Sup y'all Happy New Year! This is part 2 of a series of reflective posts. Missed the first part? No worries click here.
I've rounded out 2017 by finishing my first course with MIT's Executive Education program studying the Business Implications of Artificial Intelligence. I'm happy to continue my musings on what I've learned and the discussions that took place with my classmates. This 2nd module focused on Natural Language Processing. What is that? What the heck does it do and why does it matter?? Well let's talk...
When you call the customer service number for your bank, cable company (the worst), insurance company, or other service providers these days, chances are good your call will be answered by an integrated voice response (IVR) system instead of a real person. Early IVR systems were limited to understanding keypad entries only (“Press 1 for complaints, press 2 for sales…”). More recent versions can understand human speech, up to a point. The better ones will even ask “In a few words, tell me what you’re calling about".
Be honest: When you encounter this type of IVR system, do you try to play along with it, or do you immediately say “Agent” or “Representative” in hopes of getting a real person to talk to? I do the latter every single time, or pro-tip: use gethuman.com to bypass the system entirely. Thank me later.
Most of us try to skip ahead in the line right? That's understandable because often your problem can’t be described “in a few words,” or doesn’t fit into one of the handful of categories you are presented with. Playing the IVR game often means going around in circles and getting nowhere. Talking to a real person enables you to cut to the chase and, with any luck, get quick resolution and be on your way. Or in my personal experience, get transferred to at least 25 different departments before the issue is solved.
Recent discussions in my coursework at MIT’s Sloan School of Management have revolved around a particular application of machine learning that, sooner or later, may eliminate the option of talking to a real customer service rep, in such a way that you won’t even miss that human touch. This is the up-and-coming technology of natural language processing, and its cousins, natural language generation and, someday, natural language understanding.
So, a quick rundown:
In my current role at AndPlus, we try to take these emerging technologies and bring them to life in a business setting. How can our clients and prospects benefit from these emerging technologies? What is the business value of implementing this new tech? There are so many gimmicks and half-baked frameworks that it's tough to discern what's worth the investment.
At the moment, Alexa and similar products are fun things to have in your home. But the real impact of NLP is going to be felt in business applications. Businesses, by and large, are interested in any technology that can reduce costs, increase productivity, and improve the customer experience (remember the cost leadership bit in Part 1 of this blog?).
Here are some ways that NLP and similar technologies are expected to help with all of these objectives. I'm just spitballing here and would love to hear your ideas!
These examples are only the tip of the iceberg—NLP technology will soon work its way into business-related applications in surprising ways. The future of this technology is a clean slate and I’m eager to see how business in general (and AndPlus clients in particular) would like to apply it.
At the very least, the IVR experience may soon be less exasperating when calling customer service on the phone; you might even enjoy getting answers to your questions or resolving issues without having to talk to a human. Wouldn’t that be nice?
Click here for Part 3 of this series.
Extra reading: Apple's Machine Learning blog is an underrated resource. Peep this post about On-Device Speech Synthesis (the new voice of Siri on iOS 11)