For humans, learning is an inevitable task. From birth we are constantly analyzing our situations around us to learn about the world we live in. For more than twelve years of our life we even go to a designated area for eight hours a day to focus solely on learning. It is second nature for our species to gain knowledge through our own experience or through studying the experiences and findings of others. If there are more than seven billion people on Earth who already learn from their surroundings and trillions more living things that do the same, why would we want to teach machines to learn?
To begin answering this question, we first have to look at what it means for a machine to learn. Arthur Samuel, a pioneer in the field of machine learning, described machine learning as a computer’s ability to look at large data sets and “learn without being explicitly programmed1.” Basically, the computer builds analytical models based on the data it is given, helping people make more accurate data driven predictions or find hidden insights within the data. There are various models and approaches for machine learning which makes the hardest part about machine learning figuring out what model is most useful to extracting functional insights about the various data the computer pursues.
The ability to use data to predict future outcomes is is not a new revelation or recent innovation; in fact, we have been using statistics for hundreds of years.
“Just like how humans constantly learn, machines can be programmed to do the same”
The recent push for machine learning is driven by the application of complex mathematical calculations to big data.2 Machine learning has been applied to numerous industries from transportation to health care. Numerous Fortune 500 companies use this technology; for example, Amazon’s use of machine learning for their recommendations feature, or ExxonMobil’s use for their analytics research in the energy sector. Within health care, machine learning is creating numerous discoveries everyday; Medecision, a population health management solutions company, used machine learning to identify 7-8 unique variables that help prevent diabetic patients from getting hospitalized, one of which was getting the flu vaccine.3 Their insights on something may seem arbitrary, but are actually incredibly important for the patient’s health and wellbeing.
By finding unique correlations in data, we can applying that knowledge to future situations, which is incredibly useful and even life- saving. Just like how humans constantly learn, machines can be programmed to do the same which would help improve our quality of life.
References:
- Palmer, S. Can machines really learn? http://www.shellypalmer.com/2015/03/can-machines-really-learn/ (accessed 10/29/16), part of Shelly Palmer.
- Machine learning: what it is and why it matters. http://www.sas.com/en_us/insights/analytics/machine-learning.html (accessed 10/31/16), part of SAS.
- Morgan, L. 11 Cool Ways to Use Machine Learning. http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/11-cool-ways-to-use-machine-learning/d/d-id/1323375?image_number=3 (accessed 10/30/16), part of Information Week.