Art and the science of generative AI
By Ziv Epstein, Aaron Hertzmann, L. Herman, Robert Mahari, Morgan R. Frank, Matthew Groh, Hope Schroeder, Amy Smith, Memo Akten, Jessica Fjeld and 4 more. Science, 2023
628 citations recorded by the source corpus.
Understanding shifts in creative work will help guide AI’s impact on the media ecosystem The capabilities of a new class of tools, colloquially known as generative artificial intelligence (AI), is a topic of much debate. One prominent application thus far is the production of high-quality artistic media for visual arts, concept art, music, and literature, as well as video and animation. For example, diffusion models can synthesize high-quality images (1), and large language models (LLMs) can produce sensible-sounding and impressive prose and verse in a wide range of contexts (2). The generative capabilities of these tools are likely to fundamentally alter the creative processes by which creators formulate ideas and put them into production. As creativity is reimagined, so too may be many sectors of society. Understanding the impact of generative AI—and making policy decisions around it—requires new interdisciplinary scientific inquiry into culture, economics, law, algorithms, and the interaction of technology and creativity.
Subfields: General Agricultural and Biological Sciences, Management, Monitoring, Policy and Law, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/223cdf1198a045c049f2359ad9d1db77e8028e80
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
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Citations in the corpus
- From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference (2023, background): “From ethical concerns ranging from violations of copyright laws [2], [3] to safety concerns arising from the fact that these models are capable of hallucinating or fabricating information, concerns about these models in the educational and medical domain [4], [5], their carbon footprint, and many…”
- Can large language models provide useful feedback on research papers? A large-scale empirical analysis (2023, background): “Researchers have discussed the potential of LLM to improve productivity 47,48 , creativity 49 , and facilitate scientific discovery 50 .”
- Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation (2024, neutral): “’, which highlights predictive capabilities along with the Reasoning that is essential for decisions and recommendations (OECD 2019).”
- Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024, background): “Generative AI is a class of machine learning technology that learns to generate new data from training data [18, 19].”