In order for OpenAIDE to remain free, it has to offload the major computations required when training your machine learning solution to either your local machine or to your optional cloud account (e.g. AWS).
The browser extension is only needed if you want to execute training runs on your local machine - e.g. you do not have an account with a cloud service like AWS, or you are trying to save money by running locally as much as possible, or you have a custom GPU on your machine that you prefer to use, or your business has its own corporate compute network you want to use.
The OpenAIDE application is downloaded and run on your localhost webserver which you have previously configured.
It is felt that for the goals of this application: ease-of-use, democratization, collaboration, social contributions, etc. the advantages of hosting OpenAIDE in a browser (accessibility, portability, convenience) outweighs the disadvantage (access to standard python frameworks). This disadvantage then, access to python, is replaced by two lessor disadvantages: either having to install a browser extension or setting up your own account in the cloud that hosts your own suite of python libraries.
At this level the brain is a black box. This 'brain' may just be a simple CNN model or something much more complex. As you can see there are similarities between the so-called (thinking and doing) passive and active brains. The design attempts to exploit this to the greatest degree possible, and at the very highest level, the brain just takes in input and generates output and there is vast amount of knowledge about how to work with and implement these kinds of brains.
The Monitor, perhaps misnamed, is included in these diagrams as the god-like combination of teacher, meta-feedback loop, tuning and debug tool that we think may need to be present in order to generalize brains beyond single task designs, and elevate humans from having to explicitly instruct the brain about how it should do things to a higher, more supervisory role.
This brain receives notifications about data, whether training or runtime, and reports the results of its analysis of the data e.g. makes some kind of predictions about it. The runtime brain may differ, and in some deterministic manner is derived from, from the training brain.
This brain inquires about its environment and receives events about changes to the environment, and takes actions based on the results of its analysis of the situation. The environment is separated into two distinct entities as there is at present no clear advantage to combining them and, in fact, there appears to be clear benefits in terms of generalizing the brain by keeping them separate (i.e. to take advantage of the similarities with the passive brain).