• 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.

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  • Why is the Hard Cap so high? Why do you need so much money for this project?
    The simple reality is that AI research and development is extremely expensive. Costs for a breakthrough or revolutionary technology doesn’t come cheap, and this is certainly true in the AI space today.
    To put things into perspective, McKinsey estimates that in 2016, global tech giants such as Google and Baidu spent $20-30 billion in both R&D and acquisitions in the AI space. It cost IBM roughly $900 million to $1.8 billion to build Watson; and Baidu itself spent a majority of its $3 billion R&D budget on AI in 2016. Large portions of the money allocated to AI by these tech giants went into machine learning projects that require huge amounts of data and computing power.
    However, the good news is that Mind AI doesn’t need expensive supercomputing infrastructure to power its engine. What it needs is a community of the best linguists, ontologists, researchers, logicians, engineers, scientists, and even the regular Joe on the internet to teach, or train, the Mind engine just as a parent would raise a child. Through the accumulation of ontologies, the Mind engine can then go gather information and learn on its own. Therefore, our social impact, juxtaposed to our financial ask, is actually significantly lower than other AI projects out there.
  • In what stage is Mind's current development?
    Currently, we have a working MVP. We will begin live testing of its application use with a few partners starting in Q4 2018, and we plan to go live with the Mind ecosystem in 2019 assuming we secure our necessary funding by reaching the hard cap. Upon its live release, Mind will have required roughly fourteen years of R&D.
    It is important to note that Mind is not a fixed engine with a fixed set of rules and behaviors. It has the capacity to expand its knowledge of the world, and further its own growth and development.  Therefore, when referring to Mind’s development, we can look at it as an ever evolving tool, always learning, improving, updating, and branching out into new domains. In order to scale the growth and further development of Mind, to allow it to reach its fullest capacity, we too must open up. This is why we need the community, and why we are creating the Mind ecosystem,  
    Scientists are well known for having large budgets, coming together from all walks of the planet and joining together to figure things out. The very nature of their approach to solve problems is exactly what must be done for AI, and essentially the planet as a whole. We aim to share the Mind engine and ecosystem so that these AI advances can really benefit society; we aim to collectively improve the well-being of everyone and everything everywhere.
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