I’ve read a few articles recently talking about IBM’s Watson and how the technology they developed may be the harbinger of unemployment for people in many professions.
Here’s one from Fortune magazine, asking if IBM’s Watson will put your job in jeopardy.
Here’s another suggesting that those who train others in Internet, social media, ATS, and resume database sourcing techniques and strategies will be eventually eliminated by semantic search solutions.
Watson Winning at Jeopardy isn’t Surprising
First, let’s first recognize that it’s an apples to oranges comparison between Jeopardy and sourcing/recruiting.
The ability to quickly research and answer trivia questions (or provide questions for the answers, in the case of Jeopardy) is a far cry from having to boil a hiring need (skills, capabilities, and specific responsibilities in specific industries and environments) down to a series of queries to mine flawed and incomplete human capital data (i.e., resumes and social media profiles) in order to return people who have a high probability of not only being qualified for the position, but also interested in the job (i.e. “recruitable”).
With trivia, all of the facts and information are readily accessible, completed and identifiable on the Internet, or in the case of Watson, saved on a multi-TB hard drive array.
It’s not really shocking that a highly specialized $900,000,000 to $1,800,000,000 (estimated 3 year cost of developing Watson) NLP (Natural Language Processing) computer can sort through 200 million pages of structured and unstructured content, including the full text of Wikipedia, to retrieve information faster than a human relying on memory alone.
Why is anyone surprised that Watson spanked people?
I wasn’t.
However, I can’t pass up the opportunity to point out that Watson did make mistakes – here’s one example in which Watson thought the answer was “Who is Picasso?” when the correct answer was “What is modern art?”
Who knew that the err is human, as well as inhuman?
:-)
The Unique Challenge of Human Capital Data
Unlike finding the answers to trivia questions, when it comes to finding and identifying qualified and talented people based on their resumes and social media profiles and updates, the information is often incomplete, and in many cases, critical bits of identifying data are simply not present.
For example, how do you find someone with Spring MVC experience when many people don’t mention it on their resume, nor on LinkedIn, Twitter, blogs, etc.?
I recently gave the world a tiny glimpse into the Dark Matter of LinkedIn – direct keyword, title, and even concept/relational search methods, used by humans or algorithms, can only retrieve results based on existing text.
Quite simply – if the text isn’t there to be retrieved or analyzed, a semantic search/NLP algorithm can’t do anything with it.
Good sourcers really do “read between the lines” of both the job description and requirements as well as the human capital data they are searching for and analyzing.
There is much more to high-level sourcing than keyword and title search/match.
There have been semantic solutions on the market for quite some time that can do keyword, title and concept matching reasonably well (as well as some that claim to, but don’t). The issue with those solutions that no one seems to (or wants to) realize is that they have limitations – they find some matches, exclude some, and bury others.
The real question is who, how, and why are some matches found and ranked highly, while others are excluded, and others ranked lowly but actually represent the best talent?
What Do I Know?
I have hands-on, practical experience (read: trying to find people to fill real jobs) with many of the “top shelf” semantic search applications out there, specifically designed for human capital data, so when I write or speak on the matter of semantic search, I’m not throwing around empty opinions.
I’ve seen what these solutions can do, and I’ve also directly experienced their limitations, including what they simply can’t do.
Unlike many people who write on the subject of semantic search, I have to personally find people and help others find and recruit talented, qualified candidates with highly specialized skills and experience within 24-48 hours of receiving a client request on a daily basis. If semantic search solutions (including the one I have access to) could speed up that process and help me find more and better candidates faster – trust me, I would use them!
I’ve witnessed a sourcer with 9 months of total experience find better qualified matches (and faster) than a big-name semantic search solution in front of one of the senior technical managers responsible for developing the product. It was eye-opening and even somewhat confusing for them, to say the least.
I’ve also spoken with sourcing/recruiting managers at Fortune 500 companies who have evaluated leading semantic search solutions and they passed on purchasing them because the solutions did not find more and better results faster than their sourcing/recruiting team.
Ultimately, it’s not about humans vs. technology – it’s about results.
The Solution is Part of the Problem
I’ve found the creators of semantic search products don’t seem to like it nor do they seem to really listen when you point out the flaws and limitations of their creations – and I’ve had exchanges with people who hold patents in this space.
I’ve also gotten the sense from talking with semantic search solutions providers that some of these folks believe that sourcers, recruiters and HR professionals don’t (and/or can’t!) really understand semantic search and more complex information retrieval strategies.
To their credit, if their perception (based on experience or otherwise) is that recruiters and HR professionals struggle with Boolean search – the most basic query “language” – why wouldn’t they assume that the average recruiter could not possibly understand and appreciate what’s going on “under the hood” of semantic search solutions?
However, it is folly to apply that stereotype to all sourcing, recruiting and HR professionals – there are plenty of us who actually know more about the specific challenges posed by human capital data and the practical needs and concerns of recruiting organizations than the people who are developing the solutions that we are supposed to intrinsically trust to automatically find the best people available.
I don’t hate – I appreciate semantic search. I simply want these solutions to live up to their hype. Semantic search vendors – listen to your current and potential customers – they just want your product to work better!
I’d like to extend an open invitation to any semantic search/NLP vendor – I will happily evaluate your product and make suggestions for improvements…for free! If you’re very confident in your solution, I’ll also write a review online. If you’d rather not have your product exposed publicly, I can also evaluate products privately. I really do want to accelerate the efficacy of semantic search applications for sourcing and recruiting!
I also want to educate others who may be buying these kinds of solutions so they are more knowledgeable and informed as to the pros and cons, capabilities and limitations of these solutions, and not sold simply on impressive sales pitches, techno-speak and “see how many results?” demonstrations. If you’re a potential customer of semantic search solutions, please be sure to include your best sourcers/recruiters in the evaluation process – if the only people who are evaluating a semantic search solution are HR, management, and procurement professionals who don’t actually search for top talent on a daily basis and won’t be using the proposed solution, you can easily be sold on a product that doesn’t actually work as well as you might think based on the sales presentation.
If you’re looking to buy a new flat screen TV or car, anyone can read reviews online, test drive them and compare them to competing products. I find it interesting (and telling!) that you can’t do the same thing when it comes to recruiting and HR software.
When you buy a house – you get it inspected by a specialized professional before you buy it so you really know what you’re getting beneath the surface. Before you buy a semantic search solution, you should have it evaluated by a person who specializes in human capital information retrieval (who is also ideally a neutral third party!).