This is a quick ramble about Parametric TikTok — a pattern shaped by the recommendation algorithm where creators make viral formats and bombard them w/ variation, not unlike processes seen w/ GANs and style transfer.
“Dance at 10% with 100% emotion.” “0% dance, 0% emotion, 100% far away.” “100% dance and 100% sadness.” “10% emotion, 100% dance.” “100% right arm, 10% left arm.”
What’s interesting is the feedback loop between how parametric the whole thing is and the TikTok algorithm — itself a parametrically weighted system.
The “killer feature” on TikTok is the algorithm — the sauce determining what appears in For You, the primary surface. It’s noticeably better than anything similar, like Instagram Discover.
Apps are mediums of their own. What is appropriate for one platform may feel out of place on another. The context shapes the content.
Parametric TikTok is truly native to the platform.
Parametric TikTok is a symptom of its parent platform similar to early Mr Beast on Youtube. Let’s call it Analytic Youtube. My absolute favorite is the 2017 durational work (lol) “Saying Logan Paul 100,000 Times” in which he says Logan Paul 100,000 times over 17 hours.
Youtube is a platform driven aggressively by metrics. Big numbers. No surprise this performed well. Today the work has 16,492,195 views.
Hear me out; this shit is profound3. He takes the aggregate behavior of 100,000 Youtubers and performs it in one go. Call it “The User is Present” or whatever.
A parametric world
We live in an increasingly parametric world. One easily consumed and shaped by models. It’s funny to think about how these TikTokers are normalizing “parametric design” in a sense.
For instance, within architecture, a site condition is established and permutations in form are generated. The design processes becomes curatorial. Many practices today are centered around these principles of parametricism in response to advances in fabrication; the tools and materials at hand.
We can say this is nothing new or novel. It’s just more evenly distributed now. In other words, fuck your process, I’m just making TikToks.
Generative adversarial networks
In the near future I expect to see way more ML video processing beyond face filters. The emergent behavior of TikTokers feels like a warm up for this super automated future. A convergence between inevitable functionality and what creators are making today.
Snap has shipped filters which map your face on to animations that make you dance… this is not what I’m trying to point out here. It has more to do w/ creative process and how the platforms shape the work created and shared within them.
I wonder how much of Parametric TikTok’s novelty is thanks to interpretation. If we had trained a model on Little Durag’s dance and curated the best 10 out of 100,000 permutations would it hit the same? Even assuming they were indistinguishable from the originals?
By the time it’s possible I assume the novelty will have worn off. Similar to how anyone who has grown up with the artificiality of facetune can see right through it.
I am curious to see the unexpected ways these future applications of ML on platforms will continue to shape what users create and share.
I’m not here to make a point. Just meandering after going down a TikTok hole
And don’t get me started on Pandemic Parametric TikTok.
3 days later
Kyle Chayka, whose writing is consistently a source of useful insight, kindly shared this entry.
a curious aspect of this is how it ultimately leads back to gut decisions. the ability to anticipate the Good Moment during permutations. — @electricgecko
- There are plenty of other instances, including imjoeyreed. Durag stands out for how the comments are literally weighted.↩
- Yea I’m gonna play this card. I was waaaaay ahead of the curve on Metro Station. I was friends w/ someone who was the sister of one of the two guys. A demo of Shake It was her song on Myspace. I remember when they signed to Columbia. Let me cling to my relevancy.↩
- Come at me.↩