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This is Five-Minute Friday on the Staggering Pace of Progress, Part 2.
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On last week’s Five-Minute Friday, I provided an overview of the evolution of recruitment since the dawn of civilization. This survey was intended to provide perspective on the dramatic technological revolution enveloping us today that is dramatically altering every aspect of our lives. Our minds adapt so quickly that we are seldom conscious of how data and automation are facilitating what any generation before us would have considered magic (or, perhaps, a sliver of the most recent generations might have considered at least science fiction).
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To refresh the broader magical perspective, consider that for thousands of years — from the earliest-known recruiters in ancient Egypt until the emergence of printed newspapers in 16th-century Europe — work as a recruiter was near-entirely unchanged. If you’d lived at any point in those millennia, you might not have observed a single innovation; whatever tools and techniques were available when you croaked might have been exactly the same tools and techniques as when you were born.
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By the 20th century, the pace of technological change had picked up considerably. A recruiter in say the United States who began by placing veterans returning from WWII could over a multi-decade career gradually shift from a focus on in-person canvassing and newspaper-based ads to telephone calls. If they enjoyed an especially lengthy career, they might also witness the emergence of emails, electronic job boards, and digital ads.
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In the 21st century, it is a given that recruiters’ tools, technologies, and ways of working will change dramatically over the course of a single career — likely several times over. Some of the major factors underlying these changes are quantifiable. As key examples consider that in the two decades since 2000, the cost of data storage has fallen by 1000x, enabling the affordable storage of staggeringly vast datasets for training machine learning (ML) algorithms.
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The cost per transistor on a computer chip has fallen by 100x, enabling data scientists to devise increasingly intricate ML models. These algorithms can be fed vast datasets and produce staggeringly nuanced outputs such as a paragraph of text on a given topic that is indistinguishable from one composed by a human.
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And, finally, the cost of transferring data over the Internet has fallen 500x, facilitating global, real-time, and typically free access to the latest, most sophisticated ML approaches.
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In addition to those factors that I just mentioned, there are exponentially more abundant sensors (e.g., cameras, microphones, heart-rate monitors) that are collecting data and the 5G “internet of things” currently rolling out will accelerate that trend further by increasing sensor mobility and interconnectivity. We are also experiencing unprecedented (yet still growing) investment in data-modeling innovations from public and private sources alike.
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The confluence of all of these technological trends has enabled ML subfields like deep learning to usher in the dawn of an artificial intelligence (A.I.) revolution. While the film industry and news media typically portray A.I. as being on the cusp of replicating human intelligence, the reality on the ground is that even the cleverest machines today are extremely narrow specializations, and it may be decades (if ever) before a machine has a learning capacity beyond an infant’s.
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Popular misconceptions aside, we are nevertheless demonstrably in the early stages of an A.I. revolution that is rapidly transforming all aspects of life and work. Returning to our recruitment-industry focus, the transformation is exemplified by the ML algorithms developed by a firm like mine (untapt), which eliminate historical hiring biases while simultaneously augmenting recruiters’ capacity, enabling them to operate at a way, way unimaginable scale for a person, it’s a hitherto unimaginable prior to machine learning models we have today.
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So, to call that example a bit, each month, hundreds of thousands of new job postings from across of large number of markets around the world flow into our database. Our ML models evaluate the fit of any given pre-screened candidate in our database to each of those jobs. Since there are a million of these vetted, high-quality candidates, that corresponds to trillions of evaluations per month, hundreds of thousands of new job postings each month, being compared to each of a million candidates. That corresponds to trillions of evaluations.
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So, this trillion-scale volume is far beyond the grasp of a single person’s mind or even all the recruiters’ minds at a single recruitment company, yet our algorithms have suddenly made that scale accessible. By automatically presenting the best-fitting candidates for the available job openings to a recruiter in an intuitive, click-and-point newsfeed-like interface, the most promising handful of recruitment opportunities from amongst the trillions of possibilities each month, can be considered individually.
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Augmenting the broad intelligence of humans with the scale of very narrowly intelligent machines like this has tangible real-world benefits to society. For example, it can cut down the time it takes to place travelling nurses in regional healthcare networks that are responding to a local covid outbreak.
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Augmenting human capacity like we’re doing with ML algorithms today is only the beginning of the A.I. revolution. In a forthcoming Five-Minute Friday episode, we’ll take a peek at how the recruitment industry and broader society may be impacted by an exponential acceleration in A.I. capability over the coming decades.
