Goetterfunke: Creativity in Machinae Sapiens. About the Qualitative Shift in Generative AI with a Focus on Text-To-Image
The year 2022 marks a watershed in technology, and arguably in human history, with the release of powerful generative AIs capable of convincingly performing creative tasks. With the help of these systems, anyone can create something that would previously have been considered a remarkable work of art. In human-AI collaboration, the computer seems to have become more than a tool. Many who have made their first contact with current generative AIs see them as "creativity machines" while for others the term "machine creativity" remains an oxymoron. This article is about (the possibility of) creativity in computers within the current Machine Learning paradigm. It outlines some of the key concepts behind the technologies and the innovations that have contributed to this qualitative shift, with a focus on text-to-image systems. The nature of Artificial Creativity as such is discussed, as well as what this might mean for art. AI may become a responsible collaborator with elements of independent machine authorship in the artistic process.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Sapiens: Foundation for Human Vision Models
We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-r…
2D Human Pose Estimation2D Pose EstimationDepth EstimationHuman Part Segmentation+3eSapiens: A Platform for Secure and Auditable Retrieval-Augmented Generation
We present eSapiens, an AI-as-a-Service (AIaaS) platform engineered around a business-oriented trifecta: proprietary data, operational workflows, and any major agnostic Large Language Model (LLM). eSapiens gives business…
"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models
Prewriting is the process of discovering and developing ideas before a first draft, which requires divergent thinking and often implies unstructured strategies such as diagramming, outlining, free-writing, etc. Although …
MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection
This paper does not introduce a novel architecture; instead, it revisits a fundamental yet overlooked baseline: adapting human-centric foundation models for anatomical landmark detection in medical imaging. While landmar…
Pose EstimationGalton's Law of Mediocrity: Why Large Language Models Regress to the Mean and Fail at Creativity in Advertising
Large language models (LLMs) generate fluent text yet often default to safe, generic phrasing, raising doubts about their ability to handle creativity. We formalize this tendency as a Galton-style regression to the mean …