Feedback loop learning between artificial intelligence systems

Patent metadata

Field	Value
Publication number	US-2021027136-A1
Application number	US-201916521185-A
Country	US
Kind code	A1
Filing date	Jul 24, 2019
Priority date	Jul 24, 2019
Publication date	Jan 28, 2021
Grant date	—

Abstract

Techniques that facilitate feedback loop learning between artificial intelligence systems are provided. In one example, a system includes a monitoring component and a machine learning component. The monitoring component identifies a data pattern associated with data for an artificial intelligence system. The machine learning component compares the data pattern to historical data patterns for the artificial intelligence system to facilitate modification of at least a component of the artificial intelligence system and/or one or more dependent systems of the artificial intelligence system.

First claim

What is claimed is:

1 . A system, comprising:
a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a monitoring component that identifies a data pattern associated with data for an artificial intelligence system; and
a machine learning component that compares the data pattern to historical data patterns for the artificial intelligence system to facilitate modification of at least a component of the artificial intelligence system.

2 . The system of claim 1 , wherein the monitoring component identifies the data pattern associated with the data for the artificial intelligence system based on a dependency mapping graph for the artificial intelligence system.

3 . The system of claim 1 , wherein the monitoring component monitors one or more application programming interface communications associated with the data for the artificial intelligence system.

4 . The system of claim 1 , wherein the monitoring component monitors one or more events associated with the artificial intelligence system.

5 . The system of claim 1 , wherein the monitoring component monitors accuracy of output data generated by the component of the artificial intelligence system.

6 . The system of claim 1 , wherein the machine learning component infers a new classification for the component of the artificial intelligence system.

7 . The system of claim 1 , wherein the computer executable components further comprise:
a development component that trains the component of the artificial intelligence system based on the data pattern.

8 . The system of claim 7 , wherein the development component updates an artificial intelligence model for the artificial intelligence system based on the data pattern.

9 . The system of claim 7 , wherein the component of the artificial intelligence system is a first component of the artificial intelligence system, and wherein the development component updates a second component of the artificial intelligence system based on the modification of the first component of the artificial intelligence system.

10 . The system of claim 9 , wherein the machine learning component compares the data pattern to the historical data patterns to improve performance of the artificial intelligence system.

11 . A computer-implemented method, comprising:
monitoring, by a system operatively coupled to a processor, an artificial intelligence system to identify a data pattern associated with data for the artificial intelligence system; and comparing, by the system, the data pattern to historical data patterns for the artificial intelligence system.

12 . The computer-implemented method of claim 11 , further comprising:
modifying, by the system, one or more portions of the artificial intelligence system based on the comparing.

13 . The computer-implemented method of claim 11 , further comprising:
modifying, by the system, one or more artificial intelligence components of the artificial intelligence system based on the comparing.

14 . The computer-implemented method of claim 11 , further comprising:
modifying, by the system, one or more models associated with the artificial intelligence system based on the comparing.

15 . The computer-implemented method of claim 11 , further comprising:
modifying, by the system, training data associated with the artificial intelligence system based on the comparing.

16 . The computer-implemented method of claim 11 , wherein the comparing comprises improving performance of the artificial intelligence system.

17 . A computer program product facilitating feedback loop learning between artificial intelligence systems, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
monitor, by the processor, an artificial intelligence system to identify a data pattern associated with data for the artificial intelligence system; compare, by the processor, the data pattern to historical data patterns for the artificial intelligence system; and modify, by the processor, one or more portions of the artificial intelligence system based on the data pattern and the historical data patterns.

18 . The computer program product of claim 17 , wherein the program instructions are further executable by the processor to cause the processor to:
modify, by the processor, one or more artificial intelligence components of the artificial intelligence system based on the data pattern and the historical data patterns.

19 . The computer program product of claim 17 , wherein the program instructions are further executable by the processor to cause the processor to:
modify, by the processor, one or more artificial intelligence models of the artificial intelligence system based on the data pattern and the historical data patterns.

20 . The computer program product of claim 17 , wherein the program instructions are further executable by the processor to cause the processor to:
modify, by the processor, training data for the artificial intelligence system based on the data pattern and the historical data patterns.

Assignees

• IBM

Inventors

• Hwang Jinho
• Shwartz Larisa
• Völzer Hagen
• Nidd Michael Elton
• Castrillon Rodrigo Otavio

Classifications

• G06F18/21

  Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation · CPC title

• G06N20/20 Primary

  Ensemble learning · CPC title

• G06F18/2413

  based on distances to training or reference patterns · CPC title

• G06N3/082 Primary

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title

• G06N3/09

  Supervised learning · CPC title