The current trend of using noise in digital art is associated with many terms such as glitch, databending, datamoshing, pixel shifting, pixel sorting and others.
We experience digital noise routinely in modern life. The way your Netflix pixelates occasionally when too many in your apartment building is using the internet. The sound of digital noise from the occasional random bit drop from your Spotify stream. This is digital noise, or broadly termed a “glitch”.
Digital glitches occur when the signal is altered in some way that affects how it is rendered for the user. Often a glitch can just make an image unviewable. However, there are several common signal processing and telecommunications ways noise enters the system and some of my favorite glitch art uses the original process to artistically create glitches.
Pixel sorting is a good example for how making glitch art works. Pixels are 3 sets of numbers, from 0 to 255, which represent color values for Red, Green, and Blue LEDs in a digital display.
Organizing those pixels by greatest to largest is one way to “glitch” an image.
Let’s say the first 3 pixels of a .jpg file are such: (34, 125, 179), (212, 55, 0), (138, 20, 36)
A pixel sorting glitch would re-sort them as such (34, 125, 179), (138, 20, 36), (212, 55, 0).
Example below, pixel sorted image on left, original image on right.
This github repository is my go-to for most glitch projets: https://github.com/GlitchTools
It uses the python programming language to mimic the effects of ‘natural’ glitches such as the pixel sort and also some old school methods like a wordpad copy/paste method.
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glitch selfie by Justin Epperly
Pyramid glitch by Justin Epperly @DrEpperly on twitter.
Like you I was truly disgusted with election coverage, results, and resulting coverage of the results, and therefore tried to avoid any discussion of it over the Thanksgiving holiday.
This has given me some mental space to really think about what Trump’s victory means:
1. “American Democracy is advanced-level democracy and demands and educated voting populace”
I heard that on NPR and I think this is the operating truth that helps contextualize how Trump could win.
If you can fool our voters, you can “rig” our election.
It doesn’t require *actual* vote tampering. If our voters are dull-minded and unable to filter their information and make real-world decisions then they can be fooled.
2. Hillary was a bad candidate.
Hillary Clinton is deeply unpopular across all demographics. She’s obviously infinitely better than Trump, but her unpopularity is real and lasting. That we’ve known since the 2008 primary defeat to Obama.
Here’s where it gets confusing: Hillary is unpopular for **both** sexist/mysoginist reasons **and** for rational/logical reasons.
Both are true. There are rational reasons to find Hillary repulsive if you are a progressive.
64% of non-college white women voted for Trump. This is a relevant fact that needs to be processed and acted upon.
3. The Democratic Party failed to capitalize on Obama’s winning coalition. The DNC failed big time.
The example here is the info revealed in the DNC hack showing the DNC clearly was playing dirty tricks to get Hillary the nomination. This is absolutely unacceptable and shows a party that doesn’t understand itself and failed as a result.
4. White people are still racist.
We haven’t progressed as far as we think we have in this country. Or maybe, you could say our reach has extended our grasp like the old saying goes.
When white people become poor, they will lash out at anything and apparently that includes embracing thinly-coded neo-nazi rhetoric!
We have to fight trend this will all our strength, but also understand it comes from pain and suffering and desperation. We have to heal, educate ourselves, and work to overcome this resurgence of neo-nazi ideology…we can’t just fight fire with fire because the whole place will burn.
5. It takes longer to fix something than to break it.
We can recover as a nation from what Trump will do, whatever that is…but it may take a really long time and we need to be prepared for that fact.
Our task now is to work harder, smarter, both within and outside ‘the system’ to make our country the place we dream of it becoming.
It’s the era of “big data” (humans have always used all data at their disposal through history but that’s another blog post) and I think I have found a big problem with one of big data’s biggest reporting hubs: 538.
They are cherrypicking the polls they use.
fivethrityeight.com is a site by Nate Silver and they do a pretty good job of reporting and analyzing news from a “data science” perspective. Again, journalism has always been about ‘data’ but I digress.
538’s “Election Forecast” is a well made site that gives users 3 different ‘views’ of 538 projections based on how they are made: http://projects.fivethirtyeight.com/2016-election-forecast/florida/
Here are the categories:
- Polls Plus – 538’s proprietary research that weights (changes) poll results according to 538’s factors they thought up, like how a state voted historically or how well they rate the poll. More relevant factors get more ‘weight’ meaning.
- Polls Only – Just a poll of polls, no ‘weight’ from 538 data analysts
- Now-cast – Highly chaotic, based on flash polls and other sources of quick polling
How does 538 decide what polls to include in their ‘poll of polls’?
That’s the question of course. Big Data nerds will never admit this but no matter what, *the data analyst* makes subjective decisions on *how* to go about analyzing the data. It’s far from unbiased. Numbers don’t like, but people interpreting the numbers are apt to ‘lie’ or screw up just as any other human.
So why do I think 538 might be cherrypicking polls?
In the pic above, a highly weighted poll by Sienna College showing a +6 for Trump is listed as “new”.
However, browse over to the Sienna College site and see they have been polling for at least since March. Here’s their list of all polls: https://www.siena.edu/news-events/news-archive/category/sri-political
Why start including Sienna College now and not before?
I’m 100% sure they have an answer, and there’s a good chance it actually explains their choice here.
However, this is lesson in understanding polls. They are complex but they can be understood and their flaws are rooted in the same thing all system flaws are: human choice.