Moravec’s paradox is the observation by artificial intelligence and robotics researchers that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor skills require enormous computational resources.
The principle was articulated by Hans Moravec, Rodney Brooks, Marvin Minsky and others in the 1980s.
Moravec wrote in 1988, “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.
Hans Moravec laid the basis of this paradox and considers one possible explanation to be rooted in evolution.
Evolution has taken millions of years to evolve the current sensorial abilities of humans.
Some examples of skills that have been evolving for millions of years: recognizing a face, moving around in space, judging people’s motivations, catching a ball, recognizing a voice, setting appropriate goals, paying attention to things that are interesting; anything to do with perception, attention, visualization, motor skills, social skills and so on.
These skills that we possess are mostly unconscious and come to us without any thought; we seem to do them without any strain, making what looks difficult to be fairly easy. These unconscious processes are difficult to reverse-engineer and teach to the computers, thus increasing the complexity of the problem.
Some examples of skills that have appeared more recently: mathematics, engineering, human games, logic and scientific reasoning. These hard things that we make computers solve are based on a deliberate process we call reasoning. This process is conscious, one of abstract thought and closely related to brain functions in humans that have evolved recently (less than 100,000 years ago) in the context of the long evolutionary process.
A compact way to express this argument would be:
- We should expect the difficulty of reverse-engineering any human skill to be roughly proportional to the amount of time that skill has been evolving in animals.
- The oldest human skills are largely unconscious and so appear to us to be effortless.
- Therefore, we should expect skills that appear effortless to be difficult to reverse-engineer, but skills that require effort may not necessarily be difficult to engineer at all.
Human & Machine Augmentation
Despite the huge amount of enterprise data available, half the picture is missing. Systems of records like ERPs only record what happened. And what happened is the results of human decisions made “outside” systems based on a context which can soon be forgotten or lost.
Example: You detect an oversell situation and expedite replenishment, but due to external factors some customers are cancelling their orders, and you end up with excess costs and stocks. A bad outcome for the replenishment decision, and a lesson to remember if it happens too frequently.
The real world is different, and context can make a lot of difference. We’re able to assess and understand context (because of evolution) in ways that machines cannot.
The lesson for the business world is that utilizing AI is not just about replacing manual work by computation. Instead, it’s about respective augmentation between humans and machines – leveraging each other’s strengths, timely and consistently.
We sense danger, threats, rewards and apply judgment, unconsciously most of the time. We make decisions based on context that may violate rules or convention – sacrificing the queen at chess, driving through red light if necessary, or expediting a drug by helicopter for a life-threatening emergency.
With a human/machine augmentation model, we are moving from people doing the work supported by machines to machines doing the work guided by people.
It’s not about us v/s them
The concept that humans and machines have different strengths played out in a research from Harvard. In the study, a breast cancer detection algorithm was able to detect cancer cells 92% of the time. However, the doctors were able to identify cancer cells 96% of the time.
This clearly shows that humans are better, right? But wait—the next finding was perhaps the most telling part of the study. By combining the algorithm with human experience and intuition, the team was able to identify more than 99% of cancer cells. This blending of strengths of humanity and technology points to an incredible opportunity to solve the most complex problems that the world is facing today.
The idea that “technology will fix things” is misguided.
Machines expertly handle repetitive and automated tasks and will always be faster and more precise. They might be good at tasks closely related to brain functions in humans that have evolved recently(Algebra, Logic, Abstract Thinking etc.), less than 100,000 years ago, in the context of the long evolutionary process.
However Technology cannot fix bad processes, poor management practices, or failing employee morale. Without people, there is no innovation, no strategy and no connections with customers.
The uniquely human skills of creativity, innovation, adaptability, empathy, integrity, and imagination which have evolved over a million years are becoming increasingly critical to success, and these skills cannot be taken over by machines.
The new era is going to be about the handshake between humanity and technology wherein machines will be doing the work guided by people.