Staffing Challenges

The use cases for AI you didn't know existed

Andrew Buhrmann
September 30, 2018
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It's likely that you interact with some form of AI every day. Whether it be a Google search to find out who that one guy in that one movie was, a 45-minute window shop through Netflix's recommendations, or a voice command to turn off the lights so you can finally just settle into another episode of The Office. AI has become commonplace, both at home and at work.

Even if your Friday nights don't follow that AI-fueled pattern, you've probably read about how AI is being used in data security, financial trading, healthcare, fraud detection, or elsewhere. AI has taken hold in these industries, in part, because there is “big data” associated with each and ready-made AI solutions. But, what about the industries or use cases that don’t involve mountains of numerical data? 

First, some quick definitions:

  • AI can be broadly defined as the ability for a computer to perform tasks that are typically associated with intelligent beings.
  • Natural Language Processing (NLP) is a branch of AI that deals with the interaction between computers and humans using natural language (text, words, speech).

NLP is typically associated with things like search results, email filters, or smart assistants. Each requires the computer to make sense of text first and then take some action based on the understanding of that text.

Most NLP-driven experiences out there today, are only concerned with syntax, which refers to the arrangement of words in a sentence such that they make grammatical sense. Some syntax-related techniques include segmentation, parts-of-speech tagging, parsing, and sentence breaking. So, syntax is one area of NLP - the other main area is semantics.

Semantics in NLP refers to understanding the meaning conveyed by text. This is a subfield of AI that has really not taken off yet commercially. Many of the world’s top data scientists are actively trying to build solutions in semantics that have useful business impact. employs some of those folks and we've been able to push the science forward while generating meaningful results for business users.

Enough with the science talk - you came here for use cases. has innovated in this semantic space of NLP, creating AI that is capable of evaluating and understanding text just as a human reviewer would. learns the roles words play in a sentence, paragraph, and document such as a resume or essay. The AI uses this understanding to help a business user make more informed decisions.

Think about the task of reading 100s of applications for a given academic program and making decisions on them. Your workflow might look like this:

  1. Read the applications
  2. Decide who to interview
  3. Interview
  4. Decide who to extend an offer to can make step 1 and 2 a breeze. Say you've been doing this type of work for a while. You are sitting on 1000s of applications that you've made that interview/no interview decision on. This is all we need to train an AI model to make this decision on your behalf. can read and evaluate these applications for you so you can spend your time elsewhere.

This is the use case for AI that you didn't know existed. You can delegate the manual review of text documents to AI that will give you critical decision-making support. The AI generates scores that inform you on how to proceed. In our application example, the scores may have weeded out the majority of the applications that weren't worth reading in the first place. Or maybe the scores help you prioritize which applications are worth looking at first.

No matter what types of decisions you make, if reviewing a lot of textual information is part of your workflow, can help.