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How generative AI used in jouralism?

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Generative AI, particularly large language models (LLMs), is increasingly utilized in journalism to enhance various aspects of content creation. These models can assist in personalized persuasion strategies by tailoring messages to recipients' psychological profiles, thereby increasing influence . Furthermore, LLMs have shown promise in augmenting text annotation procedures, although their performance varies based on the dataset and annotation task, emphasizing the need for validation against human-generated labels . In storytelling generation, LLMs can generate text that surprises readers and avoids predictability, incorporating elements like unexpectedness and inference to enhance narrative variety and reader experience . Overall, generative AI in journalism offers opportunities to automate personalized content creation, improve annotation processes, and enhance storytelling capabilities.

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Proceedings Article•DOI Leveraging Generative Conversational AI to Develop a Creative Learning Environment for Computational Thinking Sangho Suh, Pengcheng An - Show less +1 more 22 Mar 2022 7 Citations	Not addressed in the paper.
Open access•Posted Content•DOI The Potential of Generative AI for Personalized Persuasion at Scale 22 Apr 2023	Not addressed in the paper.
Journal Article•DOI Automated Annotation with Generative AI Requires Validation Neil Fasching 31 May 2023-arXiv.org	Not addressed in the paper.
Open access•Journal Article•DOI An Investigation about Entailment and Narrative by AI Techniques (Generative Models) Paolo Marocco, Roberto Gigliucci - Show less +1 more 16 Nov 2020 2 Citations	Not addressed in the paper.
Open access•Posted Content•DOI Automated Annotation with Generative AI Requires Validation 31 May 2023	Not addressed in the paper.

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What can generative AI do?5 answersGenerative AI has diverse capabilities. It can accelerate research in various fields but faces backlash due to copyright concerns, potentially impacting its future legality. In genetics, generative models like GANs and RBMs can create high-quality artificial genomes, aiding in data augmentation, imputation, and encoding for supervised tasks. In drug design, generative models are used to generate novel structures within defined applicability domains, enhancing drug-likeness and fostering industrial adoption. Specifically in de novo drug design, generative models like GANs, autoencoders, and transformers are applied to expand compound libraries, design specific properties, and develop molecular design tools. Overall, generative AI's potential spans from accelerating research and genetic studies to aiding in drug design, albeit with challenges and legal implications.

Whats generative AI?4 answersGenerative AI refers to the technology that can autonomously generate new content, such as text, images, audio, and video. It has the potential to enhance the search experience, reshape information generation and presentation methods, and become new entry points for online traffic. Generative AI can produce high-quality artistic media for various fields, including visual arts, music, fiction, literature, video, and animation. It has the capability to fundamentally alter the creative processes and has its own distinct affordances as a new medium. However, there are concerns regarding the use of generative AI, including issues of attribution, compensation, and copyright infringement. Ongoing lawsuits in the United States may have substantial implications for the future of generative AI systems, affecting their legality and use. The integration of generative AI in the IT industry is also transforming the role of IT professionals, impacting their responsibilities, skills, and career prospects.

How does generative ai work?4 answersGenerative AI works by using deep neural networks to simulate Bayesian models and uncover the inverse Bayes map between parameters and data. This allows for high dimensional regression with dimensionality reduction and nonlinearity, which is equivalent to Bayesian computation. The main advantage of Generative AI is its ability to be density-free, avoiding the need for Markov Chain Monte Carlo (MCMC) simulation of the posterior. Deep quantile neural networks are proposed as a general framework for inference and decision making in Generative AI.

What is the definition of generative AI?3 answersGenerative AI refers to a type of artificial intelligence that uses machine learning to create new content, such as images, video, and text, based on large data sets. It has the ability to recognize patterns in existing data and generate new and unexpected content. Generative AI tools, like ChatGPT and Stable Diffusion, have been applied in various industries, including healthcare education and product design. These tools can be used to create never-before-seen images, support visual learning, and stimulate discussions in the classroom. Generative AI is seen as a new medium with its own unique affordances, and it has the potential to fundamentally alter the creative processes and impact various sectors of society. Understanding the implications and making policy decisions around generative AI requires interdisciplinary scientific inquiry into culture, economics, law, algorithms, and the interaction of technology and creativity.

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