The meta-goal

Metagoal is a concept that is the basis for much research in artificial intelligence and machine learning. She suggests that artificial intelligence can be trained both to solve specific problems and to achieve a general goal, which can be called a “meta-goal”.

Metagoal plays an important role in brain science because it reflects the brain's ability for abstract thinking and general learning. It is also the basis for developing more efficient artificial intelligence models. Models that are able to achieve a meta-goal can solve a wider range of problems and become more versatile when used in different fields.

For example, if we train an artificial intelligence model to play chess, then it can learn to play not only chess, but also any other game with a certain set of rules. Thus, the metagoal allows the model to be more flexible and versatile in performing various tasks.

Another important feature of a metagoal is its potential for training independent thinking systems. This can happen through the use of a meta-model, which is a system that can determine a higher-level goal, such as knowing which building is around the corner or how to overcome some problematic section of the road. In this case, the system will be able to determine what task it needs to solve depending on the specific situation and set a goal.