Systems and methods for graph-based AI training
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.
1 . A virtual assistant system, comprising:
a display;
a processor communicatively coupled to the display; and
a memory, the memory storing an application that configures the processor to:
provide a workspace via the display;
record interactions with the workspace associated with a user;
encode the recorded interactions as nodes into a graph structure, where the graph structure comprises:
a plurality of nodes; and
a plurality of edges connecting nodes in the plurality of nodes;
train a machine learning model by providing the graph structure; and
suggest interactions in the workspace using the trained machine learning model.
2 . The virtual assistant system of claim 1 , wherein a subset of nodes in the plurality of nodes encodes operators available in the workspace.
3 . The virtual assistant system of claim 1 , wherein the application further configures the processor to:
obtain a new interaction with the workspace associated with the user; and
suggest interactions in the workspace by providing the trained machine learning model with the new interaction.
4 . The virtual assistant system of claim 1 , wherein to encode the recorded interactions as nodes, the application further configures the processor to remove schema-specific details from the recorded interactions.
5 . The virtual assistant system of claim 1 , wherein a subset of edges in the plurality of edges connect nodes in the plurality of nodes such that there is a 1-to-1 mapping of user interactions to the graph structure.
6 . The virtual assistant system of claim 1 , wherein a subset of nodes in the plurality of nodes encode user data.
7 . The virtual assistant system of claim 1 , wherein the application further configures the processor to provide a summary of the user data.
8 . The virtual assistant system of claim 1 , wherein the graph structure is a manifold.
9 . The virtual assistant system of claim 8 , wherein to train the machine learning model, the application further configures the processor to traverse the manifold.
10 . The virtual assistant system of claim 9 , wherein the manifold has a convex hull, and wherein to traverse the manifold, the application further configures the processor to traverse nodes on the convex hull.
11 . A method for providing a virtual assistant, comprising:
providing a workspace via the display;
recording interactions with the workspace associated with a user;
encoding the recorded interactions as nodes into a graph structure, where the graph structure comprises:
a plurality of nodes; and
a plurality of edges connecting nodes in the plurality of nodes;
training a machine learning model by providing the graph structure; and
suggesting interactions in the workspace using the trained machine learning model.
12 . The method of claim 11 , wherein a subset of nodes in the plurality of nodes encodes operators available in the workspace.
13 . The method of claim 11 , further comprising:
obtaining a new interaction with the workspace associated with the user; and
suggesting interactions in the workspace by providing the trained machine learning model with the new interaction.
14 . The method of claim 11 , wherein encoding the recorded interactions as nodes comprises removing schema-specific details from the recorded interactions.
15 . The method of claim 11 , wherein a subset of edges in the plurality of edges connect nodes in the plurality of nodes such that there is a 1-to-1 mapping of user interactions to the graph structure.
16 . The method of claim 11 , wherein a subset of nodes in the plurality of nodes encode user data.
17 . The method of claim 11 , further comprising providing a summary of the user data.
18 . The method of claim 11 , wherein the graph structure is a manifold.
19 . The method of claim 18 , wherein to training the machine learning model comprises traversing the manifold.
20 . The method of claim 19 , wherein the manifold has a convex hull, traversing the manifold comprises traversing nodes on the convex hull.