It’s not monolithic. It has strong advantages as well as important drawbacks. It will have major implications for what products will look like in the future. These are the three things Medha Agarwal says you need to know about machine learning.
As an investor with Redpoint Ventures, a data management and integrated marketing technology company, Agarwal has “the privilege of learning from people and companies on the cutting edge of this technology,” she writes in a blog posted earlier this month at Medium.com.
In the piece, she succinctly lays out some of the “nuances and mechanics” of what machine learning is and how it works. In her view, the landscape of this emerging technology — and its potential for impact on products and services in the future — can be viewed in three ways. She presents them in the form of takeaways:
Takeaway #1: Machine learning is not monolithic. Actually, there are different kinds of learning within machine learning: Supervised, Unsupervised, Semi-supervised, and Reinforcement, Agarwal writes.
Takeaway #2: Deep learning has emerged as a technique with strong advantages, but also has important drawbacks. Deep learning is robust. It’s generalizable. And it’s scalable. These are great advantages.
“At the same time, there are drawbacks to keep in mind when using deep learning,” Agarwal cautions. “One big one is that when a neural net determines certain features are important and makes decisions based on them, we do not know why. This means that if there is corrupted data or human bias in the system, we will not be able to pinpoint that it exists, which can be dangerous for specific use cases, such as various areas of finance and law enforcement, that could impact society.”
Takeaway #3: Machine learning will have major implications for what products will look like going forward. It’s important to remember that it’s “not a solution in and of itself but a tool to optimize the desired outcome,” she says. “Therefore companies leveraging machine learning should focus on providing insights that are actionable, and move from helping customers manipulate data for analysis to focusing on strategy and recommendations to make decision making more efficient and accurate.”
Read her entire post — complete with helpful graphics — here.
Machine learning is an important part of several of the cutting-edge tools and products offered by Capture Higher Ed. Of course, our patent-pending Envision Predictive Engine — the only comprehensive predictive engine in the higher ed market today — utilizes machine learning to accurately predict student applications and enrollment. We also incorporate big data, machine learning and data science to create our proprietary Capture Engagement Score (CES), which helps guide influential communication strategies that are unique to our partner universities.
By Kevin Hyde, Content Writer, Capture Higher Ed