Machine Learning and the Problem of Instrumental Convergence

 


Paperclip Maximizing

What do paperclips have to do with digital marketing? Admittedly, pretty much nothing. The ‘paperclip maximizer’ thought experiment comes from Nick Bostrom at Oxford University. In essence, it looks at the idea that if you tell a machine to optimize to a specific goal it will do so at all costs. If you told a machine to maximize the number of paperclips it produces the machine would eventually start destroying things like computers, refrigerators, or really anything made of metal to make more paper clips once other sources of metal run out. 

Paperclip vs Pay-per-click

If you transform the idea of paperclips into the idea of paying per click this becomes very relevant to digital marketing. In 2020, almost every platform touts some version of artificial intelligence or machine learning to revolutionize campaign performance, which is a boon to everyone. By releasing control to machines, media buyers can focus on additional tasks such as deeper insights for reports and understanding clients’ goals while campaigns continuously improve themselves. They do this by finding what works and drilling down into successful things while avoiding the things that are not driving performance. 

Defining Performance

What exactly is ‘performance?’ The easy answer is whatever your KPI may be. It could be clicks, it could be video views, it could be many things. However, this is already a simplified goal. If you are running a traffic campaign, the marketing goal should not just be to “get more clicks”. The goal should be along the lines of driving qualified users from a target audience to an advertiser’s website and increase the favorability of a brand. The goal is something bigger than a single metric. A KPI can be a stepping stone, or possibly the most granular thing you can track, but it is not the final objective. A truly successful campaign will not simply be the campaign that drove the most clicks at the cheapest price point. 

What Machines Lack

Context. Context is something that a robot has not yet mastered. As a marketer, I know that increasing clicks, directionally, should push me closer to my goal. The machine knows this too, but this is all the machine knows. It will endlessly optimize to a single goal. Maximizing the number of clicks given a fixed amount of budget. This can lead to unintended consequences. A machine might say only run display banners and forget about high impact formats such as video and connected TV. The machine might push 100% of impressions into in-app environments. The machine would say never buy another out of home ad ever again. The machine would never know to build brand awareness because there is no optimization point that it can use. 

Machines Need Guidance

Coegi understands the role of the media buyer is not going away, but it is morphing. There is a new symbiotic relationship between buyer and machine which empowers them to not maximize paperclips, rather they can maximize your brand as a whole. Successful media buyers need not be someone who can spend 80 hours per week finding every combination that leads to success. Most of these tasks can be done through harnessing technology and freeing up time to look at the media plan from a higher level.  These benefits go directly to our clients in the form of more time dedicated to listening to client needs, smarter digital media plans, and ultimately higher performing campaigns. 

Author: Jake Amann, Data & Technology Manager