Artificial intelligence still requires intelligence
In the world of business and design, we have started using terms like “algorithm” and “machine learning” as magic calculations for problems we would like to gloss over. These terms often become blockers for deeper problem solving and can stall even the most worthwhile projects.
“We’ll figure it out with an algorithm” used generally is similar to the fantastic conversations my daughter and I had when she was six. She would come up with inventions for amazing things like solar power in a backpack and magic windows. If she wanted to skip over something, she included, “and then you put some potion on it.” This would quickly get her to the other side of the idea. Soon after that, she moved past solving problems with potion and is still coming up with inventive solutions for many things.
Describing an algorithm in this general way has become the latest version of putting some potion on it. Often, the real solution is not particularly complicated, but does require some deeper thought. As strategic designers, we can all get more involved in emerging artificial intelligence by breaking down the problem and getting more deeply involved in the discovery process.
Often the first part of creating the “potion” is to find a task that can be done manually. If the goal is analyzing text in articles, for example, the first task should be taking the text from articles and finding the right words, word pairs, or sentences that would get to the result we are looking for — make sure it works before you automate it.
The Flame Index was a service I cofounded with a three-person team in 2010, which scanned thousands of news sites 24 hours per day and published a real-time ranking of companies generating the most negative press. It sounds like an impressive algorithm, but the starting point was the manual and time-consuming process of reading news articles and creating a list of words and word pairs that seemed to define the issues we were looking for. The spreadsheet analysis work helped us dig deeper into the problem we were hoping to solve. Terms like “bankruptcy,” “oil spill,” and “explosion” were prime candidates for how we were analyzing issues — they revealed how “on fire” each company had become in the media. Over the five-year life of the company, we manually identified more than 2,000 words and word pair terms for the ranking.
For each of the terms on the index, we assigned a score to rank negative news.
