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Art Scene Disrupting the Market Through Inclusion


Art Scene (formerly AI Art Advisor), the next-generation art discovery and evaluation app, is disrupting the art market by combining cutting-edge technology with deep insights into art and aesthetics. Our proprietary "artistic quotient" machine learning system helps users discover their unique taste in art and navigate the art market with confidence. With Art Scene, collecting art is no longer limited to the elite few - our app is democratizing the market and making it accessible to everyone.



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machine learning Tag

Smartphone notifications creating a sense of urgency and FOMO

Moving Beyond FOMO

Ebonique Boyd (product lead) examimes the implications of the Fear of Missing Out (FOMO) in app design, arguing for a more user-centric approach that prioritizes satisfaction and wellbeing. We delve into the concerning rise of anxiety among young adults, drawing links to certain design practices.

Data Cleaning

This team from Georgia Tech discuss the process of data cleaning and analysis in the context of studying color patterns in artworks. They used a dataset from the Art Institute of Chicago, consisting of 122,435 artworks, for this purpose. The data was cleaned to extract

Giving Color Vision to a Machine

This blog post titled "Giving Color Vision to a Machine" by Global Data Artists delves into the intricate process of training a machine to recognize and categorize similar colors within different images. The authors detail their approach of employing KMeans clustering on image data, confronting

The New AI

This article explores the use of clustering algorithms and art museum APIs to extract dominant color palettes from artworks, with a focus on the collection of the Art Institute of Chicago. The authors demonstrate how to programmatically access image and metadata, extract color statistics, and

Art & Intelligence

The article discusses a project to analyze patterns in the use of colors in paintings by using a 3D K-Means clustering algorithm to extract the dominant colors in images and their associated metadata, and then using this data to perform time series and spatial regression.