The present invention relates to a sustainable canonical machine intelligence core structure. The sustainable canonical machine intelligence core structure includes a “conscious prior” which is used for representation learning wherein the “conscious prior” is combined with other priors in order to help disentangling abstract factors from each other. The “conscious prior” is further adapted and learn continuously without catastrophic forgetting. The learning is performed instantaneously and rapidly for each incoming sample without latency.