Designing an airplane has long been an exercise in tradeoffs. A larger airplane with more powerful engines can hold more people and go farther, but is costlier to run. A smaller one is more cost efficient, but lacks capacity. For decades, these have been nearly inviolable constraints that manufacturers just had to live with.
Boeing's new 787 Dreamliner is different. It didn't just redesign the airplane, it redesigned the materials that go in it. Because of its use of composite materials that are lighter and stronger than traditional metals, the company was able to build an aircraft that is 20% more efficient, but sacrifices nothing in terms of capacity or performance.
Typically, this has been a game that only a multibillion dollar corporation can play. The cost of developing and testing new materials costs millions of dollars a year, with no guarantee of any return on investment. Yet today, that's beginning to change. Big data and machine learning are revolutionizing the science of making things and make it available to the masses.
The seeds of the coming revolution, ironically, lie not in the factory floor, but in a Cancer lab. Typically, cancer research has been approached according to the traditional scientific method. A scientist would come up with a theory about how a particular mutation causes a tumor, apply for a grant, perform a study and then publish results. But in 2005, researchers at the National Cancer Institute (NCI), saw an opportunity to go in another direction.
"We said, 'Let's gather data along with some basic analysis, publish it and allow the scientific community to study it,'" Jean Claude Zenklusen a biologist at NCI told me. "We did this because we believed by releasing the data in this way, we could tap into the collective expertise of thousands of researchers across a number of fields and accelerate innovation."
This approach formed the basis for The Cancer Genome Atlas (TCGA), a joint project between NCI and the National Human Genome Research Institute, which began in 2006. It has since sequenced the tumors of over 10,000 patients encompassing 33 types of cancer. "Cancer data has now become open data," Zenklusen told me proudly.
The success of the program turned traditional science on its head. It showed that you no longer need to spend years testing a hypothesis to do science effectively. There is already an abundance of scientific data gathered from endless experiments and most of it is discarded. New data techniques, however, make it possible to make use of it.
When you think about it, a manufacturing process is much like a Genome. DNA codes for proteins that determine how stuff in biological organisms gets built. If you have one set of proteins, you might have brown eyes. If you have another, you might have blue. The same goes for our blood type, our cellular process and virtually every aspect about us.
The materials that go into manufactured products work in a similar way. The initial components, like metals, chemicals or organic materials go through a particular process that gives the material certain properties, like density, tensile strength, electrical conductivity and many other things. These properties then determine a product's performance.
Historically, developing new materials has been a slow and expensive process because while there are countless materials and ways to process them, identifying one that will give you the properties you want is like looking for a very small needle in an almost infinitely large haystack.
