Method and apparatus of increasing knowledge based on uncertainty in neural networks
AssigneeElectronics and Telecommunications Research Institute
InventorsHyun Woo Kim, Jeon Gue Park, Hwa Jeon Song, Yoo Rhee Oh, Byung Hyun Yoo, Eui Sok Chung, Ran Han
Life of this patent
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Abstract
A knowledge increasing method includes calculating uncertainty of knowledge obtained from a neural network using an explicit memory, determining the insufficiency of the knowledge on the basis of the calculated uncertainty, obtaining additional data (learning data) for increasing insufficient knowledge, and training the neural network by using the additional data to autonomously increase knowledge.
Claims15
A method of increasing knowledge based on uncertainty of a neural network in an artificial intelligence (AI) agent, programmed and executed by a computing device to include a knowledge generating neural network for generating knowledge about a user query and a dialogue generating neural network for generating an answer to the user query on the basis of the knowledge, the method comprising:
- converting each of a current user query, previous knowledge generated by the knowledge generating neural network, and a previous answer, generated by the dialogue generating neural network, into an embedding vector value having a specific dimension;
- calculating a plurality of output values corresponding to the embedding vector value by using the knowledge generating neural network with a dropout technique applied thereto and calculating uncertainty of the knowledge generating neural network on the basis of the calculated plurality of output values;
- and
- performing linear conversion on the calculated uncertainty to calculate a compensation value, obtain learning data having the calculated compensation value from a database built through unsupervised learning, and training the knowledge generating neural network and the dialogue generating neural network on the basis of the obtained learning data to increase knowledge.
A knowledge increasing apparatus comprising:
- a storage configured to store an artificial intelligence (AI) agent programmed to include a knowledge generating neural network for generating knowledge about a user query and a dialogue generating neural network for generating an answer to the user query on the basis of the knowledge;
- and
- a processor configured to execute the AI agent stored in the storage,
- wherein the AI agent comprises:
- a preprocessor that converts each of a current user query, previous knowledge generated by the knowledge generating neural network, and a previous answer, generated by the dialogue generating neural network, into an embedding vector value having a specific dimension;
- a calculator that calculates a plurality of output values corresponding to the embedding vector value by using the knowledge generating neural network with a dropout technique applied thereto and to calculate uncertainty of the knowledge generating neural network on the basis of the calculated plurality of output values;
- and
- a sampler that linearly converts on the calculated uncertainty to calculate a compensation value, obtain learning data having the calculated compensation value from a database built through unsupervised learning, and train the knowledge generating neural network and the dialogue generating neural network on the basis of the obtained learning data to increase knowledge.
Dependencies are inferred from the claim text.