Atomic Habits are a series of small practices which if applied in the medium/long term can help us to achieve remarkable results (small changes adds up together creating a growth mindset). One of the main reason why we might be tempted to form bad habits instead of good habits is not in general because of a luck of motivation but because we tend to focus on goals rather then building systems.
In “Good economics for hard times” Abhijit Banerjee and Esther Duflo try to approach some of the most pressing problems of economics providing different possible scenarios and solutions for issues such as immigration, income inequality, climate change, slow economic growth, globalization and technological unemployment.
Nowadays Machine Learning models, makes use of correlations between the different dataset features in order to try to understand how these are related to each other in order to then create a model able to accurately summarise how the data generative process works. This type of approach has been demonstrated to be quite effective in some applications, although not in causality driven systems. In fact, correlations do not necessarily imply causations and this can lead to biased models which make wrong assumptions about how different aspects of a system are connected to each other. Embedding causal reasoning in Machine Learning models would then enable us to create models which can have much greater general use (avoiding overfitting) and which do not rely on training on large amount of data. In The Book of Why we therefore find out about the importance of causality in order to build truly general AI systems.
In this book, Hans Rosling points out how a large number of people has an inaccurate view of our world and some key trends that are now taking action (e.g. politics, immigration, life-expectancy). Therefore, in Factfulness Hans tries to help us to update our perception about things and make it instead rely on actual facts and statistics. In order to help us to build a fact-based view of our world, Hans proposes 10 fundamental mental filters to use before coming to any conclusion.