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Aug 19, 2019 • 11 tweets • 7 min read
Poster art of the #HongKongProtests
The typeface is often chosen to mimic the fonts used in the MTR train system, and manga.
Typeface used by a city’s public transport system becomes representative of the city. Here is an experiment by an artist where they swapped the fonts between Hong Kong MTR map and Tokyo Metro:
Calligraphy or Graffiti?
Capitalism with Chinese characteristics
@cathaypacific The designers behind the #HongKongProtests poster art
Training for the Hong Kong Marathon?
@StanChartHelp @HKWORLDCITY Hong Kong explained as GitHub activity

「HK原本是從CN fork出來的，後來被UK push了好多feature，現在要merge回CN，conflict太多了⋯⋯」 🤣
fine arts students @CUHKofficial crafting large banner
Sculptures produced by art students @CUHKofficial
“Hong Kong democracy goddess” statue is the result of a crowded funded project, where HKD200k (~USD25k) was raised within 6 hours.

The details of the design was also decided via public voting, so democratic values is embedded in the art creation process.

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More from @hardmaru

hardmaru Profile picture
Jan 28, 2021
Great debate with @chamath on CNBC about the deeper structural issues behind $GME and $AMC

Love the comments on YouTube:
“lol this CNBC dude is so concerned about my $200 invested…Fuck man…I never realized how much some people cared about me losing it.”
Apparently, CNBC is trying very hard to remove this full interview and copies of it from YouTube. I wonder why... drive.google.com/file/d/16IV7TI…
Someone called in for a few favors from their broker buddies...
Read 6 tweets
hardmaru Profile picture
Dec 14, 2020
I respect Pedro's work, and I also really enjoyed his book. But man, I don't know where I should start with this one…
Maybe can start with “Facial feature discovery for ethnicity recognition” (Published by @WileyInResearch in 2018):
2 years later

“Huawei tested AI software that could recognize Uighur minorities and alert police. The face-scanning system could trigger a ‘Uighur alarm,’ sparking concerns that the software could help fuel China’s crackdown”

The article cited the paper.
washingtonpost.com/technology/202…
Read 8 tweets
hardmaru Profile picture
Apr 17, 2020
Why is it that we can recognize objects from line drawings, even though they don't exist in the real world?

A fascinating paper by Aaron Hertzmann hypothesizes that we perceive line drawings as if they were approximate, realistic renderings of 3D scenes.

arxiv.org/abs/2002.06260
The coolest result in this paper is when they took a depth estimation model (single-image input) trained on natural images (arxiv.org/abs/1907.01341), and showed that the pre-trained model also works on certain types of line drawings, such as drawings of streets and indoor scenes.
This paper seems like an alternative, perhaps complementary take, on @scottmccloud's views about visual abstraction:
Read 5 tweets
hardmaru Profile picture
Mar 19, 2020
Neuroevolution of Self-Interpretable Agents

Agents with a self-attention “bottleneck” not only can solve these tasks from pixel inputs with only 4000 parameters, but they are also better at generalization!

article attentionagent.github.io
pdf arxiv.org/abs/2003.08165

Read on 👇🏼
Work by @yujin_tang @nt_duong me @googlejapan

The agent receives the full input, but we force it to see its world through the lens of a self-attention bottleneck which picks only 10 patches from the input (middle)

The controller's decision is based only on these patches (right)
The agent has better generalization abilities, simply due to its ability to “not see things” that can confuse it.

Trained in the top-left setting only, it can also perform in unseen settings with higher walls, different floor textures, or when confronted with a distracting sign.
Read 9 tweets
hardmaru Profile picture
Feb 12, 2020
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

If we train a neural net to classify CIFAR10 photos but also give it unlabelled QuickDraw doodles, how well can it classify these doodles? arxiv.org/abs/1912.11570
@MILAMontreal @GoogleAI Answer: Better than we thought!

Recent paper by Alex Lamb, @sherjilozair, Vikas Verma + me looks motivated by abstractions learned by humans and machines.

Alex trained SOTA domain transfer methods on labelled CIFAR10 data + unlabelled QuickDraw doodles & reported his findings:
@MILAMontreal @GoogleAI @sherjilozair Alex found SOTA transfer methods (labelled CIFAR10 + unlabelled doodles) achieves ~60% accuracy on doodles. Supervised learning on doodles gets ~90%, leaving ~30% gap for improvement.

Surprisingly, training a model only on CIFAR10 still does quite well on ships, planes & trucks!
Read 4 tweets
hardmaru Profile picture
Dec 18, 2019
“Fudan University, a prestigious Chinese university known for its liberal atmosphere, recently deleted ‘freedom of thought’ from its charter and added paragraphs pledging loyalty to the Chinese Communist Party, further eroding academic freedom in China.”

qz.com/1770693/chinas…
Students and academics at Fudan University appear to have protested this change. But the video of the group chanting lines from the old charter, shared on social media (domestic WeChat), has since been removed:
“The changes provoked a substantial reaction on Weibo. A professor at Fudan U’s foreign languages school, said on Weibo that the amendment is against the university’s regulations, as no discussions occurred at staff meetings. That post was later deleted.”

bloomberg.com/news/articles/…
Read 4 tweets

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