Living in an age that is re-embracing mass shaming
The sweet, but naive, view of history clings to the rainbows-and-puppies hope of moral arcs bending and of people getting better and better and better. Somehow, we tell ourselves, we’ve evolved beyond the innate evil that lies deep within each human heart and history has only one direction: forward. The Salem witch trials were just nightmarish lore of a darker past. Puritanical stocks and taunts were just temporary blemishes on our otherwise bright and shining march into the sunlight of progress.
AI is progressing at a fast and furious pace. Over twenty years ago, IBM’s Deep Blue beat Gary Kasparov at chess. Seven years ago, IBM’s Watson beat Ken Jennings at Jeopardy. Last year, DeepMind’s AlphaGo beat world champ Ke Jie at Go. And just two days ago, moving to the very apex of civilization’s ladder, DeepMind’s AlphaStar walloped two of the best human StarCraft II players.
Despite all this progress, your day-to-day attempts to interact with Siri, Alexa, or Cortana…
One of the challenges I was working through was trying to identify unique person records from a blizzard of API data. It purports to have a unique key for each person, but, in reality, not so much (or, honestly, at all). As a result, I’m working to identify unique people by shared attributes across records. For instance, email can be a reliable unique identifier, but we have many families in our records where dad or mom might share the same email address as the kids. So we’re taking a more holistic approach to examine all the attributes of a particular…
One of the first things people working in data science / machine learning have to grapple with is that our claims are predictive, not inferential. Instead of making careful, logical moves from one clear, proven claim to another, we mix together a set of features, build a model, and then present our results as a reasonably-probable forecast of the future.
Thoughtful people, however, know that “correlation does not equal causation.” Just because two things can be shown to occur in some sort of reliable sequence does not mean that Thing A necessarily caused Thing B. When I leave for work…
Jim Keeler graciously invited me to attend this past Tuesday’s meeting of the Austin Chapter of SIM (the Society for Information Management), which is doing a broader version of the work we used to do six years ago through the CIO/CTO Roundtable of the Austin Technology Council. Kudos to them for doing notably stronger work than we did to reach out to women in tech. It was warm and energizing to re-connect with old friends like Jim, Russ Finney, and Vijay George. It was fascinating to meet new people and learn from them as well. …
Joe Hootman
Diving into oceans of data to discover pearls that help you make wiser decisions. Predictive data analyst, machine learner, data engineer. Disciple in Austin.
