Humanity and Technology Archives

In this post I am going to talk about Embrace, a baby warmer that was created by the students of The Design for Extreme Affordability course at Stanford University.

Embrace was developed to solve the problem of high mortality rates in premature and low-birth-weight babies in developing countries.

The Problem

Each year, 20 million premature and low-birth-weight babies are born

In developing countries the mortality rate of these children is quite high as incubators in these countries are quite rare.

Moreover, hospitals and clinics in these countries don’t have enough incubators to cater to the extreme demand.

Last but not least, to purchase new incubators was overwhelming from a cost standpoint and incubators received by way of donations lacked simplicity of use and were very difficult to maintain.

So the Extreme Affordability students were challenged to design a better incubator for the developing world.

The Insight

The Embrace team began their need finding in Kathmandu, the capital city of Nepal.

After spending several days observing the neonatal unit of the Kathmandu hospital, the team asked to be taken outside the city to see how premature infants were cared for in rural areas.

They learned two alarming things: First, the overwhelming majority of all premature Nepalese infants were born in these rural areas. And second, most of these infants would never make it to a hospital.

They realized that no matter how good their design for a new incubator was, it would never help these babies if it stayed in a hospital.

To save the maximum number of lives, their design would have to function in a rural environment.

It would have to work without electricity and be transportable, intuitive, sanitizable, culturally appropriate, and perhaps most importantly—inexpensive.

The Embrace Incubator

By the end of class, the team had created their first prototype of the Embrace Incubator.

The design looked something like a sleeping bag.

It wrapped around a premature infant, and a pouch of phase-change material (PCM) kept the baby’s body at exactly the right temperature—and maintained this temperature for up to four hours.

After four hours, the PCM pouch could be “recharged” by submerging it in boiling water for a few minutes.

The Embrace Incubator is small and light, making it easy and inexpensive to transport to rural villages.

The entire sleeping bag can be sanitized in boiling water.

It is far more intuitive to use than traditional incubators, and fits well into the recommended practice of “Kangaroo Care,” where a mother holds her baby against her skin.

Finally, compared to the $20,000 price of a traditional incubator, the Embrace incubator only costs $25.

The product uses an innovative wax incorporated in a sleeping bag to regulate a baby’s temperature.

It stays warm without electricity, has no moving parts, is portable and is safe and intuitive to use.

The Embrace Infant Warmer can be used in a clinical setting, for transporting babies, and in a community setting.

Conclusion

The whole philosophy of Embrace is that you have to be close to your end user to make a really good design.

Last but not least, according to Nobel prize winning economist Muhammad Yunus, reducing the rate of infant deaths actually helps to control population growth.

The theory is that as parents become more confident of their babies’ survival they are more willing to use contraceptives and have fewer children.

In India, a country of over 1.1 billion people, this is a welcomed side effect.

Credit: https://extreme.stanford.edu ( Impactful Solutions for Low Resource Communities)

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.