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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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News

Unveiling Our New Business Lead: Nick Stoynoff

The Art Scene app is breaking new pathways under new business leadership with the introduction of Nick Stoynoff, a multifaceted visionary with a passion for art, technology, and empowering all. Budget Collector is excited to introduce Nick, a former Uber-alum, to our audiences and is

Visualizing Color Usage

Previous Blog TLDR: Our goal of this post is to implement color quantization in R, compare several methods of detecting segments of an image, and start creating the visualization UI. It’s no surprise that this topic naturally lends itself to some preliminary data exploration and visualization, and

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