Ambitious art blog building the largest paintings database takes up new challenges
Jason Bailey who called himself “an art nerd trying to trigger an art analytics revolution” created the Boston-based art blog Artnome in 2015. Jason believes that there is currently no single database of known works for the world’s most important artists and Artnome should step up and improve the world’s art historical record with the use of technology and data, which will benefit collectors, museums, and scholar.
Creating paintings database
In the last few years, Bailey and his team have developed a powerful art database with their best endeavours and they stated that Artnome has the world's largest analytical database of known works across their most important artists which can be considered as Zillow/Moneyball for art and artists. However, Artnome didn’t stop there and it has started producing its own research and performing artwork analytics by using the latest technologies like machine learning and computer vision and the precious art data in the database it created. The analytical reports are being published on Artnome’s blog regularly for everyone’s reference.
Proposing new art analytics
At the end of 2018, Artnome published an article on inventing new art analytics in which its collaboration with MutualArt, the leading provider of art market data, analysis and advanced decision support tools has been mentioned, showing Artnome’s database is a major contribution to the art market and its effort to develop the database and conduct further artwork analytics has been recognised. Artnome tried to layout a new approach to conduct descriptive art analytics by using its database of artists’ completes works, shared its data scientist’s predictive analytics using a random forest machine learning model, predicted the auction results of the Barney A. Ebsworth collection at Christie’s, and shared useful artwork data provided by MutualArt and you can read the article here.
Predicting the price of masterpiece
Recently, Artnome has accepted a new challenge: predicting the price of a masterpiece. Artnome’s data scientist Kyle Waters is attempting to create a machine learning model for predicting auction prices, aiming to price the entire art market and bring transparency to the auction house industry. Although the data set is still limited, Waters hopes that Artnome can become “a general starting point for those interested in finding out the price of an artwork” and people can make good use of Artnome’s data set and model open to the public.
Art authentication and more to expect
Carrying out artwork analytics aside, Artnome has also teamed up with art authentication company Art-Recognition to provide affordable art authentication services using artificial intelligence and you can get more information here. Artnome clearly understood the important of big data in terms of art and it has been adopting art tech wisely. It won’t be surprising to see Artnome expand its database to cover other forms of artworks apart from paintings and more exciting development and collaborations on art tech with art tech startups and reputational art organisations and professionals in the near future.