BACKGROUND

Modern deep learning techniques leveraging neural network architectures are applied across a wide range of fields to analyze complex data and output conclusive results. Solution techniques utilized by neural networks (e.g., gradient descent) typically depend on training data to optimize and weight the neural network architecture. However, dependence on static training data alone limits the ability of the architecture to adapt to changing real-world environments, as it is not able to dynamically learn from newly received environmental data. Furthermore, current architecture and techniques are limited to learning from large data sources. These limitations reduce the effectiveness of current deep learning systems in certain implementations wherein real-time, subtle data changes can be of particular importance (e.g., cybersecurity, intrusion detection, misappropriation detection). Therefore, there exists a need for an improved, situationally-aware deep learning technology that is sensitive to small data sources.

BRIEF SUMMARY

The following presents a simplified summary of one or more embodiments of the invention in order to provide a basic understanding of such embodiments. This summary is not an extensive overview of all contemplated embodiments and is intended to neither identify key or critical elements of all embodiments, nor delineate the scope of any or all embodiments. Its sole purpose is to present some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description that is presented later.

Embodiments of the present invention address these and/or other needs by providing an innovative system, method and computer program product for incremental state-based learning utilizing neural networks. An artificial intelligence system for state-based learning using one or more adaptive response states of the artificial intelligence system is provided. The system comprises a controller configured for modifying a neural network engine, the controller comprising at least one memory device with computer-readable program code stored thereon, at least one communication device connected to a network, and at least one processing device, wherein the at least one processing device is configured to execute the computer-readable program code to: monitor a data stream having a data pattern by comparing the data pattern to a trained data pattern; identify a change in the data pattern of the data stream; determine a response state of the neural network learning engine, the state defining one or more neural network parameters for monitoring the data stream with the neural network learning engine; identify a predetermined policy for reconfiguring the neural network learning engine based on the data pattern and the response state; and in response to identifying the change in the data pattern and determining the response state, reconfigure the one or more neural network parameters according to the predetermined policy.

In one embodiment, the response state is a first state, and the at least one processing device is further configured to reconfigure the neural network learning engine from the first state having a first set of neural network parameters to a second state having a second set of neural network parameters based on the predetermined policy.

In another embodiment, the system further comprises a library configured to store a plurality of predetermined response states and configurations of the neural network and the one or more neural network parameters associated with the plurality of predetermined response states and configurations. In yet another embodiment, the library stores one or more response state change conditions for defining predetermined state change pathways of the neural network learning engine.

In yet another embodiment, the at least one processing device is further configured to: determine that reconfiguring the second set of neural network parameters does not result in improved performance of the neural network learning engine; and return the neural network learning engine to the first state or another state that does not reconfigure the one or more neural network parameters.

In yet another embodiment, the one or more neural network parameters comprise a robustness function configured to identify erroneous data received in the data stream based on the response state of the neural network learning engine. In yet another embodiment, the robustness function is further configured to operate in an adversarial mode wherein, the robustness function is further configured to weight predetermined training data higher than the erroneous data in response to identifying the erroneous data. In yet another embodiment, the at least one processing device is further configured to ignore the erroneous data and identify the change in the data pattern based on only the predetermined training data.

In yet another embodiment, the one or more neural network parameters comprise an alertness function configured to determine an impact of new data received from the data stream and a subsequent reaction to the new data based on the state of the neural network learning engine.

In yet another embodiment, the one or more neural network parameters comprise an adaptiveness function configured to modify at least one of a learning rate of the neural network learning engine and a weighting of a data point in the data stream.

In yet another embodiment, reconfiguring the neural network parameters comprises reconfiguring an architectural configuration of the neural network learning engine. In yet another embodiment, reconfiguring the architectural configuration of the neural network learning engine further comprises modifying a number of neurons participating in the neural network learning engine. In yet another embodiment, reconfiguring the architectural configuration of the neural network learning engine further comprises modifying a weight of at least one of a neuron and an edge within the neural network learning engine.

In yet another embodiment, the controller is an online controller and the at least one processing device is further configured to generate the trained data pattern dynamically based on receiving the data stream in real-time.

An artificial intelligence system for state-based learning using one or more adaptive response states of the artificial intelligence system is also provided. The system comprises, a controller configured for modifying a neural network learning engine, the controller comprising at least one memory device with computer-readable program code stored thereon, at least one communication device connected to a network, and at least one processing device, wherein the at least one processing device is configured to execute the computer-readable program code to: monitor a data stream in real-time; based on monitoring the data stream, determine a data pattern; determine a response state of the neural network learning engine based on the data pattern, the response state defining one or more neural network parameters for monitoring the data stream with the neural network learning engine; identify a change in the data pattern of the data stream; and in response to identifying the change in the data pattern and determining the response state, reconfigure the one or more neural network parameters.

In one embodiment, the response state is a first state, and the at least one processing device is further configured to reconfigure the neural network learning engine from the first state having a first set of neural network parameters to a second state having a second set of neural network parameters based on determining the data pattern from monitoring the data stream in real-time. In another embodiment, the at least one processing device is further configured to incrementally reconfigure the one or more neural network parameters from the first set to the second set. In yet another embodiment, incrementally reconfiguring the one or more neural network parameters comprises adjusting a learning rate of the neural network learning engine before reconfiguring an architecture of the neural network learning engine.

In yet another embodiment, the response state is an initially predetermined response state having a first set of neural network parameters, wherein the at least one processing device is configured dynamically reconfigure the first set of neural network parameters to modify the predetermined response state and produce a modified response state.

In yet another embodiment, the at least one processing device is further configured to identify the change in the data pattern based on monitoring a combination of real-time data and predetermined training data received in the data stream.

In yet another embodiment, the controller further comprises a dynamic optimization algorithm, the at least one processing device being further configured to execute the dynamic optimization algorithm to reconfigure the one or more neural network parameters in real-time based on monitoring the data stream.

A computer-implemented method for artificial intelligence state-based learning using one or more adaptive response states is also provided. The computer-implemented method comprises: providing a controller configured for modifying a neural network engine, the controller comprising at least one memory device with computer-readable program code stored thereon, at least one communication device connected to a network, and at least one processing device, wherein the at least one processing device is configured to execute the computer-readable program code to: monitor a data stream having a data pattern by comparing the data pattern to a trained data pattern; identify a change in the data pattern of the data stream; determine a response state of the neural network learning engine, the state defining one or more neural network parameters for monitoring the data stream with the neural network learning engine; identify a predetermined policy for reconfiguring the neural network learning engine based on the data pattern and the response state; and in response to identifying the change in the data pattern and determining the response state, reconfigure the one or more neural network parameters according to the predetermined policy.

The features, functions, and advantages that have been discussed may be achieved independently in various embodiments of the present invention or may be combined with yet other embodiments, further details of which can be seen with reference to the following description and drawings.

