In a disruptive & ever-changing world, what matters the most? Depth or Breadth?
I fear not the man who has practiced 10000 kicks once, but I fear the man who has practiced one kick 10000 times
For generations, we have been practicing the concept of Depth through Specialization and by developing skills & expertise in our chosen fields. This specialization formula has been working great and was leading to innovation in the respective domains till the world started to witness newer & unprecedented problems.
Also, on the other side, Breadth is often perceived as “jack-of-all-trades, master-of-none”. The question remains, should we strive for depth or breadth?
What is the Dichotomy of Depth and Breadth? Let’s examine through some bipolar views:
- In depth, we learn and gain knowledge. In breadth, we experience and gain wisdom.
- In depth, we develop skills & expertise. In breadth, we see analogies & patterns.
- In depth, we build crystalized intelligence. In breadth, we build fluid intelligence.
- In depth, we find relevance. In breadth, we find relatedness.
- In depth, there is specificity. In breadth, there is applicability.
Depth is about Specialization; Breadth is about Diversification. In depth, one is a specialist. In breadth, one is a generalist.
What’s our Problem statement today?
Today’s world is characterized as being VUCA; Volatility, Uncertainty, Complexity, and Ambiguity in which the complex problems have surpassed & over-powered the skills or expertise that we had. So complex problem solving required much more than the skills & expertise or what the Innovation had to offer.
What odds are our learnings against their applicability?
In the research paper “The Two Settings of Kind and Wicked Learning Environments” written by Robin M. Hogarth; Hogarth stated that inference involves two settings: In the first, Information is acquired (learning); in the second, Situation it is applied (predictions).
- Kind learning environments: The information in the learning environment closely matches the situation in which the decision is to be made. Kind environments provide useful feedback to the decision-maker.
- Wicked learning environments: There are mismatches between the information in the learning environment and the decision situation, which are likely to lead to mistakes in decision making. The feedback is poor, misleading, or missing.
Our environment is more Wicked than ever before where we often find mismatches between our learnings & their applicability.
The Devil’s advocate:
In the book Range: Why Generalists Triumph in a Specialized World author David Epstein presents a contrarian argument that specialization should be the exception rather than the rule. A couple of snippets from the book:
- “Knowledge is a double-edged sword. It allows you to do some things, but it also makes you blind to other things that you could do.”
- “Analogical thinking takes the new and makes it familiar or takes the familiar and puts it in a new light and allows humans to reason through problems they have never seen in unfamiliar contexts.”
Clearly, David Epstein emphasizes the need for ‘Analogical thinking’ for successful problem-solving. Epstein goes on to say “The bigger the picture, the more unique the potential human contribution. Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly.”
If Human potential contributes uniquely in a bigger picture; what’s the scope of AI & Machines?
There are several paradoxes in the world of AI & Machines, mostly to derive that Humans & Machines can have complementary skills & strengths; we can have a look at couple of paradoxes below:
- Moravec’s paradox states that the difficulty of reverse-engineering any human skill to be roughly proportional to the amount of time that skill has been evolving in animals. So, some of our oldest human skills that appear effortless or unconscious are difficult to reverse-engineer.
- Polanyi’s paradox summarized in the slogan “We can know more than we can tell”, Polanyi’s paradox emphasizes the Tacit knowledge in humans.
So, there’s enough scope for AI & Machines to complement us in doing what they are best at (than us); leaving us enough space on stuff that comes naturally to us (from evolution).