Information is only for a few countries in the world as valuable as in Estonia with its 1.3 million inhabitants. The small country in Eastern Europe is still the only country that enables citizens to vote via the internet (Cavegn, 2017). Additionally, all citizens possess an electronic ID-card in which their personal data is stored. Although doctors or civil servants can easily access this information, citizens can still decide what will happen with their data and in addition they get a message whenever someone tries to view their information. Every action leaves a digital trail which can be tracked via blockchain technology. Also, Estonia was one of the first countries to adopt a digital tax and electronic banking system. It’s safe to say that data and connectivity play a huge role in the Estonian society (Alonso, 2017).

So why is the rest of Europe not as progressive as Estonia? Politicians often fear that the rising involvement of technology in our daily life will help sustain tax evasion and privacy violations. They rather focus on the disadvantages and fail to see the opportunities that digital life and commerce can produce (Herszenhorn & Plucinska, 2017). For instance, just recently Uber got banned in London (Cox, 2017).

On the other hand, Estonia strongly believes that Europe needs to catch up in the field of innovation. Nowadays the list of biggest internet companies only consists of American and Chinese companies. Estonia’s director of EU affairs, Klen Jäärats, said: “They’re not pessimists because they don’t see the potential of digital. They just exaggerate the risks. But this is life, anyway. Things are going online. It is happening and you need to be ready for that” (Herszenhorn & Plucinska, 2017).

The situation in Estonia shows the importance of information and how a country can benefit from this. If done right, combining information and technology can help make life easier for both citizens and governments. However, information is valuable and it needs to be handled with care to make sure that it does not fall into the wrong hands.

References:

Alonso, S. (2017, October 2). De EU mag wel wat harder rennen, vindt Estonia. Retrieved from https://www.nrc.nl/nieuws/2017/10/02/de-eu-mag-wel-wat-harder-rennen-vindt-e-estonia-13293644-a1575670

Cavegn, D. (2017, October 5). Why is e-voting available only in Estonian? Retrieved from http://news.err.ee/634456/why-is-e-voting-available-only-in-estonian

Cox, J. (2017, September 22). Uber London ban: App loses licence to operate in capital. Retrieved from http://www.independent.co.uk/news/business/news/uber-london-ban-live-updates-tfl-ride-hailing-app-prive-hire-licence-not-renewed-expires-taxi-a7960856.html

Herszenhorn, D. M., & Plucinska, J. (2017, September 28). Europe’s ‘e-believers’ brave skepticism in Estonia. Retrieved from http://www.politico.eu/article/europes-e-believers-brave-skepticism-in-estonia-digital-tech-summit-tallinn-eu/

When online retail and auctioning were established it was technologically revolutionary and it disrupted the traditional markets. Nowadays our world is dominated by online players like Ebay, Google, Amazon and many more. In this blog we will elaborate on how the art industry may be disrupted by machine learning.

It is clear that different technologies have changed our ways of consuming and buying. But one particular technology, machine learning, will perhaps alter the normal ways of doing business even more. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed (Samuel, 1959). Machine learning has been used in data analytics and predictive analytics with the help of algorithms, which have been used by many companies and has become a buzzword in the corporate tech-world (Koza et al., 1996). However, algorithms and mathematical machine learning are also applicable in industries that seem more abstract. An example of an abstract industry is the art industry. But first we will elaborate more on auctions. Sales auctions are auctions with one seller and many buyers, there are four traditional types of sales auctions: the English auction, Dutch auction, first price sealed-bid auction and second price sealed-bid auction, also known as the Vickrey auction.

Art auctions start with an appraisal of the acquired artwork, this decreases the information asymmetry problem. Every art auction has a catalog and a pre-sale exhibition. If an artwork does not have any bids, the artwork will be bought-in. If it’s sold, the auction house takes a seller’s commission and the rest of the proceeds are given to the consignor, the owner of the artwork. Oftentimes the sale of an artwork is the result of one of the “three Ds”: death, divorce, or debt (Goldstein, 2012).

Machine learning can be implemented in the art industry and could affect it in multiple ways. First, algorithms have decreased the uncertainty in the art market, because the value of an artwork will be determined based on historical data and mathematical models. Sellers of art are reluctant to sell their artwork, because they worry that an unsuccessful sale will “burn” the value of the artwork. Algorithms offer more convincing and accurate sales forecasts enabling more assurance to the seller. Furthermore, information asymmetry decreases from the buyer’s side. The artwork can be benched to historical data and objective mathematical models, opposed to the subjective knowledge of the valuer. Lastly, auctioneers and dealers are not needed anymore. Machines can value the artworks cheaper than humans and they will not ask for a commission fee.

It is clear that the impact of machine learning in the art industry has benefits for sellers and buyers. But will the art world just like other industries transform from a high-touch industry into a high-tech industry? The art industry is one of the few industries that captures true human creativity and imagination because valuing artwork is very subjective. So to what extent can machines and algorithms replace human subjectivity? Only time can tell.

References:

Goldstein, A. M. (2012). A Beginner’s Guide to Art Auctions. Retrieved from http://www.artspace.com/magazine/art_101/art_market/art_101_a_guide_to_auction_lingo-5558

Koza, J. R., Bennett, F. H., Andre, D., Keane, M. A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design ’96. Springer, Dordrecht. pp. 151–170.

Samuel, A. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. 3 (3).