Albeit the fact that deep neural networks are extremely good at finding correlations/patterns in massive amounts of data, we’re discovering they’re far from perfect and suffer serious flaws.

While some people have been advocating for the importance of causal reasoning in building AI systems for quite a while (Judea Pearl and Gary Marcus among others), the fact that Yoshua Bengio, a Turing award winner for his contribution to the development of deep learning, is now advocating for similar ideas is a big step in moving beyond the hype.

tl;dr. Deep learning is good at finding patterns in reams of data, but can’t explain how they’re connected. Turing Award winner Yoshua Bengio wants to change that.

tl;dr. PyTorch lies ahead in research while TensorFlow is still dominant in industry. Meanwhile, some new frameworks are emerging, like Jax, that natively supports forward-mode auto-differentiation, useful for quantizing/distilling models.

An overview of the challenges in managing ML Products from a Product Manager working for a startup that builds machine learning software for robotic vision and control.

tl;dr. Machine Learning is a new field that keeps evolving, deploying ML is a highly iterative process driven by experimentation that faces structural challenges beyond purely technical ones, including lack of buy-ins from stakeholders because of explainability and interpretability issues.

A short O'Reilly interview with Jed Dougherty (Dataiku) about the things that are often overlooked when building Machine Learning systems.

A set of guidelines to help you build better data models that are maintainable, useful and performant: how to contextualize given your constraints, design considerations like grain, naming, materialization, permissioning and governance.

A conversation with Gary Marcus in the Artificial Intelligence podcast discussing the limits of deep learning.

Allyson Ettinger, a computational linguist, joins the Data Skeptic podcast to discuss her work in computational linguistics, specifically in exploring some of the ways in which the popular natural language processing approach BERT has limitations.

tl;dr. Although displaying impressive performance, BERT also suffers from surprising weaknesses, from extreme vulnerability to adversarial inputs to exploiting flaws in the benchmark or completely failing to show a generalizable understaning of the meaning of negation within the context of a sensitivity text.

Includes PyTorch Mobile, Quantization, TPU Support: some of its biggest weaknesses until now.

Python 3.8 is out and people at Real Python explored the biggest changes to show you how you can take advantage of Python 3.8.

Assignment operation was a very contentious proposal, that decided Guido Van Rossum to retire as the BDFL for Python. What do you think of these new changes?

A well-documented introduction to the Bootstrap resampling method, followed by the motivation when it was introduced by Bradley Efron (1979). With Python code and maths equations, based on some good resources (with links provided in the article).

A full, 8 hours long 😲 video tutorial from a Google Engineer that teaches data structures to beginners using high-quality animations to represent the data structures visually.

You will learn how to code various data structures together with simple to follow step-by-step instructions. Every data structure presented will be accompanied by some working source code (in Java) to solidify your understanding.

Learn more about the two new APIs that give you direct access to your experiment data, making it possible to analyze your MLflow data via a pandas or Spark DataFrame.