Mind AI - Leading the Third Wave of AI.

                           

 Mind AI is an artificial intelligence engine and an ecosystem that was created to offer an entirely new approach to AI. Rather than building an architecture that requires parallel processing, supercomputers, and large amounts of data, we built its core reasoning engine based on an internationally patented, completely new data structures that we call canonicals. 

 Though Deep Learning has taken the field of AI into a new state, its narrowness limits today's AI from being truly intelligent. Mind will be able to learn autonomously in real-time, generalize, and be able to reason abstractly through natural language. This will be the beginning of the Third Wave of AI, and another step forward in the path to artificial general intelligence. 

                          

The Mind  plan to open-source  engine so that it will be readily accessible to the global development community. Just as the Second Wave of AI required data scientists, the Third Wave of AI requires morals and ethics. This is why we have decided to open-source Mind and allow the global community to partake in its growth. Today, the richest corporations and nations are in an arms race to develop the best AI for their own self interests. This will be detrimental to the masses. Our mission is to return the power of AI to the people and democratize this tool that can help humanity solve the most pressing issues. 

                                                                            Canonicals

Mind breaks down information into canonicals, which we refer to as a unit of reasoning. The very base of this novel data structure consists of nodes and links. These nodes and links can form connections, and these connections form canonicals. Each symbol represents one level of semantics, within which words can be placed in the specific positions in a canonical. The order or placement of information in a canonical is fundamentally important to its semantics. 

                                                         Augmented Topological Network

We consider our network to be “augmented” because a node can be a link, a link can be a node, and a canonical can be a link or a node. Through this augmentation and data structure, Mind is able to perform not only deductive reasoning, but also inductive and abductive reasoning. Unlike brittle AI models that break down when data isn’t input in a certain way, Mind is able to identify what data it needs, then asks for that data. The reasoning process is completely transparent, as we are able to traverse through the network and determine exactly how Mind came up with its solution.

                                                                      Contextualization 

 Any piece of information only makes sense to Mind when it is contextualized. It helps Mind understand not only what, but also why. For example, the word love can be used in various contexts, and it’d be difficult for a software to determine if the word is being used to describe a platonic, romantic, or any other type of love. By being able to arrange canonicals in different ways, Mind is able to contextualize, and discover or derive the context in which the information was input into Mind.

                                                                       Metatheoretics 

 There will be two significant inflection points as Mind accumulates ontologies, both domain specific and universal. The first is what we call Critical Mass. When Mind reaches Critical Mass, it will be able to go out and accumulate knowledge on its own. The second point is the idea of Metatheoretics. This is when Mind will be able to create its own original theories, essentially coming up with its own hypotheses and experiments in any domain. This will allow humanity to utilize the power of Mind to pursue a new level of prosperity.

                                                                                                    

Official source:   https://mind.ai/        

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