- Mind AI understands the user's intentions based on meaning through reasoning, not mathematical probability-based intent classification. Understanding the sentences’ logical meaning, the entire process of reasoning from the user's input to the classification of logical meaning is transparent.
● Intent Classification without a Subject & Disambiguation
- Mind AI solves the problem with abductive reasoning, semantic distance and user verification. This is because the models are composed of sentences of logical meaning and is not based on the intent model. For example, if the composed problems are “TV problem” and “Internet problem”, the logical forms that can have a “problem” are “TV” and “Internet”. If they want “Internet”, Mind AI will understand what “problem” is specifically being solved by verifying with the user.
● Context Hopping
- As it is mentioned in the adaptive learning below, Mind AI is not a model in which conversations are made only according to the flow by creating a flow based on intent. Switching between contexts is made based on the Context (lower left node) within which our Canonical model already exists.
● Adaptive Learning
- The method of Mind AI is that we understand a written manual in a logical form, creating logical structures between these contents, and processes it through reasoning. This is not the way chatbot developers or designers configure flow, but a how a user problem is naturally, logically configured. Enter the written manual. Mind AI's inference engine converts the manual into Canonical structures to grasp the meaning and outlines the logic through inference.
● Natural Language Reasoning
- Many AI companies, including IBM Watson, do natural language processing, but Mind AI is the first in natural language reasoning. This is a logical reasoning method that accurately understands all contexts in a completely different way from the approaches of existing AI companies. They try to understand by inferring the approximate meaning by applying patterns or rules.
● Augmented Topological Network
- When a network is formed by applying the semantically fragile structure of the existing symbolic AI to the Canonical structure of Mind AI, different words and contexts with the same meaning exist on the network with the same semantics.
● Linear, Qualitative Process
- It is not a method of grasping an approximate context through a tagging technique for a specific word, but a continuous, logical reasoning that relates meaning to meaning according to the Canonical network structure.
- Because the Canonical structure of Mind AI takes a form that humans can understand, the causal relationship between cause and effect can be traced back through the Canonical network structure.
● Don’t Need Tons of Data
- Mind AI accepts education in human language, and constructs an ontology with single data statements, and performs logical reasoning based on that data. Therefore, unlike existing AI, it does not require vast amounts of training.
● Any Language
- Since Mind AI's model grasps meaning based on symbols, it can be applied to all languages used by humans.
- Unlike existing AI models that can only be applied to specific domains and problems, Mind AI's model can be applied to all areas that humans can understand.
● New Symbolic Paradigm
- It combines the strengths of both Symbolic AI and Neural Network AI, and no other company has this structure as the means by which to represent artificial intelligence. We are unique in this space.