# Inference (11)

**Why machine learning struggles with causality**- Apr 8, 2021.

If there's one thing people know how to do, and that's guess what caused something else to happen. Usually these guesses are good, especially when making a visual observation of something in the physical world. AI continues to wrestle with such inference of causality, and fundamental challenges must be overcome before we can have "intuitive" machine learning.**Causal Inference: The Free eBook**- Sep 25, 2020.

Here's another free eBook for those looking to up their skills. If you are seeking a resource that exhaustively treats the topic of causal inference, this book has you covered.**Microsoft’s DoWhy is a Cool Framework for Causal Inference**- Aug 28, 2020.

Inspired by Judea Pearl’s do-calculus for causal inference, the open source framework provides a programmatic interface for popular causal inference methods.**4 Free Math Courses to do and Level up your Data Science Skills**- Jun 22, 2020.

Just as there is no Data Science without data, there's no science in data without mathematics. Strengthening your foundational skills in math will level you up as a data scientist that will enable you to perform with greater expertise.**Beyond Neurons: Five Cognitive Functions of the Human Brain that we are Trying to Recreate with Artificial Intelligence**- Sep 3, 2019.

The quest for recreating cognitive capabilities of the brain in deep neural networks remains one of the elusive goals of AI. Let’s explore some human cognitive skills that are serving as inspiration to a new generation of AI techniques.**Easily Deploy Deep Learning Models in Production**- Aug 1, 2019.

Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges.**Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference**- Jul 12, 2019.

Researchers from MIT recently unveiled a new probabilistic programming language named Gen, a language which allow researchers to write models and algorithms from multiple fields where AI techniques are applied without having to deal with equations or manually write high-performance code.**Deep Compression: Optimization Techniques for Inference & Efficiency**- Mar 20, 2019.

We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.**Inference Made Simple – Applying the reasoning power of GRAKN.AI to find new knowledge about the world**- Jul 7, 2017.

This article aims to provide an overview of getting started with GRAKN.AI, and provides a simple example of how to write inference rules using Graql.**Introduction to Bayesian Inference**- Dec 16, 2016.

Bayesian inference is a powerful toolbox for modeling uncertainty, combining researcher understanding of a problem with data, and providing a quantitative measure of how plausible various facts are. This overview from Datascience.com introduces Bayesian probability and inference in an intuitive way, and provides examples in Python to help get you started.**How Bayesian Inference Works**- Nov 15, 2016.

Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.