Artificiality Bites 💊 Issue #48

Hello Human! This is a new issue from my weekly newsletter, holding a tiny compilation made of interesting articles from last week, projects, tutorials and tools; all related to Data, Artificial Intelligence and adjacent topics. Dobrou chuť!

📝 Interesting publications this week

• Better computer vision models by combining Transformers and convolutional neural networks 6'
  Facebook AI introduced a new computer vision model called ConViT, which combines CNNs and Transformer-based models, in order to overcome some important limitations of each approach on its own.
• Causal Inference in the Wild: Elasticity Pricing 13'
  How can machine learning improve causal inference in industry? A practical guide.
• We tested AI interview tools. Here’s what we found 7'
  MIT Technology Review tested software from two firms specializing in AI job interviews, MyInterview and Curious Thing.
• Building a data team at a mid-stage startup: a short story 26'
  A made up story about data teams and organization based on at least 3 personal experiences.
• Demystifying the Draft EU Artificial Intelligence Act 26p
  An overview of the Regulation on Artificial Intelligence proposed by the European Commission, and the analysis of its implications.

🔧 Tutorials

• Parameterizing and automating Jupyter notebooks with papermill 10'
  How to use Papermill to automate the execution of your notebooks with any set of parameters.
• What are Diffusion Models? 26'
  It has been shown recently that diffusion models can generate high-quality images and their performance is competitive to state-of-the-art GANs.

📦 Repositories

• tuplex/tuplex
  Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code.
• instadeepai/Mava
  Mava is a research framework for distributed multi-agent reinforcement learning.
• visualpython/visualpython
  Visual Python is a GUI-based Python code generator, developed as an extension for Jupyter Notebook environments.

• ankurchavda/SparkLearning
  A comprehensive Spark guide collated from multiple sources that can be referred to learn more about Spark or as an interview refresher.

🎓 Courses / Events

• Introduction to Deep Learning videos by Sebastian Raschka 📹
  170 video lectures recorded this year, covering from Adaptive Linear Neurons to Zero-shot Classification with Transformers.

🚀 Extra bits

• “Hacker” shows what Tesla Full Self-Driving’s vision depth perception neural net can see 3'
• European Parliament approves mass surveillance of private communications 11'
• A Computer Scientist Who Tackles Inequality Through Algorithms 11

👋 See you next week!