Why “impossible” solutions are already around us — without our notice.
Too often companies try to solve their challenges and pains by going out onto the “market of solutions”. They ask some peers, to look at G2, Captera, or simply Google for it. Then vendors are evaluated. Most of the time though you find that there is no perfect solution yet available.
This article here is to remind you that this is actually not true! Chances are high that things ARE doable. Solutions ARE already possible. You just need to be brave enough, reach out to some talents and pioneers and let them do what can work — if you just try.
“Aren’t the major invents been already made? The wheel, the lamp, the computer?” This is what a fellow Ph.D. student asked me back then in 2000.
I was shocked that he ask such questions.
22 years later I need to conclude: Not only is it amazing which investors are popping up year by year. Even more: No matter which mission impossible you can think of, chances are that the solution is already around us. You just need to find it.
You don’t believe me?
You will — if you follow me back in time and let me guide you thru 3 examples from my field of expertise “Unearthing success drivers from data”
The History of Key Driver Analysis
The basic concept behind driver analysis is the multiple regression which was published by Legendre in 1805 and by Gauss in 1809.
Unilever is using it since 1919 for Marketing Mix Modeling.
Even today too many marketing executives view the method as “advanced analytics”. It is still more common that practitioners to compare KPS or look at correlations instead of using regression.
Even worse, many software companies use multiple regression and call is “Artificial Intelligence”.
The potential that is available since 1805 is still 200 years after being largely underleveraged.
The History of Machine Learning
The invention of the backpropagation concept was the ultimate breakthrough for machine learning.
Long time this invention was attributed to Rumelhart, Hinton & Williams published in 1986.
Just yesterday I learned that the technique was independently discovered many times, and had many predecessors dating to the 1960s — the earliest Henry J. Kelley in 1960 and by Arthur E. Bryson in 1961
Imagine! A lot of what we know as modern Artificial Intelligence is already possible since the 1960s. Its 60 years ago!
And I would not be surprised to learn that lots of pilot applications already happened in the 60s and 70s. We just don’t know about it.
The History of Causal Machine Learning
The concept of causality was a long time just a matter of philosophy -mostly known by the work from Hume (1748).
Later the statistical framework of the multiple regression served as a means to calculate causal impact. Later major contributions followed by Granger in 1969, Pearl 2000 and Rubin 1974 added techniques to identify causal directions from data.
My own contribution in this space focusses to combine machine learning with causal inference. This for a practical reason: It turns out that it’s the most versatile, predictive, explanatory and practical way of modeling, reasoning and predicting.
I am publishing my work since 2001. It feels a bit like a treadmill. There is progress but large growth percentage on something small is still tiny. There are amazing success cases.
Still, the vast majority of possible applications don’t know about it. This will probably not change in the decades to come.
Now looking back to Key Driver Analysis and to Machine Learning gives me patients. When AI needed 60 years to prosper, I can not expect Causal AI to do in 20.
MY TAKE AWAY
Imagine you are in 1880 running a consumer product business. Key Driver Analysis would be available already to perform Marketing Mix Modeling (you just need to run it by paper and pencil).
Imagine you are in the 70s building one of the first home computers like the Mac — you could have built-in Artificial Intelligence already back then — if you would have understood the power of it.
Imagine you are who you are today. You are eager to understand better than anyone what are the hidden causal reasons, the ultimate actions that most effectively drive success. You could run causal machine learning already today. Learn how things relate and influence each other.
Just DM me 😉