Our Technology
Canonicals
Mind breaks up language into nodes and links that connect at specific points to create a triangular form we call a canonical. Words can be placed in specific positions, as seen by the symbols in the graphic. The placement of these symbols represents one level of semantics and it is fundamental to their logic.
  • Augmented Topological Network
    We call our network "augmented" because a node can interchangeably be a link, a link a node, and a canonical can either be a link or a node. This enables the engine to perform deductive, inductive, and abductive reasoning at many levels. Mind is able to not only identify what data it needs in order to perform its logic, but it is also able to share and reveal all of the logical processes, which are completely human-readable and transparent. In contrast, older and more brittle AI models broke when presented with any novel inputs.
  • Contextualization
    When we read, sometimes we infer meaning of words or a situation based on the context clues within a text. In the same way, information makes sense to Mind only when it is contextualized. For example, the word love can be used in a variety of ways, and it would be difficult for software to determine if the word is being used to describe a platonic, romantic, or any other type of love without context. Mind is able to discover or derive the context from the principles of how the arrangement of the canonical implies meaning.
  • Metatheoretics
    Mind will accumulate domain specific and universal ontologies in natural language and there will be two important turning points in this period. The first is Critical Mass, in which Mind will be able to seek out and accumulate knowledge on its own. The second is Metatheoretics, when Mind will independently conduct experiments, creating its own theories and hypotheses. It is at this time that humanity will be able to utilize the full power of Mind to purse a new level of prosperity.
Ontologies
Ontology is knowledge that people already understand, such as "people die", "birds can fly". The Mind engine can use these ontologies as fuel to infer like a human being.

Ontologies can consist of domain-specific knowledge that can be applied to a specific field or context, or general knowledge that can be used as common sense about the world. The Mind engine utilizes general ontologies for use in reasoning for any domain, as we pursue the goal of creating an artificial general intelligence (AGI).
  • Definition Ontologies
    Definitional ontologies consist of facts to understand about the world or a parts of the world.
    bird wings animal <> }{ fly
    "a bird is an animal with wings to fly"
  • Predicate Ontologies
    Predicate ontologies define a process or function where something changes about the world: why the change happens, and what that change entails.
    A
    Preparation (A)
    "a person is at place1"
    person place1 ?
    <> }{ ><
    B
    Rationale (B)
    "a person needs to move"
    person place1 ?
    <> }{ ><
    C
    Causation (C)
    "a person moves to place2"
    person place1 ?
    <> }{ ><
    D
    Effect (D)
    "a person is at place2"
    person place1 ?
    <> }{ ><
    <> }{ Full Predicate
    "a person moves"
    View Details
    • A
    • B
    • C
    • D
    View Details
    Full predicate
    a person moves
Mind AI becomes smarter as it gets more ontologies, always growing more intelligent.
Mind AI becomes smarter as it gets more ontologies, always growing more intelligent.
How it Works
Read our Technical Whitepaper for information on how this all works:
Technical Whitepaper
Use Cases
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