1. One of the best changes in recent years is the GOP abandoning libertarianism. Here's GOP Rep. Greg Steube: “I do think there is an appetite amongst Republicans, if the Dems wanted to try to break up Big Tech, I think there is support for that."

X : Our executive team is concerned that we need to up our game in order to out innovate Amazon.
Me : Do you map?
X : Yes
Me : Like this?
X : No. What's that?
Me : A map of a tea shop.

X : Why is that a map?
Me : Long story, all to do with how space has meaning. To keep it short, maps help people to focus on user needs, the components involved, to communicate missing components and scenario play ideas like staff becoming robots.

Me : They’re also good for measuring and managing capital flow, making investment decisions, removing bias and getting rid of duplication.
X : I don’t see how that helps with innovation?

Me : Well, innovation is a tricky word because we use it to describe many things. If you’re talking about differentiation with the adjacent unexplored then you’re experimenting in the “uncharted” space e.g. immortality with magic provided by the special custom built kettle.

X : That sounds like nonsense.
Me : A lot of what people think will be the next great innovation is nonsense. Actually, most of it is. That’s the nature of the uncharted space, it’s experimental, high risk and generally results in failure.
X : We need more reliable innovation.

Two years ago on a weekend, I built a tool to make it easier to evaluate Twitter accounts. Since then 36590 people used it to analyze 55390 different Twitter accounts.

Over the last months @mmkaradeniz and I made a new version. We launched it last night:

@accountanalysis doesn't use machine learning or AI „magic“. Instead of telling users if an account is authentic or not, it helps them to evaluate the accounts themselves.

It visualizes the different features (date, time, type, app, etc.) of Tweets to make them interpretable. /1

@accountanalysis The core concept is still the same, but it looks much better and is easier to operate. Not only for the users, but us developers as well. Enabling us to continuously roll out new features in the future.

Side-by-side screenshots of the old and new version. /2

@accountanalysis Some people had questions what different charts displayed and how they could be interpreted. There are now explanations for all charts, that can be toggled on and off at any time. /3

@accountanalysis The selected/retrieved Tweet count moved to the top to make it clear that not all Tweets of an account are analyzed. 3200 is the API limit by Twitter. It's possible to get more through the Premium API, but I don't think people would pay $100+ for the analysis of one account. /4

Today we’re going to talk statistics and semantics. I’m going to do so via the medium of one of Twitter’s favourite stats 1.7% (aka “intersex is as common as red heads.”). The statistic comes from Fausto-Sterling’s work, which you can see a review of

To understand the statistic, we have to drill down into what the meaning of the word intersex is. Most people think of hermaphrodites. Let’s stress at the beginning there has been no example ever of a human being with both sets of working reproductive organs.

There is a very tiny percentage of people who are born with both testicular and ovarian tissue (what Fausto-Sterling calls “true hermaphrodites”). And when I say tiny, we’re talking intersex tiny, as in 0.0117/1,000 live births, or one in 1,000,000. These are very complex cases.

So, how do we arrive at 1.7%? What Fausto-Sterling does is apply a wider scope. Instead, she argues that intersex is anything that doesn’t fit into a platonic ideal. The platonic ideal, in this case, being a phenotypically perfect XX female and a phenotypically perfect XY male.

This means that intersex, used in the context of the 1.7% statistic, includes people for whom there is no sexual ambiguity. Take for example Klinefelter syndrome (XXY males). As Fausto-Sterling acknowledges, many males can go their whole life and not realise they are XXY.

Last up in Privacy Tech for #enigma2021, @xchatty speaking about "IMPLEMENTING DIFFERENTIAL PRIVACY FOR THE 2020

Differential privacy was invented in 2006. Seems like a long time but it's not a long time since a fundamental scientific invention. It took longer than that between the invention of public key cryptography and even the first version of SSL.

But even in 2020, we still can't meet user expectations.
* Data users expect consistent data releases
* Some people call synthetic data "fake data" like
"fake news"
* It's not clear what "quality assurance" and "data exploration" means in a DP framework

We just did the 2020 US census
* required to collect it by the constitution
* but required to maintain privacy by law

But that's hard! What if there were 10 people on the block and all the same sex and age? If you posted something like that, then you would know what everyone's sex and age was on the block.