Abstract: Graphs are powerful structures made of nodes and edges. Information can be encoded in the nodes and edges themselves, as well as the connections between them. Graphs can be used to create manifolds which in turn can be used to efficiently train more robust AI systems. Systems and methods for graph-based AI training in accordance with embodiments of the invention are illustrated. In one embodiment, a graph interface system including a processor, and a memory configured to store a graph interface application, where the graph interface application directs the processor to obtain a set of training data, where the set of training data describes a plurality of scenarios, encode the set of training data into a first knowledge graph, generate a manifold based on the first knowledge graph, and train an AI model by traversing the manifold.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Graphs are powerful structures made of nodes and edges. Information can be encoded in the nodes and edges themselves, as well as the connections between them. Graphs can be used to create manifolds which in turn can be used to efficiently train more robust AI systems. Systems and methods for graph-based AI training in accordance with embodiments of the invention are illustrated. In one embodiment, a graph interface system including a processor, and a memory configured to store a graph interface application, where the graph interface application directs the processor to obtain a set of training data, where the set of training data describes a plurality of scenarios, encode the set of training data into a first knowledge graph, generate a manifold based on the first knowledge graph, and train an AI model by traversing the manifold.

Abstract: Graphs are powerful structures made of nodes and edges, Information can be encoded in the nodes and edges themselves, as well as the connections between them. Graphs can be used to create manifolds which in turn can be used to efficiently train more robust AI systems. Systems and methods for graph-based AI training in accordance with embodiments of the invention are illustrated. In one embodiment, a graph interface system including a processor, and a memory configured to store a graph interface application, where the graph interface application directs the processor to obtain a set of training data, where the set of training data describes a plurality of scenarios, encode the set of training data into a first knowledge graph, generate a manifold based on the first knowledge graph, and train an AI model by traversing the manifold.

Abstract: Graphs are powerful structures made of nodes and edges. Information can be encoded in the nodes and edges themselves, as well as the connections between them. Graphs can be used to create manifolds which in turn can be used to efficiently train more robust AI systems. Systems and methods for graph-based AI training in accordance with embodiments of the invention are illustrated. In one embodiment, a graph interface system including a processor, and a memory configured to store a graph interface application, where the graph interface application directs the processor to obtain a set of training data, where the set of training data describes a plurality of scenarios, encode the set of training data into a first knowledge graph, generate a manifold based on the first knowledge graph, and train an AI model by traversing the manifold.

Abstract: Systems and methods for training AV models in accordance with embodiments of the invention are illustrated. One embodiment includes an autonomous vehicle (AV), a vehicle, a processor, and a memory, where the memory contains an AV model capable of driving the vehicle without human input, where the AV model is trained on a plurality of edge case scenarios. In a still further additional embodiment, a method for training AV models, including obtaining a data structure storing a plurality of scenarios that an AV can encounter, and distance metrics indicating the distance between each scenario, generating a list of edge case scenarios within the plurality of scenarios, identifying hazard frames within the edge case scenarios, encoding the hazard frames into one or more records interpretable by an AV model, and training the AV model using the one or more records.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Graphs are powerful structures made of nodes and edges. Information can be encoded in the nodes and edges themselves, as well as the connections between them. Graphs can be used to create manifolds which in turn can be used to efficiently train more robust AI systems. Systems and methods for graph-based AI training in accordance with embodiments of the invention are illustrated. In one embodiment, a graph interface system including a processor, and a memory configured to store a graph interface application, where the graph interface application directs the processor to obtain a set of training data, where the set of training data describes a plurality of scenarios, encode the set of training data into a first knowledge graph, generate a manifold based on the first knowledge graph, and train an AI model by traversing the manifold.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device including a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database, wherein the graph database includes a set of nodes and a set of edges, identify a region of interest within a graph described by the graph database, construct a feature space from the region of interest, and extract explanatory variables from the feature space.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.

Abstract: Systems and methods for visualizing and manipulating graph databases in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a graph database manipulation device includes a processor and a memory configured to store a graph database manipulation application, wherein the graph database manipulation application configures the processor to obtain a graph database including a set of nodes and a set of edges, determine a source node within the set of nodes, locate a set of related nodes based on the source node and the set of edges, recursively locate a set of sub-related nodes based on the set of related nodes and the set of edges, generate a representation of the set of related nodes from the perspective of the source node, and recursively update the generated representation of the set of sub-related nodes from the perspective of the source node and the related nodes.