Wednesday, November 14, 2018

Two Kinds of Data Gurus

Two Kinds of Data Gurus - Harvard Business Review: “…To hire the right people for the right roles, it’s important to distinguish between different types of data scientist. There are plenty of different distinctions that one can draw, of course, and any attempt to group data scientists into different buckets is by necessity an oversimplification. Nonetheless, I find it helpful to distinguish between the deliverables they create. One type of data scientist creates output for humans to consume, in the form of product and strategy recommendations. They are decision scientists. The other creates output for machines to consume like models, training data, and algorithms. They are modeling scientists….”

I was a private investigator, spying for insurance companies. Here's what I found


"Technology is ruled by two types of people: those who manage what they do not understand, and those who understand what they do not manage."
~Arthur C Clarke

Australia becoming more corrupt, warns former judge

The Democrats exceeded expectations in U.S. midterms

They were not finished counting the ballots in the eastern United States — and the polls were not even closed in the west — when the canard begin to take flight that the Democrats had blown the midterm elections.


Sinema targeted moderate Republican and independent women by painting herself as a nonpartisan problem-solver who voted to support Trump's agenda 60 percent of the time. Her nearly single-issue campaign talked about the importance of health care and protections for people with pre-existing conditions *Democrat Sinema wins Arizona Senate seat running as centrist





Mything the point: The AI renaissance is simply expensive hardware and PR thrown at an old ideaThe promise and problems of including ‘big data’ in official government statistics

Fleur Johns, Caroline Compton, Wayne Wobcke (UNSW)The Australian Bureau of Statistics will soon announce the kinds of information it will collect in the next national census in 2021. If international trends are a guide, “big data” will comprise a growing part of ABS data collection and analysis.


Evidence-based policymaking still a travesty
Nicholas GruenEvidence-based policy ... if everyone claims to want it, and practice it, why do so many projects get glowing evaluations without the slightest tinkering?

Randomistas: how radical researchers changed our world


Andrew LeighRandomised trials are in your life, whether you like it or not. In most advanced countries, governments won’t pay for pharmaceuticals unless they’ve undergone a randomised evaluation. Increasingly, the world’s smartest aid agencies are looking for the same level of evidence before they allocate funds to a project.





Tech C.E.O.s Are in Love With Their Principal DoomsayerNYT. “He worries that because the technological revolution’s work requires so few laborers, Silicon Valley is creating a tiny ruling class and a teeming, furious ‘useless class.'” Jackpot!
Capital Hill Democracy
Be Afraid of Economic ‘Bigness.’ Be Very Afraid. Tim Wu, NYT. The URL (likely autogenerated from the original title): “fascism-economy-monopoly.” Not quite so anodyne….



Specification gaming examples in AI – master list Vraikovna (more here). “A robotic arm trained to slide a block to a target position on a table achieves the goal by moving the table itself.” But aren’t these specs basically how evolution works?