Clarity in complexity

RAINBOW – Clarity in Complexity

RAINBOW began one evening when there was actually enough information – and yet the bigger picture was getting lost. Decisions, dependencies and possible consequences were all in front of me at the same time. The more I tried to hold everything together, the less clear it became what truly mattered. I realized that something crucial was in danger of getting lost in all of it. And suddenly there was a thought that seemed to have nothing to do with all the information – and yet it mattered more than anything else. It pushed its way through all of it – and stopped me at exactly the moment when it mattered most. The thought stayed with me through the night. The next morning, it was still there just as vividly as it had been the evening before. And at the exact moment I opened my eyes, there was a rainbow in the sky. Until then, rainbows had never meant anything to me. But in that moment – with that thought and that light in the sky – it felt as though the world were saying: Everything will be all right. That is where the name RAINBOW comes from.

Only later did I understand what had stayed with me from that moment: Information is not always what is missing. Often, what is missing is the context that gives it meaning. That is true for an individual just as it is for companies, teams and institutions. A number can be correct. A document can be complete. A decision can make sense when viewed on its own. And yet something essential can still be missing: What influences what? Where do dependencies arise? Which piece of information changes the meaning of another? Which consequence only becomes visible when several pieces of information are considered together? And what is truly relevant to the situation at hand?

RAINBOW brings relevant information together in a way that makes the connections visible – in everyday personal life as well as in projects, processes, risks and decisions within companies and institutions. The same principle applies to the technology behind RAINBOW. Language models are trained on enormous amounts of information. For a specific task, however, they need context. What matters is not simply how much context there is, but which information is relevant to the situation at hand and how it relates to the rest. One principle applies:

RAINBOW does not decide for people. RAINBOW should help people see the crucial connections before they decide. Responsibility remains where it belongs: with people. Not as much information as possible. The information that matters – in the right context.

Oh, right. Thank you, little rainbow. 🌈