A Machine Learning Breakdown
“ML is the future!”. This is a statement that you have probably come across in some form recently, as machine learning seems to be all the rage in the tech realm. But what exactly is machine learning? How has it managed to position itself, in a numerous amount of fields, as a major driver of progress? And why won’t people stop talking about it? A quick overview: in the mid-20th century, Arthur Samuel, who coined the phrase machine learning, decided that computers should be able to learn on their own rather than be taught the intricacies of the human mind (Valchanov). The objective of machine learning, therefore, lies in a computer’s capability to create new algorithms from existing data. In turn, this helps the technology recognize patterns and provide correct predictions given fresh, similar data (Castrounis). With ML intact, our world’s technology is able to reach new heights at an exponential rate.
Generally speaking, machine learning is predicted to improve the functions of everything from healthcare to Yelp. In regards to the medical field, for instance, Providence St. Joseph Health is seeing success in its trials with machine learning through improved surgical outcomes and increased patient arrivals (Siwicki). Interestingly enough, this health system that operates over 50 hospitals attributes their success with machine learning to its vast amount of patient data. As opposed to other fields, health systems have direct access to extensive patient information, which, when coupled with machine learning, can optimize their system and types of care (Siwicki). Additionally when speaking of large amounts of data, one must consider the possibilities that machine learning would have on financial trading. In the world of finance, ML affects a plethora of everyday applications such as fraud detection, portfolio management, and algorithmic trading (Faggella). However that is not all, as machine learning could impact much more of the finance industry as it has the potential to help tighten security and increase financial product sales (Faggella). Machine learning can also directly benefit the average Joe by improving applications that are near and dear to many: social media. By curating your Twitter feed with favorable tweets at the top and brightening up your Pinterest content discovery, ML is hitting closer to home then ever before (Shewan).
What’s truly startling is that these machine learning applications only occupy the tip of the iceberg. Thanks to Arthur Samuel (and many others) technology is looking pretty limitless these days.