The physical world is full of unique details that differ from place to place, person to person, and item to item. In contrast, the world of software is built on abstractions that make for relatively uniform coding environments and user experiences. Machine learning can be a bridge between these two worlds.
Software is largely homogenous. When a search-engine company or smartphone maker upgrades its product, users all over the world are offered the same upgrade. This is economically efficient because, despite high fixed costs for design and manufacturing, it results in low marginal costs for manufacturing and distribution. These economics, in turn, support huge markets that can finance innovation on a grand scale.
In contrast, the real world is heterogeneous. One city is surrounded by mountains, another by plains, yet another by seas. One has paved roads, another dirt tracks. One has street signs in French, another in Japanese. Because of the lack of platforms and standards — or the impossibility of creating them — one size doesn’t fit all. Often it fits very few.
This is one reason why it’s difficult to design a self-driving car. Making a vehicle that could find its way around safely would be much easier if every city were built to a narrow specification. Instead, self-driving systems must be able to handle streets of any width, stop lights in any configuration, and a vast array of other variables. This is a tall order even for the most sophisticated machine learning systems.
Software companies have been successful at getting users to adapt to one-size-fits-all products. Yet machine learning could help software capture and interact with the rich diversity of the physical world. Rather than forcing every city to build streets of the same composition, width, color, markings, and so on, we can build learning algorithms that enable us to navigate the world’s streets in all their variety.
We have a long way to go on this journey. Last week, I wrote about how Landing AI is using data-centric AI to make machine learning work under the wide variety of conditions found in factories. When I walk into a factory, I marvel at how two manufacturing lines that make an identical product may be quite different because they were built a few years apart, when different parts were available. Each factory needs its own trained model to recognize its own specific conditions, and much work remains to be done to make machine learning useful in such environments.
I hope that you, too, will see the heterogenous world you live in and marvel at the beautiful diversity of people, buildings, objects, and cultures that surround you. Let’s use machine learning to better adapt our software to the world, rather than limit the world to adapt to our software.