Since the very first line of code was written, there have been software bugs (one apocryphal story traces the origin of the term to 1946 when an actual moth was found trapped in the relays of a Harvard mainframe – and like all good code, was dutifully documented by being taped into a notebook). As a result, the development of debugging tools has closely mirrored the rise of modern software. From symbol tables and breakpoints to the sophisticated predictive code profilers of the 21st century, better debugging tools have enabled us to create sophisticated and smoothly functional software.
Now that 2017 resolutions have been made (and, perhaps, already broken), I’m going to go on record with the prediction that 2017 is going to be the year of the Smart Lab. From industrial and commercial sectors, to the smart home, smart technologies have made major inroads in every category except one — scientific research.
As a technologist and a scientist, it is very gratifying to see the technologies that have transformed business and consumer markets being applied to the challenges in the laboratory and beyond. Miniaturization of components, sensors, machine learning, wireless communications, and big data have shaped other industries, and are finally positioned to make a huge impact in the science-based organizations that are doing the most for human-kind.
I had the honor of opening the MM&M Transforming Healthcare Conference in New York on 5 May, 2016, kicking off an agenda that explored all phases of the industry, from understanding the patient to the various clinical trials stages and considerations. Props to the MM&M team for assembling a diverse and engaging series of speakers and viewpoints, and thanks for including me.
Disruptive innovation has made its way through most industries as a driving force for advancement and change.
But, the scientific research sector, whether public or private, has remained largely untouched by the benefits of disruptive innovation. The result? Two of life science’s biggest hurdles – astoundingly high lack of experimental reproducibility, and the fact that it continues to take 10+ years and over $2 billion to develop a life saving drug in the year 2016 – are still ongoing challenges.
Research scientists are swimming in data. Every day. Every experiment. The data and the integrity of the data are paramount.They provide documentation of scientific processes and hold keys to success, failure and reproducibility. But there is always missing data in most experimental research – information that you should have collected that gives context to the experimental data itself.
Virtually every startup team has ambitions to solve the world’s most pressing problems. Elemental Machines is no different.When we began contemplating our next adventure and looked at the world around us, our focus kept returning to the fact that, as a species, we have been able to survive (and thrive) on this planet because of our ability to invent our way out of the holes we’ve found ourselves in. This unique ability to innovate and discover new technologies has been a hallmark of human ingenuity. So we decided to focus on helping scientists discover more quickly and less expensively.