Big Picture

• Everything. This 10-minute game trailer from a game that shows the universe from a huge variety of perspectives. The promotional trailer is narrated by philosopher Alan Watts. It’s a refreshing break and the biggest of big pictures. 
• The Myth of a Superhuman AI. Kevin Kelly offers an alternative point of view. Superhuman intelligence is the wrong way to think about it and a long way off.

HR’s View

• What is a Machine Learning Engineer? It’s the h0ttest new job title. This article explains the differences between Machine Learning Engineer and Data Scientist.
• 8 Ways Machine Learning Is Improving Companies’ Work Processes. from HBR. Improving recruiting is on the list. Better career planning is promised.
• Data Science’s Dirty Little Secrets. It’s harder than we thought to define what a data scientist is and does.

Execution

• Machine Learning Can Help HR Overcome Human Failings. Read this closely. Then ask yourself why it’s necessary to suggest that a fully featured human being is somehow less capable than a machine. There’s lots of talk about improvements in the quality of decision making but precious little evidence so far.

Tutorial

• What is machine learning debt? Debt is a way that software engineers describe the fact that things tend to get more complex over time.
• Hidden Technical Debt in Machine Learning Systems. ” developing and deploying ML systems is relatively fast and cheap, but maintaining them over time is difficult and expensive.” Technical but somewhat readable analysis of the total costs of owning a predictive model. Someone on your purchasing team should understand this. Otherwise, you can’t make a coherent decision about implementing ML projects. From Google.

Quote of the Week

“How does machine learning differ from “data science”? Data science is clearly the more inclusive term. But there is something significantly different about the way deep learning works. It was always convenient to think of a data scientist exploring the data: looking at alternate approaches and different models to find one that works. Classics like Tukey’s Exploratory Data Analysis set the tone for what much of data scientists have done: exploring and analyzing masses of data to find value that’s hiding in them.

Deep learning changes this model significantly. Your hands aren’t directly in the data; you know the result you want, but you’re letting the software discover it. You want to build a machine that beats Go champions, or that tags photos correctly, or that translates between languages. In machine learning, these goals aren’t attained through careful exploration; in many cases, there’s too much data to explore in any meaningful sense, and way too many dimensions. (What is the dimensionality of a Go game? Of a language?) The promise of machine learning is that it builds the model itself: it does its own data exploration and tuning.” – What is a Machine Learning Engineer?  Ben Lorica and Mike Loukides

About

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 seems to mean 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 8 to 10 links with some explanation. The goal is to give you a way to surf the rapidly evolving field without drowning in information.