SEO, Googlenerfing and Boris painting busses

I don’t know whether Boris has an SEO expert on his team but this appears to be a successful piece of search engine “optimisation” which is to say nerfing.

nerf (verb) : To weaken, especially of weapons in a computer game. After the company making soft-toy weapons.


Googling Boris bus has, since 2016, produced images of Boris Johnson and the Brexit bus, bearing the lie about the UK sending £350million per week to the EU.

Whereas when Boris talked about painting model buses in an interview on 25th June 2019, the flurry of internet comment it caused led to searches for Boris bus producing results for the interview instead.

I have no idea whether Boris does paint model buses, though I do know he tells lies. Eddie Mair’s interview of Boris is remarkable.

Boris Johnson stands in front of the bus with the lie about £350million


UK European Parliament Elections 2019 – Brexit analysis

It’s hard not to see the UK Euro elections as a Brexit Poll, Round 2. Perhaps some voters didn’t see it that way, but presumably Brexit party voters did (what else did Brexit Party mean?); and presumably the LibDem & Green swingers did too.

These are the England, Wales, Scotland results (Northern Ireland results not yet available) analysed as if it were a Brexit poll and on the assumption that 100% of Conservative voters were Pro-Brexit and that Labour is split 50/50:

Pro-Brexit          7,349,304 44%
Pro-Remain          6,791,094 41%
Split          2,398,097 14%
N/A                88,759 1%
Total        16,627,254 

“Split” covers primarily the Labour Party. Evidently there is a large part of the Labour Party that supports a People’s Vote; the YouGov poll survey (fieldwork between Dec 2018 and Jan 2019) puts the Labour Split as 71%/21%. Although I have treated a vote for Conservative as a vote for Brexit, the YouGov poll suggests that is only 69% true of Conservative voters.

Taking those two factors into account brings us closer to the picture described in the Jan 2019 YouGov survey, which has Brexit support at 40%, and Remain support at 46%.

If there were a referendum today on whether or not the UK should remain a member of the European Union, how would you vote?TotalConLabLib Dem
Remain a member of the EU46%2671%84
Leave the EU39%6921%11
Would not vote6%221
Don’t know7%453
Refused2%010


Most other parties have an explicit stated position:

Party%ageVotesBrexit
Position
The Brexit Party31.6%5,248,533->Brexit
Liberal Democrat20.3%3,367,284Remain
Labour14.1%2,347,255 Split
Green12.1%2,010,909Remain
Conservative9.1%1,511,485->Brexit
Scottish National Party3.60 594,553Remain
Plaid Cymru1.0% 163,928Remain
Change UK3.4% 571,846Remain
UKIP3.3% 549,348->Brexit
The Yorkshire Party0.3% 50,842Split
English Democrats0.2% 39,938->Brexit
UK European Union Party0.2% 33,576Remain
Animal Welfare Party0.2% 25,232Remain
Women’s Equality Party0.1% 23,766Remain
Independent Network0.1% 7,641
Socialist Party of Great Britain0% 3,505
Independents0.5% 77,613

Deep Learning & Unintended Algorithm Bias

This was a 5 minute talk on deep learning for the very excellent @chesterdevs. Like others talking about deep learning, I took visuals and the face-learning example from the landmark 2012 paper, Quoc Le/Google/Andrew Ng paper, “Building High-level Features Using Large Scale Unsupervised Learning.”

Only afterwards did I notice that the subset of images which their system show as “most like a face” from their test set were 90% male and 90% white, as is the prototypical face that the machine outputs.

And so we have a neat demonstration of unintended algorithm bias: their input was 10 million randomly-chosen youtube videos; the output was white and male. I bet they didn’t expect that.

A salutary reminder that—as the hard-working statistician will tell you—“random selection” does not mean “unbiased”.

Conway’s Law & Distributed Working. Some Comments & Experience

The eye-opener in my personal experience of Conway’s law was this:

A company with an IT department on the 1st floor, and a marketing department on the 2nd floor, where the web servers were managed by the marketing department (really), and the back end by the IT department.

I was a developer in the marketing department. I could discuss and change web tier code in minutes. To get a change made to the back end would take me days of negotiation, explanation and release co-ordination.

Guess where I put most of my code?

Inevitably the architecture of the system became Webtier vs Backend. And inevitably, I put code on the webserver which, had we been organised differently, I would have put in a different place.

This is Conway’s law: That the communication structure – the low cost of working within my department vs the much higher cost of working across a department boundary – constrained my arrangement of code, and hence the structure of the system. The team “just downstairs” was just too far.  What was that gap made of? Even that small physical gap raised the cost of communication; but also the gaps & differences in priorities, release schedules, code ownership, and—perhaps most of all—personal acquaintance; I just didn’t know the people, or know who to ask.

Conway’s Law vs Distributed Working

Mark Seemann has recently argued that successful, globally distributed, OSS projects demonstrate that co-location isn’t all it’s claimed to be. Which set me thinking about communication in OSS projects.

In my example above, I had no ownership (for instance, no commit rights) to back end code and I didn’t know, and hence didn’t communicate with, the people who did. The tools of OSS—a shared visible repository, the ability to ‘see’ who is working on what, public visibility of discussion threads, being able to get in touch, to to raise pull requests—all serve to reduce the cost of communication.

In other words, the technology helps to re-create, at a distance, the benefits enjoyed by co-located workers.

When thinking of communication & co-location, I naturally think of talking. But @ploeh‘s comments have prodded me into thinking that code ownership is just as big a deal as talking. It’s just something that we take for granted in a co-located team. I mean, if your co-located team didn’t have access to each other’s code, what would be the point of co-locating?

Another big deal with co-location is “tacit” knowledge, facilitated by, as Alistair Cockburn put it, osmotic communication. When two of my colleagues discuss something, I can overhear it and be aware of what’s going on without having to be explicitly invited. What’s more, I can quickly filter out what isn’t relevant to me, or I can spontaneously join conversations & decisions that do concern me. Without even trying, everyone is involved when they need to be in a way that someone working in a separate room–even one that’s right next door–can’t achieve.

But a distributed project can achieve this too. By forcing most communication through shared public channels—mailing lists, chatrooms, pull request conversations—a distributed team can achieve better osmotic communication than a team which has two adjacent rooms in a building.

The cost, I guess, is that typing & reading is more expensive (in time) than talking & listening. Then again, the time-cost of talking can be quite high too (though not nearly as a high as the cost of failing to communicate).

I still suspect that twenty people in a room can work faster than twenty people across the globe. But the communication pathways of a distributed team can be less constrained than those same people in one building but separated even by a flimsy partition wall.

References