Data is the new oil! And wearable medical devices are the oil fields of our age.
The 2010s ushered Machine and Deep Learning, a breakthrough in the ability of computers to use data to make predictions. The best way to understand the magnitude of this breakthrough is to compare it to the invention of computers. In the 1940s, humans (known as computers!)performed all necessary calculations needed for science, research, accounting, etc. At a speed of tens to hundreds of calculations per hour, global computing capacity was likely in the billions of calculations per year. Today’s fastest computer calculates a thousand trillion calculations per second! Today’s cell phones calculate trillions of calculations per second or what it took the entire human race TEN years to calculateless than 100 years ago. This explosion in calculation capacity transformed every aspect of our life.
Machine learning will do the same to analysis, decision making, prediction and pattern recognition. With all the calculating ability that computers made possible, humans were still needed to direct which calculations to make which we call programming (human calculators became computer programmers). Humans also made sense of the results of all those calculations and analyzed the resulting outputs to inform decisions in accounting and finance, investing, and engineering. Humans were also need to recognize patterns in the numbers and use them to predict future outcomes such as in diagnosis, prognosis and treatment. This is rapidly changing! Machine learning duplicates humans’ ability to perform these functions just as computers duplicated our ability to calculate. Machine learning is accelerating the performance of these functions faster than computers accelerated calculation. Machine learning algorithms are now able to recognize pictures faster and more accurately than humans, they can recognize speech better than humans and pretty soon will be able to drive cars safer than humans.
To do so, however, machine learning needs HUGE amounts of data. Machine learning algorithms need data to learn patterns and to make decisions.