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Growing up in Massachusetts, self-confessed art nerd Jason Bailey was taken to the Museum of Fine Arts (MFA) in Boston at weekends by his father, on trips that would sow the seeds for Artnome, a US company aiming to trigger an art analytics revolution using Artificial Intelligence (AI).
An artist at heart living with a family of engineers, Mr Bailey was guided by his Dad through the MFA’s different civilisations and time periods and the core of his identity was built up.
Years later, the infectious zeal Bailey has for an art’s insurrection was lit when he read ‘Provenance: How a Con Man and a Forger Rewrote the History of Modern Art’. Its author argues that an assumed rate of forgeries and misattributed artworks in museums is somewhere between 15 to 20% of all pieces.
For Bailey, it was like someone was forging his bible. “Art’s been my identity since I was a little kid, and to realise that it was so susceptible to being rewritten kind of blew my mind,” he told Turbare.
“I had at the time spent 10 to 15 years working in big data, machine learning and analytics in my day job for companies that specialise in those areas. So I thought, well, these giant companies like the GE’s and Toyota’s, Johnson and Johnson’s of the world have these massive customer databases, but where’s the massive database for art and artists? If you looked online on Google and you tried to figure out how many paintings that Rothko made, it didn’t exist publicly online which seemed crazy to me.”
Deciding to tackle the forgeries and opaqueness in the art market, Bailey founded Artnome, which has been at the vanguard of a growing number of enterprises from the US to Kenya, who are chasing the ‘Holy Grail’ for art lovers and investors alike; accurate valuations for art delivered via machine learning models.
Artnome’s team of data scientists have crunched epic amounts of data to create the biggest analytical database of artwork in history. Starting with the western canon, the firm pumps its AI model full with the nitty gritty facts about famous artworks – including the dimensions of a piece, its year of execution, materials, and sale prices – from publicly accredited sources like auction data, museum records and Catalogue Raisonné.
The model includes information from over 10, 000 artists whose work has sold over the last 15 years, beginning with lesser known pieces fetching $100 USD, to Leonardo Da Vinci’s “Salvator Mundi,” that sold for a colossal $450 million in 2017.
This data – which includes many artist’s complete oeuvre – is used within the AI model to predict how much people are willing to pay for artworks at auctions, and instruct what’s important to determine valuation of a particular piece. Factors like the dominant colour of an artwork, clues that suggest the image is part of a wider collection or a self portrait, and recent auction prices are calculated to spell out a realistic price.
Artnome says machine learning models will eventually function like Zillow, an American online real estate database that can give a “Zestimate,” which estimates real estate prices for all homes on and off the market in the U.S, and which acts as a general starting point for buyers.
Artnome’s new model faces a few major challenges, however. Its machine learning system struggles to predict the works that sell for the most at auction, and the model also lacks information on the condition of the painting, which could impact the artwork’s price if it contains creases and other damages.
The US firm is making their databases public, and it’s anticipated that with good clean data available to them, tech-savvy teams from around the world will now be encouraged to run their own analysis, with new contexts emerging. Once bigger inventories are established, more precise analytics that highlight what makes an artwork valuable, exceptional or rare can be initiated. Details underlying what’s unique about each individual artwork can boost the transparency within the global art market, while also helping smooth-out early teething problems with their existing models. This is the blueprint for the art analytics revolution.
AI’s impact on the art world is yet to be fully understood, but for Bailey, big data could also have the power to help radically transform prospects for marginalised artists.
Since its inception, the global art market has predominantly been conservative, “rich and white and slow to move,” he says, but now, with tastes moving towards equality, big data could help shift perceptions towards valuing underrepresented artists.
Machine learning models are being used to give starting point valuations for art from Africa and the Middle East on the turbare.com platform, which is providing a stage for a growing audience for art from outside the perimeters of the western art world. Through Turbare’s AI model, art collectors can receive price valuations similar to the model Artnome uses, which will provide greater transparency for collectors buying online, a higher degree of certainty in their investment, and less subjectivity in the market.
The model also provides an opportunity for emerging artists who haven’t had the necessary exposure to quickly receive valuations on their artwork. How their pieces compare with other artists can be computed by the model, based on similar measurable attributes to other works. This will provide further opportunities for excellent artists from the continent to showcase their talent, and earn a living off their creativity.
Art from Africa and the Middle East is still under-explored but are growing segments of the international market, and remarkable returns can be achieved. The ongoing global reckoning with the Black Lives Matter movement could also translate into more spaces being created for under-represented artists, with collectors increasingly keen to buy works from diverse places.
A new generation of collectors is emerging, and they’re shifting online as a consequence of the ongoing pandemic. The 2020 UBS Global Art Market Report shows that 70 per cent of millennial collectors, asked in a survey, said they felt more inclined to buy art online since Covid-19 began. Yet consumer trust in the prices offered online is still one of the biggest inhibitors in growing markets.
As a response to a rapidly changing world companies like Artnome and Turbare appear already ahead of the curve.
Despite fears across the industry in the beginning of the Covid-19 pandemic, buyers have remained active. The UBS report found that 59 percent of the collectors surveyed said the pandemic had increased their collecting interest; with 70 percent of millennial collectors said they felt more inclined to buy art online now.
With galleries facing continued restrictions, and added strains placed on human valuations for rapidly expanding global markets, machine learning models will likely prove a valuable tool for a new generation of collectors.
“Data will really open up a new marketplace for younger folks who are interested in a more diverse group of artists. Tastes are moving more towards equality, and that’s going to change this market pretty dramatically, and it will improve valuations and sales for historically undervalued artists,” Mr Bailey says.
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