Thanks Mike! I just updated my introduction, it's even more abstract than Brett's :)
http://www.cognitivealgorithm.info/ Intelligence is a general cognitive ability, ultimately the ability to predict. That includes planning, which technically is a self-prediction. Any prediction is interactive projection of known patterns, hence the first step must be pattern discovery (AKA unsupervised learning, but all such negation-first terms are obfuscating). My definitions are not terribly radical, pattern recognition is a core of any IQ test. But there is no conceptually consistent bottom-up implementation, so I had to design the process from the scratch. For excellent popular introductions to cognition-as-prediction perspective see “On Intelligence” <http://www.onintelligence.org/> by Jeff Hawkins and “How to Create a Mind“ <http://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/0670025291/ref=cm_cr_pr_product_top> by Ray Kurzweil. But on a technical level, they and most everyone use neural nets, which work in a very coarse statistical fashion. I think the best way to conceptualize basic NN: multi-layer perceptron <https://towardsdatascience.com/what-the-hell-is-perceptron-626217814f53>, is as fuzzy centroid-based clustering <https://en.wikipedia.org/wiki/Cluster_analysis#Centroid-based_clustering>. Each node weighs the inputs, then sums and thresholds them into output. This normalized sum of inputs is their centroid. Top-layer output is compared to some template, and resulting error is backpropagated to adjust the weights. Which is a soft clustering: weighting is modulated inclusion / exclusion of subsequent inputs into the output. But weighted summation randomly degrades input resolution, thus degrading the whole subsequent comparison and training process. This degradation is exponential with the number of layers, which leads to largely brute-force fitting through some ridiculous number of backprop cycles. An alternative is connectivity-based clustering <https://en.wikipedia.org/wiki/Cluster_analysis#Connectivity-based_clustering_(hierarchical_clustering)>, where the first step is input cross-comparison at original resolution. Most modern methods combine such lateral cross-correlation with vertical training. CNN is edge-detection at the bottom, same as cross-comp within kernels, but with trained weights on compared nodes. Attention heads in transformers <https://www.quantamagazine.org/researchers-glimpse-how-ai-gets-so-good-at-language-processing-20220414/> and edges in Graph NNs are also initiated as lateral correlations, later weighed by vertical training. And similar positional encoding was explored in Hinton's Capsule Networks <https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b>. But this combination is not consistent with scalable generality, where incrementally higher levels are formed recursively. If connectivity clustering is superior to vertical training at any point, then it is superior on all levels, they should only differ in the depth of recursion that generates them. I propose encoding connectivity clusters with a unique set of parameters derived from cross-comp. These derivatives include match as a measure of compression, AKA predictive value: a common fitness function across the system. This encoding is very complex upfront, no way it could have evolved naturally. The complexity is deeply structured and utterly decontextualized, which is probably why no one seems to work on such methods. But they don't need interminable opaque training, my feedback only adjusts hyperparameters. In the next section, I define atomic comparison and resulting patterns, then describe hierarchically recursive algorithm of search for incrementally more complex patterns. The following sections compare my scheme to ANN, BNN, and CapsNet. This is an open project: CogAlg <https://github.com/boris-kz/CogAlg/wiki>, we need help with design and implementation in Python. I have awards for contributions, or monthly payment if there is a track record, see the last part. http://www.cognitivealgorithm.info/ ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T8366cc740ec68376-M9309d27b3ad5dd3fba150706 Delivery options: https://agi.topicbox.com/groups/agi/subscription
