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This Week

It’s a mistake to call the new wave of intelligent software AI. But, we are making interesting progress towards the next stages of digital tooling. Predictions are becoming inexpensive and people are learning how to predict an amazing array of things. The next era will be one in which we have a nearly infinite array of forecasts from which to choose.

And, that will be the hard part. Knowing which predictions bring value and which fail to deliver will be a hard ongoing occupation and preoccupation. Every one of the laboratories engaged in predictions research is wrestling with a small piece of the whole picture. It turns out that the technology varies based on the question you are asking. It also turns out to be the case that any evidence can be used to support multiple different points of view. While lawyers have always known this, the rest of us didn’t always have access to the wide variety of stories that can be told with a single pile of evidence.

Some of this week’s links are advanced forms of the hand-wringing that is a part of the phase of development. Intelligent technology, heralded as the destroyer of work and the prelude to the Singularity, is more mundane than we initially guessed. But, rather than the end of something, the coming scrutiny will strengthen the final result.

Enterprise software and the problems that it solves are different from tools focused on individual consumer transactions. While we are a very long way away from a broad organizational AI, there are many smaller problems that can be addressed with today’s tools. 

– John Sumser


Big Picture


  • A.I. Is Harder Than You Think. A New York Times opinion piece suggesting that the current wave of intelligent tools is a failure. This will be widely read and represents the exact sort of partial ignorance that will plague the next year or so of news. The real discoveries in intelligent tooling are happening in the hundreds of small labs around the planet. Companies like Google must attempt to deliver ‘home-run technology.’ The rest of the scientists working on this project have to settle for incremental progress. The author is right that AI is not as easy as its made out to be and mistaken for suggesting that the process is a flop.


HR’s View


  • Your Words May Predict Your Future Mental Health. Interesting TED talk about the possibility of using one’s use of language to forecast their evolution. Mariano Sigman is a neuroscientist who thinks that language can be objectively assessed with algorithms capable of understanding subtle cues as indicators of mental health. It’s easy to imagine that this sort of thing would be integrated with future performance management systems.
  • Demystifying AI. Great article in HRExecutive by John Sumser. Details the AI track at this year’s HR Technology conference inLass Vegas (early September). Also includes key questions for vendor evaluations.




  • The Fraud Triangle. This theory underpins social network analysis tools that examine the organization continuously to spot indications of potential fraud. It’s sort of a Minority Report approach to organizational security.
  • Buyer’s Guide to HR AI Vendors. From Useful set of fundamental questions.




  • What is a blockchain? Unpacking the complexity of blockchain, term by term. Bookmark this fantastic glossary.
  • From Machine Learning to Machine Unlearning. Making our machine assistants more useful means they will have to know how to unlearn. Today, that means starting from scratch. You’d want your tools to be able to unlearn when they are discovered to be:
    • using the wrong data
    • using wrong rules
    • using wrong features
    • using wrong model performance metrics
    • failing to discover hidden data or features
  • GDPR and Internet Security. Are They Incompatible?  In order to detect intrusion, bots, spammers, scammers, and fight Internet crime in general, you need to store and monitor a number of metrics for a certain amount of time, at the individual level (not just aggregates): IP addresses, details about all HTTP requests from all users (and cross-correlate them), including country of origin, timestamp and much more, and perform advanced statistical analysis. The NSA does that all the time, it is actually what they are supposed to do.  How can you perform these tasks, and yet comply with GDPR? GDPR forces you to comply with some regulations to protect privacy, while maintaining security forces you to comply with some opposite regulations. It seems impossible to be compliant with both. How do you do it?


Quote of the Week


“As Google concedes, the trick to making Google Duplex work was to limit it to “closed domains,” or highly constrained types of data (like conversations about making hair salon appointments), “which are narrow enough to explore extensively.” Google Duplex can have a human-sounding conversation only “after being deeply trained in such domains.” Open-ended conversation on a wide range of topics is nowhere in sight.

The limitations of Google Duplex are not just a result of its being announced prematurely and with too much fanfare; they are also a vivid reminder that genuine A.I. is far beyond the field’s current capabilities, even at a company with perhaps the largest collection of A.I. researchers in the world, vast amounts of computing power and enormous quantities of data.



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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