6 THINGS WE FOUND WORTH SHARING
1. 👁 Miscellaneous – On a rather humorous note, this comic was drawn by XKCD two years ago. Given the paper above, this fits particularly well with the security concerns regarding large language model.
XKCD/2169
2. 💡 Use Cases – In 2019 DeepMind introduced MuZero, which can achieve superhuman performance in tasks such as Chess, Go, and Atari Games, without knowing any underlying rules beforehand. After the recent publication in Nature, DeepMind's principal research scientist David Silver sat down with the BBC to discuss future applications. Now they have tasked MuZero with video compression, aiming to invent new ways to reduce the data footprint of video, which makes up the majority of internet traffic.
3. 💭 Opinion – Yann LeCun, Chief AI Scientist at Facebook, reflects on the unrealistic expectations some people hold about large-scale language models such as GPT-3. In his Facebook post he writes:
"[Trying] to build intelligent machines by scaling up language models is like building a high-altitude airplanes to go to the moon. You might beat altitude records, but going to the moon will require a completely different approach."
4. 🎓 Education – The interactive cheatsheet from Stanford's CS229 Machine Learning course is great for learners and those trying to understand ML terminology.
5. 📖 Papers – Data-efficient image Transformers (DeiT) is a new method to train computer vision models that leverage Transformers. The method requires less data and far less computing resources to produce state-of-the-art image classification models. Read more in the Facebook AI Blog and the paper on arXiv.
Facebook AI Research
6. 👩💻 Code – Animate the faces of other humans, Muppets or Nefertiti with your webcam in real-time. Using the First Order Motion Model, images of others can be matched to your movements. Try it right in your browser with this Colab notebook by Eyal Gruss.
Read more on the First Order Motion Model in this Towards Data Science article.
Vladimir Alexeev, Towards Data Science |
