Every time you see your cat - or a new cat, they look similar but never the same image is fed into the camera. The brain averages/Mixes these to create a concept node "cat" that looks in between all seen cats - like in text there is only a discrete feature 'cat'. If some or one is common however, you save the episodic memory of a more specific image or even the exact image if you repeat it in your brain.
Same for movies, you may see a series of images of someone walking, but different each time... so you create a concept movie node that is in between all seen walks. For a single image, a cat is made of face, body, tail, legs, and a face is made of eyes etc, so we build a concept node for each feature up the hierarchy, concept nodes made of concept nodes. Even text is continuous, you'll see the same thing in different ways ex. "I was walking my cat", "I walking was dog my", "my dog I was walking". In vision, layer 1 is all shades of pixel brightness ex. 0.0, 0.1 ... 1.0, same for text: vocab, but the way they fit together varies. Look at this below, if a brain sees this image feature "line", it can recognize it up-side-down because all pixels are the same if we ignore order, and if we look at all pairs of 2 pixels we will see they are only 1 pixel over too much, same for 4 pixel groups. They are relatively close. https://ibb.co/hmsXHn3 The more times the brain sees a feature the more stronger the neuron connections become. If we observe: cat ate cat ate cat ate cat slept Then if see "cat _", we predict the more frequent future *more*, because more often it is the future (75% of the time in this example). The brain is all about merging patterns. You use vision + sound to predict what will occur next or what something is. For example the image will look a bit like a cat node, and sound will weight in on it more because both are linked in close time. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T181e21f4aba061f3-M6ae0d808a86c5bc17c1891e8 Delivery options: https://agi.topicbox.com/groups/agi/subscription
