[originally published on ERE Media’s SourceCon site – link is at the end of this post]

Recruiting industry analyst Rob McIntosh believes “AI recruiting” is the future of recruiting. Ah, another future of recruiting article! Another tome in the latest assembly line of predictions that the profession will be elevated to the one where Supreme Bot Beings sit atop the Totem of Talent. Alas, being a human recruiter is no longer considered to be sexy enough for the Futurists.

Actually, automated recruiting isn’t what Rob is about, efficient recruiting is. We’ll get back to this notion in a few paragraphs.

It is very easy to be snookered in by the sweet smell of technology, unicorn valuations, 30 under 30 lists – and anything that comes to the masses via Google. But allow me to offer another perspective to this future of recruiting discussion.

Artificial Stupidity…

If there’s artificial intelligence, then by logic there has to be artificial stupidity – as in taking the Word of the Bot as Gospel. People, this is a very slippery slope that portends to push aside human expertise, experience, and compassion. Sure, the system can learn, the AlphaGo architecture is a unique extension of previous AI architectures (more about this soon), but humans have a funny way of turning logic on its side and playing a hunch that has a deeper, more robust meaning than any Monte Carlo tree search fueled by “…a huge amount of compute power” can make. Again, more about this later.

Just to be clear, I’m not against #BotRecruiting for the repeatable, scalable types of roles that take up so much of a Sourcer’s or Recruiter’s time, with or without pipelines and talent communities. Just the opposite, I’m very bullish on #AIrecruiting, addressing 68% of all hiring that isn’t significantly specialized (this is the plus or minus one standard deviation of all roles) based upon the range of problems to be solved when on the job.

I’m talking about large-scale hiring in, for instance, customer service, early career sales, retail, warehousing, even entry-level software development, where data is plenty, efficiency is a key goal, and humans are susceptible to injecting bias into the hiring process. I’m also bullish about an #AIrecruiting system that cultivates the digital crumbs and creates “probabilities of behavior” that can be used by human recruiters at some point in the process. We’re getting there tech-wise, but have a long way to move beyond black and white stones.

As promised earlier, one thing that Rob missed out on was describing AlphaGo’s architecture (it’s a darn good read), and why its performance is more evolved than previous attempts at building artificial intelligence solutions to games:

“…it combines a state-of-the-art tree search with two deep neural networks, each of which contains many layers with millions of neuron-like connections. One neural network, the “policy network”, predicts the next move, and is used to narrow the search to consider only the moves most likely to lead to a win. The other neural network, the “value network”, is then used to reduce the depth of the search tree — estimating the winner in each position in place of searching all the way to the end of the game.”

See what AlphaGo is doing with the parallel networks? Assessing hunches…feelings…ESP is what many recruiters call it. With experience, our hunches, fueled by many different scenarios and outcomes from the past, produce a higher probability of the likelihood of success, and with some hunches we learn that they don’t produce a desired outcome. Same with AlphaGo, as long as there’s more computing power to drive the parallel nature of the algorithm.

“Of course, all of this requires a huge amount of compute power, so we made extensive use of Google Cloud Platform, which enables researchers working on AI and Machine Learning to access elastic compute, storage and networking capacity on demand. In addition, new open source libraries for numerical computation using data flow graphs, such as TensorFlow, allow researchers to efficiently deploy the computation needed for deep learning algorithms across multiple CPUs or GPUs”

Let me put this another way: Remember the post-mortem analysis of meteorologists who had tried to predict the path of Hurricane Sandy? The picture below details all the models of possible paths based on a tremendous amount of data collected over decades. These models were created using a variety of simulations running on some of the most brutish computers on the planet. Yet we remember what happened, and the cost? Simulations and predictions are just that.

Each “Hurricane Sandy” adds more data and new sets of rules (learning) that enrich the model and change the hunches. Yet we all know that even the best model results in catastrophic damages. Sometimes the recruiting and hiring of the right person truly is a confluence of hunches, to an experienced recruiter, almost leaps of faith. This #AIrecruiting sure isn’t easy.

In 7 Trends for artificial intelligence in 2016: ‘Like 2015 on steroids’, Andrew Moore, Dean of Carnegie Mellon’s School of Computer Science notes:

“One thing I’m seeing among my own faculty is the realization that we, technologists, computer scientists, engineers who are building AI, have to appeal to someone else to create these programs. When coming up with a driverless car, for example, how does the car decide what to do when an animal comes into the road? When you write the code there’s the question: How much is an animal’s life worth next to a human’s life? Is one human life worth the lives of a billion domestic cats? A million? A thousand? I would hate to be the person writing that code.”

Consider the #AIrecruiting software developer, if they are an avowed animal lover, do they play a Death Race 2000 scenario in their head while coding? This is one of many issues with developing systems to replace or augment humans.

Open source, open stack, and APIs too often mask the fact that there are human beings on the other side of the application. Artificial intelligence, machine learning, intelligence systems, and automation so good that they’ll replace human beings, are not by themselves the seeds of success but are foods that when consumed unchecked further the divide between people and technology. The carrot that is held in front of us, that will have more time to do the things we love, isn’t necessarily reality. Just like the addictiveness of drugs, alcohol, and cigarettes, technology draw us in and not let go. Ask me how often I’m hiking on a lonesome trail only to come across people glued to their smartphones.

More data does not mean necessarily translate into better decisions when the human brain is conditioned to trust the technology rather than the brain.

Perfect marriages end in divorce: what’s going to happen when all these perfect Bot selected people begin working with other perfect Bot selected people and one of them farts in a meeting? Or selects “Reply All” with an unfortunate joke? Or votes for Trump/Bernie/Clinton and posts it for the world to see? Or has sex with a co-worker on their desk, then breaks up with them the next week leading to a barrage of social media stupidity? Will the “person” who pushed these Bot hires through be dinged for a bad hire? Or will the #AIrecruiting system be forced to take a timeout?

Back to the use of AI in recruiting: the question to ask is what do we not do especially well right now? Let’s, as a profession, talk now about the rules that govern these tasks and continue to tune them until we reach consensus on best practices. Let’s decide upon where automation in recruiting makes sense for the people who are likely to be impacted by automation.

The journey from predicting the movement of black and white stones to the behavior of people replete with an unending number of human variables is a huge responsibility for our profession. Rather than get all worked up over a technology to replace people, think about how #AIrecruiting can serve to re-focus organizations on how important recruiting should be, to not only the solvency of companies but to the lives of the people we touch. It is the ethical thing for us to do.

Let’s end this tome with the last line of Aldous Huxley’s Brave New World – a fitting be careful what you wish for rejoinder to what happens when you implicitly trust technology:

“Slowly, very slowly, like two unhurried compass needles, the feet turned towards the right; north, north-east, east, south-east, south, south-south-west; then paused, and, after a few seconds, turned as unhurriedly back towards the left. South-south-west, south, south-east, east…”

[read the original article on ERE Media’s SourceCon site]

To recruiters who have never substantively sourced for people (sorry, but sending out InMauls one at a time or in quantities so large that a Bill of Lading is required, is not sourcing) – and to the hiring managers these recruiters support, Sourcing is not a spigot.

I know you asked for Sourcing’s assistance 24 hours ago but this doesn’t mean that we simply turn on a spigot and out comes a waterfall of people who want to interview with you.

Sourcing is the first date, perhaps days or weeks of missed texts and calls, unreturned emails, a second date, a hiking trip, several coffees – all before the extended foreplay begins. And perhaps more – although you never know…

Sourcing is typically called in after the misery of inbound Post & Pray activity produces a 90% or more rejection rate, driving the hiring manager to their boss who then calls up HR to complain. With Sourcing now “engaged”, Sourcing then asks for an intake meeting (the one that really should have been held in the first place) with the recruiter and the hiring manager to procure the information needed to get down and dirty into the lairs where the Golden Children live. You know what info I’m alluding to – forums, newsletters, websites, associations, conferences, ad infinitum. We use these as jumping off points into the nooks and crannies of people’s lives in search of their digital crumbs that when mixed together with a soupçon of badass engagement produces conversations – first dates.

“Another meeting?” exasperates the hiring manager. “Everything you need is in the job description.” Of course that is simply untrue.

[actually sourcing happens in secret even before the sourcing, recruiting, hiring manager hook-up but this for another post]

Sourcing intake makes everyday recruiting intake look like child’s play. Sourcers are like Michelangelo’s Creation of Adam – reaching out to people they do not know so there’s special knowledge that’s required to instigate the outreach.

We’re Sourcers, Purveyors of the Spigot and Waterfall. So we source. And 24 hours go by and no one has been sent to the recruiter! The resultant exclamation from the hiring manager, “What are we paying you people for?” stings like slap to a fresh sunburn. At the end of this chain of command, Sourcing often finds itself at the ugly end of a snide remark uttered by a recruiter. Failures.

Like Hell we are.

Sourcing doesn’t exist to make recruiting look good – nor are Sourcers subservient to Recruiters (see previous paragraph and extrapolate from “snide remark”). I could toss in the word Partners but that implies the presence of a Service Level Agreement that places sourced people and employee referrals in the same category. Frankly, I believe Sourcing serves at the pleasure of the CEO – think of it as Sourcing Team 6 – to identify people and competitive intelligence that enable the organization’s driver to make split-second changes to road map.

Yes, Sourcing should be this special.

Summer Sundays snugly
Sitting, silently, soporifically
Staring sibyllically.

Strolling, searching, sifting
Submerged stories swell
Shadows…

Sunbeams, symphonies,
Spreading skyward
Sweetly, soliciting smiles.

#SourceCon

Boston-area analytics looking to bring on a few more devs to work on the various short and future projects – some of the details are below (any more detail requires a signed NDA or the possibility of death). Roles are senior to team lead – both have architecture elements; openings are because of life changes for folks who have moved on, for people who have been promoted, and for adding to the teams as a result of both new business and for customers requiring substantially more functionality.

The head of software technology is pretty darn easy-going and is a Luke Starwalker wannabe who keeps a light saber handy (but seems to always be in need of new batteries). The company began as a startup, grew, made money, yet retains some startup feel to it (just like adults have the knack for acting like goofy kids when needed). You don’t have to be in the office all the time and if you wear business casual you will likely be met with multiple Golden Retriever head-tilts.

So read on – and LMK what resonates with you (my email is at the bottom). No résumé is needed because we’re just talking…

Details About The Pipeline

• Pipeline processing utilizes Torque (batch server) and Hadoop
• Data needed for processing are stored in MySQL and Vertica databases
• Vertica is used for generating reports used by Data Quality and Data Acquisition; pipeline jobs are orchestrated by a proprietary interface built on top of Luigi; ad-hoc access to pipeline files in HDFS is provided via Hive
• Initial prep of pipeline files, such as cleansing and removal of sensitive data is via Python scripts and C++ compiled programs; the bulk of the rest of the processing is implemented as Python scripts and Map/Reduce jobs in Hadoop using streaming interface

The Company’s Development Approach

• Agile and Scrum with 3 weeks Sprints with emphasis on high priority stories (typically 80+% acceptance of stories)
• Rally project management
• No special support bucket
• Scrum and tickets for DevOps

The Software Development Flow

• GIT for source control; repos are hosted internally
• Gerrit for code reviews and merges (developer pushes changes in their topic branch up for review)
• Jenkins for deployment and unit tests (kicks off master merge to Gerrit)
• Pip install for Python packages, internal PiPy server, everything runs in virtualenv

The Software Development Teams

• Data Technology – Data Delivery (Pipeline)
  1. Distributed processes, batch processing, Map/Reduce jobs, application of Data Science models for inference and weights generation
  2. Cloud Computing with Hadoop, Torque, MySQL, Vertica
  3. Python, bash, C++
• Data Technology – Data Mining tools
  1. Development of the tools for Data Miners, mostly CLI
  2. Data Extraction using Hadoop, Torque, MySQL, Vertica
  3. Stronger emphasis on efficiencies and response times
  4. Data Aggregation and metrics generation, application of the weights
  5. Python, bash, Java
• General Interfaces Team
  1. Interface layers on top of Hadoop, Torque, Vertica, Luigi
  2. Authentication and web services
  3. Application deployment
  4. Python, Java, network/system layers
• Syndicated Products Team
  1. Python Django, Backbone, jQuery and various JavaScript charting libraries
  2. CSS3, HTML5, LESS
• Data Collection Agent Team
  1. Browser extensions for IE, Chrome and Firefox, proxy for Windows
  2. JavaScript, C++, Windows Interfaces, Install, Python for reports generation and download software website
• DevOps
  1. DevOps
     • Production runs of pipeline, backfill, re-runs
     • Pro data publish
     • Hardware troubleshooting
     • Web Servers tuning, Linux in production environment, Windows Servers, IIS, Apache, NFS, SQL, SSH/SCP/SFTP
  2. IT Labs: PC/Laptop support, HelpDesk Change MGMT

Future Tech Initiatives

• Dynamic Normalization Methodology, Mobile
  1. Complete overhaul of Pipeline
  2. Complete overhaul of Data Mining tools
  3. Redesign of Custom Products: Moving to Dynamic Normalization, simplifying User Experience
  4. Segment Management System: Allow Data Miners to generate, maintain and query a set of standard data segments with automated daily data pulls
• Next Generation Data Collection
  1. Local Proxy Windows Service
  2. Keeping up with Browser evolutions

As promised, here’s my email (just click it to ping me): levy.steve@gmail.com

Thanks. ~Steve

Hardly. Grab the popcorn and listen to my recent presentation on RecruitingWebinars.com: “Hiring Tech Talent Is Easy…Right?”

Click here for the almost hour-long webinar; you can also download my slides from this page?

Any questions?