Defining AI

Machine learning and deep learning are two phrases that are related to AI, and I want to be clear on them before proceeding. Here’s the quickest clip I could find on Youtube to get some clarity:

Video from Acadguild tutorial on Data Science

Artificial intelligence is any code, technique or algorithm that helps a machine mimic, develop and demonstrate human behavior.

Machine learning is the techniques and processes by which machines can learn the ways of humans.

Deep learning is drawing meaningful inferences from large data sets, requiring artificial neural networks.

Deep learning is a subset of machine learning, which is a subset of artificial intelligence. These three terms are related but not interchangeable.

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Intro to AI via cartoons

waitbutwhy.com always delivers with the thoughtful and funny

Kicking off this month with a primer on AI from waitbutwhy.com. This is from 2015, so there have probably been leaps in AI since this was written, but it is helpful as an anchor.

There are three types of AI:

  • Artificial Narrow Intelligence: We’re here now. AI that can reliably deliver better outcomes than a human brain, such as navigating with a map on your phone and getting customized music recommendations.
  • Artificial General Intelligence: This is AI that can reason and solve problems. We’re not here yet.
  • Artificial Superintelligence: This is intelligence that greatly exceeds human intelligence across all spectrums. This could be closer than we think.

This primer on AI goes into a lot of detail on the exponential increase of computing power over time as well.

When I think about AI’s applications for recruiting and HR, I think of many areas that artificial narrow intelligence is just starting to be applied. Recommendations based on past choices can be generated with existing data. And I also see the potential for recreating bias; this comes up over and over again as a concern with AI for recruiting. I’m curious to read more about how that can be addressed.

Welcome to AI February!

Having touched companies’ recruiting processes in some capacity the entirety of my career, I’ve been fascinated with the ways recruiting has evolved in that time. As a snapshot of this change, ten years ago as a new recruiter, I sometimes called an advertising agency to help post retail openings in the local newspaper. Today in recruiting operations, we use Google Analytics and tracking pixels to see performance of online postings, and programmatic advertising can automate some placement decisions.

One of the most exciting advancements in the field is artificial intelligence. Already there are HR tech startups touting their use of AI to enhance and improve the recruiting experience. The tagline is generally something about removing human bias and increasing efficiency – great arguments to move towards technology!

As a data analyst and a former recruiter, I am both excited and skeptical. Can these tools do what they promise? Do they truly apply AI or is this advanced statistics dressed up? And finally – aren’t people a critical component of the hiring practice?

To dive into some of these questions and familiarize myself with the state of AI, I’ve decided to commit my February to learning more about it. Every day throughout February, I will learn something about artificial intelligence and share it here. I’m focused on the following areas:

  • Defining artificial intelligence. What counts as artificial intelligence? What is the difference between AI and machine learning or are they interchangeable?
  • What’s the state of artificial intelligence in recruiting? Who is already doing this and how successful is it?
  • What are the promises and pitfalls of AI?

I realize that these three areas are rich enough that I could probably devote a month (or more!) to each. My intention here is to more broadly explore, though, so I’ll touch on all of these in one short month.

I’m looking forward to this month of learning and hope you follow along! Feel free to share any interesting tidbits, resources or your own interest with me here or via email or Twitter. Here’s to learning!