To get the most out of this book, do not be afraid to do a little programming yourself. While we will stick to no-code tools in most cases, in certain areas it is simply easier to run a small Python or R script to ensure smooth operations. For example, we will be pulling data from HTTP REST APIs to build AI predictions into our dashboards. Or we will do some basic data processing in Python's pandas or R's Tidyverse. If you are open and willing to learn some of these practices, this book will give you everything you need to create a first version of an AI use case prototype on your own, without any hand-holding, thanks to ready-to-use code templates.

This paper starts by revealing a surprising finding: without any learning, a randomly initialized CNN can localize objects surprisingly well. That is, a CNN has an inductive bias to naturally focus on objects, named as Tobias (``The object is at sight'') in this paper. This empirical inductive bias is further analyzed and successfully applied to self-supervised learning. A CNN is encouraged to learn representations that focus on the foreground object, by transforming every image into various versions with different backgrounds, where the foreground and background separation is guided by Tobias. Experimental results show that the proposed Tobias significantly improves downstream tasks, especially for object detection. This paper also shows that Tobias has consistent improvements on training sets of different sizes, and is more resilient to changes in image augmentations. Our codes will be available at https://github.com/CupidJay/Tobias.

The ride-hailing service wants to ferry the world around in self-driving cars and on electric scooters, deliver our takeouts and groceries by drone, and ship freight via robot trucks. But first Uber needs to answer a big question: will it ever make any money? This week, Wall Street will have the chance to ask that question. Uber went public in May in one of the most anticipated initial public offerings in years. To say it stalled would be an understatement.