There is more to the architecture, you can read more deeper into my design if you read my big book https://agi.topicbox.com/groups/agi/T5d63eca78f81b2b1/the-big-book-file
My video shows only 1 part of the architecture? Not at all... Didn't watch the full 36 minutes? There's text, too, bro, BRO. I explain like 10 mechanisms and other information. If you follow what I say with my mouse cursor, you can "imagine" the other parts on the static image, like the activation function, frequencies, reward dyes, energy traveling around etc. 1) All neural networks compress themselves to learn the latent salient features, they prune low frequency nodes/ connections, blend nodes, store features only once and increment frequencies (strengthen axon weight), trigger related nodes (translation), etc. Once a network is compressed/ learns a good model of the data fed to it, it can predict/ generate True data from the same distribution by using top k predictions softmaxed. 2) Lossless Data Compression is another thing, which is the best Evaluation method for testing Predictors, a neural Predictor is really good at guessing the next letter/ word and can store a separate file that stores error correction (steering the top k predictions to the correct one) and that compresses a file. Object recognition is used in vision, and text neural networks. The goal is to work with strings of objects; sentences in time. AGI is all about updating where to collect or generate data from, it uses large context in a model to make decisions/ the future state of Earth: https://www.reddit.com/r/agi/comments/gcln3p/thought_experiment_what_does_a_body_do_for_a_brain/ Elaborate? Maybe summarize haha. In my movies you see my network has a lot of things for Prediction; frequencies, translation, robustness to typos etc, activation functions, energy remaining, etc. Prediction is truth. It models the data distributions. Trust is based on context; truth. The 2nd major thing in my network is Reward, deciding what website or what mental thought to look into is an recursively updating process and evolves its own goals to achieve the root goal Survival. This goal finding steers the prediction to a path that it wants to meet so that it know "How" to get what is "Wants". The brain modifies long term memory nodes that exist etc, short term working memory (temporary energy), and permanant energy (reward). It updates all 3, to reach the reward answers. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf06e133ecd7df7c9-M3bcfe590b03160e4142177f9 Delivery options: https://agi.topicbox.com/groups/agi/subscription
