DREAM" hang in a darkening sky as the video ends. The message is clear: Despite its promises of a better life for all, the United States is in terminal decline. The video, titled American Dream or American Mirage, is one of a number of segments aired by Chinese state broadcaster CGTN – and shared far and wide on social media – as part of its A Fractured America animated series. Other videos in the series contain similar titles that invoke images of a dystopian society, such as American workers in tumult: A result of unbalanced politics and economy, and Unmasking the real threat: America's military-industrial complex. CGTN and the Chinese embassy in Washington, DC did not respond to requests for comment. The Fractured America series is just one example of how artificial intelligence (AI), with its ability to generate high-quality multimedia with minimal effort in seconds, is beginning to shape Beijing's propaganda efforts to undermine the United States' standing in the world. Henry Ajder, a UK-based expert in generative AI, said while the CGTN series does not attempt to pass itself off as genuine video, it is a clear example of how AI has made it far easier and cheaper to churn out content. "The reason that they've done it in this way is, you could hire an animator, and a voiceover artist to do this, but it would probably end up being more time-consuming.

Inaccurate tool localization is one of the main reasons for failures in automating surgical tasks. Imprecise robot kinematics and noisy observations caused by the poor visual acuity of an endoscopic camera make tool tracking challenging. Previous works in surgical automation adopt environment-specific setups or hard-coded strategies instead of explicitly considering motion and observation uncertainty of tool tracking in their policies. In this work, we present SURESTEP, an uncertainty-aware trajectory optimization framework for robust surgical automation. We model the uncertainty of tool tracking with the components motivated by the sources of noise in typical surgical scenes. Using a Gaussian assumption to propagate our uncertainty models through a given tool trajectory, SURESTEP provides a general framework that minimizes the upper bound on the entropy of the final estimated tool distribution. We compare SURESTEP with a baseline method on a real-world suture needle regrasping task under challenging environmental conditions, such as poor lighting and a moving endoscopic camera. The results over 60 regrasps on the da Vinci Research Kit (dVRK) demonstrate that our optimized trajectories significantly outperform the un-optimized baseline.

Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors' opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term "opportunities" that are akin to "anticipation" in Plutchik's theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors' decision making.

Text embedding models represent natural language as dense vectors, positioning semantically similar text near each other within the embedding space (Gao et al., 2021; Le and Mikolov, 2014; Reimers and Gurevych, 2019). These embeddings are commonly used for a wide range of downstream tasks including document retrieval, sentence similarity, classification, and clustering (Muennighoff et al., 2023). Instead of building separate embedding models for each downstream task, recent efforts seek to create a single embedding model supporting many tasks. The recent development of general-purpose text embedding models presents a challenge: these models require large amounts of training data to comprehensively cover desired domains and skills. Recent embedding efforts have focused on using extensive collections of training examples (Li et al., 2023; Wang et al., 2022).

Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

Current language processing technologies allow the creation of conversational chatbot platforms. Even though artificial intelligence is still too immature to support satisfactory user experience in many mass market domains, conversational interfaces have found their way into ad hoc applications such as call centres and online shopping assistants. However, they have not been applied so far to social inclusion of elderly people, who are particularly vulnerable to the digital divide. Many of them relieve their loneliness with traditional media such as TV and radio, which are known to create a feeling of companionship. In this paper we present the EBER chatbot, designed to reduce the digital gap for the elderly. EBER reads news in the background and adapts its responses to the user's mood. Its novelty lies in the concept of "intelligent radio", according to which, instead of simplifying a digital information system to make it accessible to the elderly, a traditional channel they find familiar -- background news -- is augmented with interactions via voice dialogues. We make it possible by combining Artificial Intelligence Modelling Language, automatic Natural Language Generation and Sentiment Analysis. The system allows accessing digital content of interest by combining words extracted from user answers to chatbot questions with keywords extracted from the news items. This approach permits defining metrics of the abstraction capabilities of the users depending on a spatial representation of the word space. To prove the suitability of the proposed solution we present results of real experiments conducted with elderly people that provided valuable insights. Our approach was considered satisfactory during the tests and improved the information search capabilities of the participants.

In our rapidly evolving digital sphere, the ability to discern media bias becomes crucial as it can shape public sentiment and influence pivotal decisions. The advent of large language models (LLMs), such as ChatGPT, noted for their broad utility in various natural language processing (NLP) tasks, invites exploration of their efficacy in media bias detection. Can ChatGPT detect media bias? This study seeks to answer this question by leveraging the Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in distinguishing six categories of media bias, juxtaposed against fine-tuned models such as Bidirectional and Auto-Regressive Transformers (BART), Convolutional Bidirectional Encoder Representations from Transformers (ConvBERT), and Generative Pre-trained Transformer 2 (GPT-2). The findings present a dichotomy: ChatGPT performs at par with fine-tuned models in detecting hate speech and text-level context bias, yet faces difficulties with subtler elements of other bias detections, namely, fake news, racial, gender, and cognitive biases.

Conceptual reasoning, the ability to reason in abstract and high-level perspectives, is key to generalization in human cognition. However, limited study has been done on large language models' capability to perform conceptual reasoning. In this work, we bridge this gap and propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions and generate solutions in a verifiable symbolic space. Using this framework as an analytical tool, we show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks compared to direct inference methods. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making. We propose two techniques to add trustworthy induction signals by generating familiar questions with similar underlying reasoning paths and asking models to perform self-refinement. Experiments show that our proposed techniques improve models' conceptual reasoning performance by 8% to 11%, achieving a more robust reasoning system that relies less on inductive biases.

Artificial Intelligence Generated Content (AIGC) technology development has facilitated the creation of rumors with misinformation, impacting societal, economic, and political ecosystems, challenging democracy. Current rumor detection efforts fall short by merely labeling potentially misinformation (classification task), inadequately addressing the issue, and it is unrealistic to have authoritative institutions debunk every piece of information on social media. Our proposed comprehensive debunking process not only detects rumors but also provides explanatory generated content to refute the authenticity of the information. The Expert-Citizen Collective Wisdom (ECCW) module we designed aensures high-precision assessment of the credibility of information and the retrieval module is responsible for retrieving relevant knowledge from a Real-time updated debunking database based on information keywords. By using prompt engineering techniques, we feed results and knowledge into a LLM (Large Language Model), achieving satisfactory discrimination and explanatory effects while eliminating the need for fine-tuning, saving computational costs, and contributing to debunking efforts.