Lukáš Lánský @lukas@social.lansky.name

RT @tgraf__@twitter.com

4 years ago we started the @ciliumproject@twitter.com. Today, Google announced the availability of Cilium as the new GKE networking dataplane.

What a great honor for everyone who has contributed to the Cilium project and to eBPF overall.

The background story:
https://cilium.io/blog/2020/08/19/google-chooses-cilium-for-gke-networking

🐦🔗: https://twitter.com/tgraf__/status/1296136348773089280

RT @drusepth@twitter.com

This is the best zero-shot prompt style I've found for generating short poetry snippets with GPT-3 (at default 0.7 temperature and 0/0 penalty rates).

You can also tweak the poem tone by adjusting the student's adjectives (clever, depressed, etc) and name (for mimicking style).

🐦🔗: https://twitter.com/drusepth/status/1293937354978271234

RT @EU_Eurostat@twitter.com

Euro area #RetailTrade +5.7% in June over May, +1.3% over June 2019 https://ec.europa.eu/eurostat/en/web/products-press-releases/-/4-05082020-AP

🐦🔗: https://twitter.com/EU_Eurostat/status/1290935579358760960

RT @ares_emu@twitter.com

"Imagine all the extraordinary things we'll be able to do once computers have literally hundreds of CPU cores!"
Developers:

🐦🔗: https://twitter.com/ares_emu/status/1284328853171605508

RT @components_ai@twitter.com

Give GPT-3 a color scale and an emoji. Get back new scales based on color of the emoji. HOW DOES IT KNOW.

violet: [
'#2d1832',
'#502b5a',
'#753f83',
#8e4c9e',
'#9f5bb0',
'#b683c3',
#c9a2d2',
'#dbc1e1',
'#ebddee',
'#f7f1f8'
],
🍑: [

🐦🔗: https://twitter.com/components_ai/status/1282379087412174848

RT @quocleix@twitter.com

A surprising result: We found that smooth activation functions are better than ReLU for adversarial training and can lead to substantial improvements in adversarial robustness.
http://arxiv.org/abs/2006.14536

🐦🔗: https://twitter.com/quocleix/status/1277668867066621952

RT @citnaj@twitter.com

So yeah I'm really happy about this new DeOldify model actually......

🐦🔗: https://twitter.com/citnaj/status/1275688161369300993

RT @JanelleCShane@twitter.com

When an AI is trained on words, weird things can happen to the physical domain.

I asked the @OpenAI@twitter.com API about horses.
https://aiweirdness.com/post/621186154843324416/all-your-questions-answered

🐦🔗: https://twitter.com/JanelleCShane/status/1273296527662841856

RT @grhmc@twitter.com

C programmers are like

🐦🔗: https://twitter.com/grhmc/status/1273062119362252802

RT @taaiconaut@twitter.com

🐦🔗: https://twitter.com/taaiconaut/status/1271126894872637443

RT @ak92501@twitter.com

Language Models are Few-Shot Learners
pdf: https://arxiv.org/pdf/2005.14165.pdf
abs: https://arxiv.org/abs/2005.14165
github: https://github.com/openai/gpt-3

🐦🔗: https://twitter.com/ak92501/status/1266175845170843654

RT @richcampbell@twitter.com

🐦🔗: https://twitter.com/richcampbell/status/1258774093521879040

RT @OpenAI@twitter.com

Since 2012, the amount of compute for training to AlexNet-level performance on ImageNet has been decreasing exponentially — halving every 16 months, in total a 44x improvement.

By contrast, Moore's Law would only have yielded an 11x cost improvement: https://openai.com/blog/ai-and-efficiency/

🐦🔗: https://twitter.com/OpenAI/status/1257701921969901569

https://ourworldindata.org/happiness-and-life-satisfaction