Methods and systems for implementing a unified data format for artificial intelligence systems
Systems and method are provided for artificial-intelligence-based formatting of data into interface-specific representations. A computing device may receive datasets including information structured for presentation through various interfaces. The computing device may train a machine-learning model using feature vectors defined from the datasets. The machine-learning model may be trained to generate interface-specific representations of data. The computing device may then receive a query through a first type of interface and execute the trained machine-learning model using the query and an identification of the first type of interface. The machine-learning model may generate a response to the query that includes a structure tailored for interfaces that correspond to the first type of interface. The computing device may then facilitate a transmission of the response to the query through an interface that corresponds to the first type of interface.
1. A method comprising:
receiving, from one or more sources, a dataset including information structured for presentation through one or more interfaces associated with the one or more sources;
defining, from the dataset, one or more feature vectors usable to train a machine-learning model;
training, using the one or more feature vectors, the machine-learning model to generate output associated with a particular interface type;
receiving from a computing device, a communication including a query through a first communication interface, the first communication interface corresponding to an first interface type;
generating a natural language communication including a response to the query, wherein the natural language communication is represented in a structure tailored for interfaces of the first interface type, and wherein the natural language communication is generated by a machine-learning model using unified data types stored in a database;
facilitating a transmission of the response to the query through the first communication interface;
receiving, from the computing device, an input associated with the response to the query the input indicative of an accuracy metric associated with the response generated by the machine-learning model;
generating a feature vector using the query, the response, and the input; and
modifying the machine-learning model using the feature vector, wherein the machine-learning model, once modified, is configured to generate responses to queries with an increased accuracy metric.
2. The method of claim 1 , wherein the dataset includes audio segments.
3. The method of claim 1 , wherein the first interface type corresponds to a user interface through which the computing device transmits and receives textual communications.
4. The method of claim 1 , wherein the first interface type corresponds to a user interface for communicating over text messaging.
5. The method of claim 1 , wherein the first interface type corresponds to a user interface of a webpage.
6. The method of claim 1 , wherein each feature vector of the one or more feature vectors includes features associated with an interface of a source through which the information is presented.
7. The method of claim 1 , wherein the interface type is a telephony-based interface.
8. A system comprising:
one or more processors; and
a non-transitory machine-readable storage medium storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations including:
receiving, from one or more sources, a dataset including information structured for presentation through one or more interfaces associated with the one or more sources;
defining, from the dataset, one or more feature vectors usable to train a machine-learning model;
training, using the one or more feature vectors, the machine-learning model to generate output associated with a particular interface type;
receiving from a computing device, a communication including a query through a communication interface, the communication interface corresponding to an interface type;
generating a natural language communication including a response to the query, wherein the natural language communication is represented in a structure tailored for interfaces of the interface type, and wherein the natural language communication is generated by a machine-learning model using unified data types stored in a database;
facilitating a transmission of the response to the query through the communication interface;
receiving, from the computing device, an input associated with the response to the query, the input indicative of an accuracy metric associated with the response generated by the machine-learning model;
generating a feature vector using the query, the response, and the input; and
modifying the machine-learning model using the feature vector, wherein the machine-learning model, once modified, is configured to generate responses to queries with an increased accuracy metric.
9. The system of claim 8 , wherein the dataset includes audio segments.
10. The system of claim 8 , wherein the interface type corresponds to a user interface through which the computing device transmits and receives textual communications.
11. The system of claim 8 , wherein the interface type corresponds to a user interface for communicating over text messaging.
12. The system of claim 8 , wherein the interface type corresponds to a user interface of a webpage.
13. The system of claim 8 , wherein each feature vector of the one or more feature vectors includes features associated with an interface of a source through which the information is presented.
14. The system of claim 8 , wherein the interface type is a telephony-based interface.
15. A non-transitory machine-readable storage medium storing instructions that when executed by one or more processors, cause the one or more processors to perform operations including:
receiving, from one or more sources, a dataset including information structured for presentation through one or more interfaces associated with the one or more sources;
defining, from the dataset, one or more feature vectors usable to train a machine-learning model;
training, using the one or more feature vectors, the machine-learning model to generate output associated with a particular interface type;
receiving from a computing device, a communication including a query through a communication interface, the communication interface corresponding to an interface type;
generating a natural language communication including a response to the query, wherein the natural language communication is represented in a structure tailored for interfaces of the interface type, and wherein the natural language communication is generated by a machine-learning model using unified data types stored in a database;
facilitating a transmission of the response to the query through the communication interface;
receiving, from the computing device, an input associated with the response to the query, the input indicative of an accuracy metric associated with the response generated by the machine-learning model;
generating a feature vector using the query, the response, and the input; and
modifying the machine-learning model using the feature vector, wherein the machine-learning model, once modified, is configured to generate responses to queries with an increased accuracy metric.
16. The non-transitory machine-readable storage medium of claim 15 , wherein the dataset includes audio segments.
17. The non-transitory machine-readable storage medium of claim 15 , wherein the interface type corresponds to a user interface through which the computing device transmits and receives textual communications.
18. The non-transitory machine-readable storage medium of claim 15 , wherein the interface type corresponds to a user interface for communicating over text messaging.
19. The non-transitory machine-readable storage medium of claim 15 , wherein each feature vector of the one or more feature vectors includes features associated with an interface of a source through which the information is presented.
20. The non-transitory machine-readable storage medium of claim 15 , wherein the interface type is a telephony-based interface.