With AI’s ability to automatically produce content and process complicated datasets with high accuracy, museums worldwide are exploring ways in which this innovative technology can help them better achieve their missions and advance accessibility efforts. Through case studies, learn about three applications of this technology: content digitalization, language accessibility, and visual description.
October was filled with new debates on AI policies, a halt to one of the most significant AI safety bills in the United States, and a host of new AI developments in video, music, and even podcasts.
Arts and cultural nonprofits often operate in multiple markets for earned and contributed revenue. With increasing attention on data-driven decision making, effective market orientation often depends on effective data use. This fundamental collection, transmission, use, and interpretation of data within an organization collectively contributes to what can be considered an organization’s data culture.
Museums have increasingly begun to volunteer their spaces as polling places, acting as a democratic vehicle for their communities and adding to the greater fabric of America’s election infrastructure. In doing so, they can support local democratic processes and prove to their communities that they are not unbreachable temples for the elite, but open platforms for the people.
As exemplified by the Hollywood strikes of 2023, we find ourselves in a new era of Artificial Intelligence in film and television. While studios are looking to cut costs, performers seek job security, and consumers seek quality content. Through research conducted in collaboration with facial motion capture technology provider Faceware Technologies Inc., Master of Entertainment Industry Management students at Carnegie Mellon sought to gain insight on how AI-generated content will transform existing industries, including how this technology could affect employment in the entertainment industry, content development, budgets, contracts, legislation, and privacy rights.
What is the difference between a Data Analyst and a Data Scientist? What is the most demanded programming language for data workers entering the cultural field? And what skills are most important for those interested in working with data? AMT-Lab contributor Julia Lewis recently attended a panel which addressed these questions and more. Read her thoughts here.
