Offline Learning

Some good points made about using particular machine learning methods off-line.    The point that offline could be dangerous.

D4RL: Building Better Benchmarks for Offline Reinforcement Learning
By Justin Fu   By Berkeley

In the last decade, one of the biggest drivers for success in machine learning has arguably been the rise of high-capacity models such as neural networks along with large datasets such as ImageNet to produce accurate models. While we have seen deep neural networks being applied to success in reinforcement learning (RL) in domains such as robotics, poker, board games, and team-based video games, a significant barrier to getting these methods working on real-world problems is the difficulty of large-scale online data collection.

Not only is online data collection time-consuming and expensive, it can also be dangerous in safety-critical domains such as driving or healthcare. For example, it would be unreasonable to allow reinforcement learning agents to explore, make mistakes, and learn while controlling an autonomous vehicle or treating patients in a hospital. This makes learning from pre-collected experience enticing, and we are fortunate in that many of these domains, there already exist large datasets for applications such as self-driving cars, healthcare, or robotics. Therefore, the ability for RL algorithms to learn offline from these datasets (a setting referred to as offline or batch RL) has an enormous potential impact in shaping the way we build machine learning systems for the future.  ... "

Downloading the Wikipedia

Since I am always connected, and have been technical since you had to solder things together,  I was unaware you could do this.   The further question is why would I do this?  With smartphones,  laptops and voice assistants arrayed all around me.    Connected to Wifi most everywhere I go.   The article below makes the case why you may want to, and tells you how to download the whole thing to use offline. 

How to Download Wikipedia
Here's how to download Wikipedia. Seriously. The whole thing ... 
By Ed Oswald in DigitalTrends.... "