Machine Learning is the second-best way of doing anything
Written by Madhav Srinath
You already know the best way
You are the actual domain experts who have ingrained this intuitive problem-solving ability into your very fibers through significant effort over a long and sustained period of time. Machine learning without a domain expert is just that — a machine without intuition.
Change is coming and machine learning is your friend
In this competitive era of technology where expertise is just a few Udacity courses away and robots are just around the corner to steal our jobs, it is important to understand our true value. Yes, these robots can help. No, they cannot steal what you have taken years to cultivate and hone. Especially not if you don’t let them.
Change is coming, and it’s not comfortable. However, it can make a positive impact on our life and a significant one at that. Machine learning tools are our partners, not our replacements. But keep in mind, they will shake us out of our comfortable reverie and onto progress.
What does it mean if that manual report that takes 4 hours every week can be automated using an artificially intelligent agent? Does it mean that I’m now worth 10% less because I only need to work 36 hours instead of 40? Absolutely not. It just means that we can reallocate that time — imagine a company who just gained 10% of their time back to focus on anything else. This starts to explain the extreme competitive advantage of machine learning when companies use it the right way. And the right way involves a partnership between their domain experts and these machines.
Machine learning has been around for a while but it needs you
So far, I have referred to machine learning as an actual entity just like you and me. However, this is not true. Machine learning is a process with a deep mathematical foundation and has been around for decades. Although there have been some developments over the years, the core concepts remain. The good news is, we don’t need to know any of that. All we need to know is machine learning cannot produce productive outputs without the input of domain knowledge. And that comes from us.
The math works out
This importance of domain knowledge is deeply rooted within the mathematical underpinnings of machine learning. For example, Bayes Belief Networks is a method which relies on probability of events to predict outcomes. The accuracy of such a method is severely affected by inadequate domain knowledge since we cannot accurately gauge the current state before we can predict the future state. A domain expert however, can work with data scientists to get a better understanding of their world and therefore provide much firmer ground when attempting to predict an outcome. Bayes Belief Networks are still used prevalently in the industry for a variety of use cases not limited to spam detection, medical diagnostic systems, and even Clippy, our old and trusty Microsoft Word friend.
Not everyone needs to be an expert in everything
It all comes down to this — domain experts and data scientists must partner to create a system that works better than the sum of its parts. We don’t all need to become data scientists or take the hottest new course on edX. However, we must understand our value and the fact that machine learning allows us the ability to delegate tasks and push ourselves and our companies to new heights.
Stay tuned.
Madhav is a volunteer of Humans For AI, a non-profit focused on building a more diverse workforce for the future leveraging AI technologies. Learn more about us and join us as we embark on this journey to make a difference!
