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This week’s issue seems heavier than usual. In the 80s, the Sunday newspaper was sometimes ten-inches thick with turn of the season news and top of the market job ads.


Every single one of these links is worth your time. The broad themes include a deeper look at privacy, clearer definitions of what is and isn’t AI, the shifting definitions of a company, the importance of being able to reproduce results, and the criticality of clean accurate data.


We are entering a distinct new phase in the evolution of technology and business. Our tools are getting better and the terrain is transforming. Unfortunately, the idea that this could be boiled down to an easy button is an outmoded view. Just as we must socially, our business data requires us to use critical thinking at all times in all levels. It seems slower at first but is more productive in the long haul. This week’s pieces help navigate the depths.

John Sumser will be presenting on Wednesday, May 2, 2018 at the O’Reilly AI Conference in New York City taking place between April 30 – May 2, 2018.


Big Picture
  • Machine Learning’s Amazing Ability to Predict Chaos. Much of the so-called AI that is being applied to organizations is better used to win games with clear rules. What happens inside a company is infinitely more complex. The result is an emerging set of tools that use flimsy models to predict real-world results. It’s an engineering problem that will take decades to really get right. This article talks about machine learning tools that are primitive but headed in the right direction.
  • The Key to Everything. Read this one with your feet up on your desk. Imagine that senior leadership is really about looking into the future for a moment. It’s a review of a book by one of the smartest people in the world written by one of the other smartest people in the world. As you might expect, they make it easy to understand what they are saying. At its core, this is about the difference between probability and specialization. Most contemporary AI focuses on probabilities and constraints. There is an alternative and somewhat complementary point of view.
  • AI: The Revolution Hasn’t Happened Yet. This essay, by the head of the Statistics Department at Berkeley, is a deep look at ‘data provenance’ …. where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation. This level of analysis is missing from most contemporary HR AI executions.
  • Frontier AI: How far are we from artificial “general” intelligence, really? “it is still very difficult to build an AI product for the real world, even if you tackle one specific problem, hire great machine learning engineers and raise millions of dollars of venture capital. Evidently, even “narrow” AI in the wild is nowhere near working just yet in scenarios where it needs to perform accurately 100% of the time, as most tragically evidenced by self-driving related recent deaths.”


HR’s View


  • If your data is bad, machine learning is useless. More HBR. “Data quality is no less troublesome in implementation. Consider an organization seeking productivity gains with its machine learning program. While the data science team that developed the predictive model may have done a solid job cleaning the training data, it can still be compromised by bad data going forward. Again, it takes people — lots of them — to find and correct the errors”
  • Networks and the Nature of the Firm. Tim O’Reilly is one of the most potent thinkers in Silicon Valley. This presentation, about the intersection of networks and the firm, is the beginning of a 21st century rethinking pf the way that ecosystem companies behave. Who is and isn’t an employee is a different story.




Quote of the Week


“There is a different narrative that one can tell about the current era. Consider the following story, which involves humans, computers, data and life-or-death decisions, but where the focus is something other than intelligence-in-silicon fantasies. When my spouse was pregnant 14 years ago, we had an ultrasound. There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. “Those are markers for Down syndrome,” she noted, “and your risk has now gone up to 1 in 20.” She further let us know that we could learn whether the fetus in fact had the genetic modification underlying Down syndrome via an amniocentesis. But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. Being a statistician, I determined to find out where these numbers were coming from. To cut a long story short, I discovered that a statistical analysis had been done a decade previously in the UK, where these white spots, which reflect calcium buildup, were indeed established as a predictor of Down syndrome. But I also noticed that the imaging machine used in our test had a few hundred more pixels per square inch than the machine used in the UK study. I went back to tell the geneticist that I believed that the white spots were likely false positives — that they were literally “white noise.” She said, “Ah, that explains why we started seeing an uptick in Down syndrome diagnoses a few years ago; it’s when the new machine arrived.”



Curate means a variety of things: from the work of vicar entrusted with the care of souls to that of an exhibit designer responsible for clarity and meaning. At the core, it means something about the importance of empathy in organization. HRIntelligencer is an update on the comings and goings in the Human Resource experiment with Artificial Intelligence, Digital Employees, Algorithms, Machine Learning, Big Data and all of that stuff. We present a few critical links with some explanation. The goal is to give you a way to surf the rapidly evolving field without drowning in information. We offer a timeless curation of the intersection of HR and the machines that serve it. We curate the emergence of Machine Led Decision Making in HR.


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