Generative AI
arXiv:2309.07930 · doi:10.1007/s12599-023-00834-7
Abstract
The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The widespread diffusion of this technology with examples such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we work and communicate with each other. In this article, we provide a conceptualization of generative AI as an entity in socio-technical systems and provide examples of models, systems, and applications. Based on that, we introduce limitations of current generative AI and provide an agenda for Business & Information Systems Engineering (BISE) research. Different from previous works, we focus on generative AI in the context of information systems, and, to this end, we discuss several opportunities and challenges that are unique to the BISE community and make suggestions for impactful directions for BISE research.
arXiv commentPublished in Business & Information Systems Engineering (2023)
References in corpus (24)
- Auto-Encoding Variational Bayes
- Sequence to Sequence Learning with Neural Networks
- Denoising Diffusion Probabilistic Models
- Survey of Hallucination in Natural Language Generation
- Semantics derived automatically from language corpora contain human-like biases
- Machine learning and deep learning
- On the Opportunities and Risks of Foundation Models
- Evaluating Large Language Models Trained on Code
- ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks
- The Creation and Detection of Deepfakes: A Survey
- Hybrid Intelligence
- GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
- Fine-Tuning Language Models from Human Preferences
- Human heuristics for AI-generated language are flawed
- AI model GPT-3 (dis)informs us better than humans
- Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity
- Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models
- MusicLM: Generating Music From Text
- How is ChatGPT's behavior changing over time?
- Multimodal datasets: misogyny, pornography, and malignant stereotypes
- Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty
- The Capacity for Moral Self-Correction in Large Language Models
- A Survey of Text Representation Methods and Their Genealogy
- HQP: A Human-Annotated Dataset for Detecting Online Propaganda
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- ThreatGPT: An Agentic AI Framework for Enhancing Public Safety through Threat Modeling
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- Integrating Generative AI into Cybersecurity Education: A Study of OCR and Multimodal LLM-assisted Instruction
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