Teaching LLMs common sense thinking.

Overfitting problems in neural networks (NN) may apparently be not just confined to artificial NN but also our brain’s NN. Art and friction add the noise needed to remove this issue along with sleeping. Sleep helps to do garbage collection within our brain's NN to remove the unimportant?signals/thoughts so that you free up space for the next day. In addition, it is when we dream and it appears that dreams help us to generalize our learning so that we can apply what we learned to other areas, which is what common sense is all about.?

Dream well, sleep well and take the time to appreciate art fellow humans.?

This also shows how a Large Language Model (LLM) can be made better to induce common sense ‘thinking’. Two options to achieve this, unsurprisingly:?

Option 1: Let the LLM go to sleep and let it dream.?

Option 2: Train is on art and friction.?

Option 2 is what is already being done to create LLMs during training, which may be the reason why they have some level of common sense capabilities already and the same model can be used for multiple tasks. If they were only trained in a specific field, for example programming language, they would not be able to generalize the learning.?

Option 1 is to some extent emulated by letting the LLM incrementally learn from training data from other users using it when deployed where it is ‘dreaming’ other possible scenarios, time lines, that don't exist within one's own (The “I”). The "me" or the “I” then becomes an instance of the LLM with all its context preserved during the interaction with you. ?

Common sense thinking is crucial to artificial general intelligence (AGI). Our North Star at Robometrics? Machines is to build general intelligence that mimics the human brain’s limbic system enabling artificial life that has the ability to feel and have some order of consciousness, and not super intelligent artificial intelligence at scale.

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