Tag Archives: NLP

Talent Sourcing: Man vs. AI/Black Box Semantic Search

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Back in March 2010, I had the distinct honor of delivering the keynote presentation at SourceCon on the topic of resume search and match solutions claiming to use artificial intelligence in comparison with people using their natural intelligence for talent discovery and identification.

Now that nearly 2 years has passed, and given that in that time I’ve had even more hands-on experience with a number of the top AI/semantic search applications available (I won’t be naming names, sorry), I decided it was time to revisit the topic which I am very passionate about.

If you’ve ever been curious about semantic search applications that “do the work for you” when it comes to finding potential candidates, you’re in the right place, because I’ve updated the slide deck and published it to Slideshare. Here’s what you’ll find in the 86 slide presentation:

  • A deep dive into the deceptively simple challenge of sourcing talent via human capital data (resumes, social network profiles, etc.)
  • How resume and LinkedIn profile sourcing and matching solutions claiming to use artificial intelligence, semantic search, and NLP actually work and achieve their claims
  • The pros, cons, and limitations of automated/black box matching solutions
  • An insightful (and funny!) video of Dr. Michio Kaku and his thoughts on the limitations of artificial intelligence
  • Examples of what sourcers and recruiters can do that even the most advanced automated search and match algorithms can’t do
  • The concept of Human Capital Data Information Retrieval and Analysis (HCDIR & A)
  • Boolean and extended Boolean
  • Semantic search
  • Dynamic inference
  • Dark Matter resumes and social network profiles
  • What I believe to be the ideal resume search and matching solution
Enjoy, and let me know your thoughts.

Talent Sourcing and Matching – Artificial Intelligence and Black Box Semantic Search vs. Human Cognition and Sourcing

View more presentations from Glen Cathey

Why So Many People Stink at Searching

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The trouble with search today is that people put too much trust in search engines – online, resume, social, or otherwise.

I can certainly understand and appreciate why people and companies would want to try and create search engines and solutions that “do the work for you,” but unfortunately the “work” being referenced here is thinking.

I read an article by Clive Thompson in Wired magazine the other day titled, “Why Johnny Can’t Search,” and the author opens up with the common assumption that young people tend to be tech-savvy.

Interestingly, although Generation Z is also known as the “Internet Generation” and is comprised of “digital natives,” they apparently aren’t very good at online search.

The article cites a few studies, including one in which a group of college students were asked to use Google to look up the answers to a handful of questions. The researchers found that the students tended to rely on the top results.

Then the researchers changed the order of the results for some of the students in the experiment.  More often than not, they still went with the (falsely) top-ranked pages.

The professor who ran the experiment concluded that “students aren’t assessing information sources on their own merit—they’re putting too much trust in the machine.”

I believe that the vast majority of people put too much trust in the machine – whether it be Google, LinkedIn, Monster, or their ATS.

Trusting top search results certainly isn’t limited to Gen Z – I believe it is a much more widespread issue, which is only exacerbated by “intelligent” search engines and applications using semantic search and NLP that lull searchers into the false sense of security that the search engine “knows” what they’re looking for. Continue reading →