ABSTRACT
Abstract
An embodiment of the present disclosure provides an artificial intelligence apparatus for generating training data including a memory configured to store an artificial intelligence model, an input interface including a microphone or a camera, and a processor configured to receive, via the input interface, input data, generate an inference result corresponding to the input data by using the artificial intelligence model, receive feedback corresponding to the inference result, determine suitability of the input data and the feedback for updating the artificial intelligence model, and generate training data based on the input data and the feedback if the input data and the feedback are determined as data suitable for updating of the artificial intelligence model.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. patent application Ser. No. 16/730,669, filed on Dec. 30, 2019, which claims the benefit of earlier filing date and right of priority to Korean Patent Application No. 10-2019-0162598, filed on Dec. 9, 2019, the contents of which are all hereby incorporated by reference herein their entirety.
BACKGROUND
The present disclosure relates to an artificial intelligence apparatus for generating training data for an artificial intelligence model by selecting data suitable for training an artificial intelligence model among collected data and a method thereof.
Recently, many artificial intelligence evolution techniques have been adopted to continuously update the artificial intelligence model to reflect usage logs of users. When the artificial intelligence model is updated based on the usage logs of the real users, since training data suitable for the usage aspects of the real users can be obtained, there is an advantage that a more realistic and useful artificial intelligence model can be configured.
Artificial intelligence evolution technology based on the user's usage log basically requires the assumption that the user provides normal feedback through normal use. If a user accidentally or intentionally provides wrong feedback, the artificial intelligence model may be updated based on a wrong usage log, which may cause the artificial intelligence model to be incorrectly learned.
Therefore, there is a need for a method for selectively generating training data by determining whether the collected usage logs are suitable for updating the artificial intelligence model.
SUMMARY
The present disclosure provides an artificial intelligence apparatus which determines whether input data and feedback corresponding thereto are data suitable for updating an artificial intelligence model and generates training data based on data suitable for the artificial intelligence model, and a method thereof.
An embodiment of the present disclosure provides an artificial intelligence apparatus which generates an inference result for input data using an artificial intelligence model, receives feedback corresponding to the inference result, determines whether the input data and the feedback are suitable for updating the artificial intelligence model, and generates input data and feedback determined to be suitable for updating an artificial intelligence model as training data, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which determines suitability of the input data and the feedback by using a plurality of suitability determination methods and determines whether the input data and the feedback are suitable for updating the artificial intelligence model based on the suitability ratio for the plurality of suitability determination results, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which determines whether input data and feedback are suitable for updating an artificial intelligence model by determining whether input data is an outlier as a first suitability determination method, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which calculates a sensitivity of an artificial intelligence model for input data as a second suitability determination method and determines whether input data and feedback are suitable for updating the artificial intelligence model in consideration of the calculated sensitivity, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which generates a plurality of temporary inference results for input data using an artificial intelligence model as a third suitability determination method and compares ensemble inference results generated from the plurality of temporary inference results and feedback with each other to determine whether input data and feedback are suitable for updating an artificial intelligence model, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which calculates a kernel score in a class corresponding to feedback through kernel analysis as a fourth suitability determination method and determines whether input data and feedback are suitable for updating the artificial intelligence model in consideration of the calculated kernel score, and a method thereof.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram illustrating an AI apparatus according to an embodiment of the present disclosure.
FIG. 2 is a block diagram illustrating an AI server according to an embodiment of the present disclosure.
FIG. 3 is a view illustrating an AI system according to an embodiment of the present disclosure.
FIG. 4 is a block diagram illustrating an AI apparatus according to an embodiment of the present disclosure.
FIG. 5 is a view illustrating an artificial intelligence system for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 6 is a view illustrating an artificial intelligence system for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 7 is a flowchart illustrating a method for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 8 is a flowchart illustrating an example of a step S 707 of determining suitability of input data and feedback for updating the artificial intelligence model illustrated in FIG. 7 .
FIG. 9 is a view illustrating examples of data unsuitable for updating an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 10 is a view illustrating an example of data unsuitable for updating an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 11 is a flowchart illustrating a first suitability determination method according to an embodiment of the present disclosure.
FIG. 12 is a view illustrating the first suitability determination method according to an embodiment of the present disclosure.
FIG. 13 is a flowchart illustrating a second suitability determination method according to an embodiment of the present disclosure.
FIG. 14 is a view illustrating a Jacobian norm score according to an embodiment of the present disclosure.
FIG. 15 is a flowchart illustrating a method for determining a second reference value for a Jacobian norm score for each class according to an embodiment of the present disclosure.
FIG. 16 is a flowchart illustrating a third suitability determination method according to an embodiment of the present disclosure.
FIG. 17 is a flowchart illustrating a fourth suitability determination method according to an embodiment of the present disclosure.
FIGS. 18 and 19 are views illustrating kernel analysis according to an embodiment of the present disclosure.
FIG. 20 is a view illustrating an artificial intelligence system for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 21 is a view illustrating an artificial intelligence system for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes âmoduleâ and âunitâ for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included.
It will be understood that the terms âfirstâ and âsecondâ are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components.
In this disclosure below, when one part (or element, device, etc.) is referred to as being âconnectedâ to another part (or element, device, etc.), it should be understood that the former can be âdirectly connectedâ to the latter, or âelectrically connectedâ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being âdirectly connectedâ or âdirectly linkedâ to another component, it means that no intervening component is present.
<Artificial Intelligence (AI)>
Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.
An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value.
The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse.
Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function.
The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network.
Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.
The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the training data is input to the artificial neural network. The unsupervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is not given. The reinforcement learning may refer to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state.
Machine learning, which is implemented as a deep neural network (DNN) including a plurality of hidden layers among artificial neural networks, is also referred to as deep learning, and the deep learning is part of machine learning. In the following, machine learning is used to mean deep learning.
<Robot>
A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot.
Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field.
The robot includes a traveling actuator including an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a traveling actuator, and may travel on the ground through the traveling actuator or fly in the air.
<Self-Driving>
Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user.
For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a predetermined route, and a technology for automatically setting and traveling a route when a destination is set.
The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but a
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. patent application Ser. No. 16/730,669, filed on Dec. 30, 2019, which claims the benefit of earlier filing date and right of priority to Korean Patent Application No. 10-2019-0162598, filed on Dec. 9, 2019, the contents of which are all hereby incorporated by reference herein their entirety.
BACKGROUND
The present disclosure relates to an artificial intelligence apparatus for generating training data for an artificial intelligence model by selecting data suitable for training an artificial intelligence model among collected data and a method thereof.
Recently, many artificial intelligence evolution techniques have been adopted to continuously update the artificial intelligence model to reflect usage logs of users. When the artificial intelligence model is updated based on the usage logs of the real users, since training data suitable for the usage aspects of the real users can be obtained, there is an advantage that a more realistic and useful artificial intelligence model can be configured.
Artificial intelligence evolution technology based on the user's usage log basically requires the assumption that the user provides normal feedback through normal use. If a user accidentally or intentionally provides wrong feedback, the artificial intelligence model may be updated based on a wrong usage log, which may cause the artificial intelligence model to be incorrectly learned.
Therefore, there is a need for a method for selectively generating training data by determining whether the collected usage logs are suitable for updating the artificial intelligence model.
SUMMARY
The present disclosure provides an artificial intelligence apparatus which determines whether input data and feedback corresponding thereto are data suitable for updating an artificial intelligence model and generates training data based on data suitable for the artificial intelligence model, and a method thereof.
An embodiment of the present disclosure provides an artificial intelligence apparatus which generates an inference result for input data using an artificial intelligence model, receives feedback corresponding to the inference result, determines whether the input data and the feedback are suitable for updating the artificial intelligence model, and generates input data and feedback determined to be suitable for updating an artificial intelligence model as training data, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which determines suitability of the input data and the feedback by using a plurality of suitability determination methods and determines whether the input data and the feedback are suitable for updating the artificial intelligence model based on the suitability ratio for the plurality of suitability determination results, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which determines whether input data and feedback are suitable for updating an artificial intelligence model by determining whether input data is an outlier as a first suitability determination method, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which calculates a sensitivity of an artificial intelligence model for input data as a second suitability determination method and determines whether input data and feedback are suitable for updating the artificial intelligence model in consideration of the calculated sensitivity, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which generates a plurality of temporary inference results for input data using an artificial intelligence model as a third suitability determination method and compares ensemble inference results generated from the plurality of temporary inference results and feedback with each other to determine whether input data and feedback are suitable for updating an artificial intelligence model, and a method thereof.
In addition, an embodiment of the present disclosure provides an artificial intelligence apparatus which calculates a kernel score in a class corresponding to feedback through kernel analysis as a fourth suitability determination method and determines whether input data and feedback are suitable for updating the artificial intelligence model in consideration of the calculated kernel score, and a method thereof.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram illustrating an AI apparatus according to an embodiment of the present disclosure.
FIG. 2 is a block diagram illustrating an AI server according to an embodiment of the present disclosure.
FIG. 3 is a view illustrating an AI system according to an embodiment of the present disclosure.
FIG. 4 is a block diagram illustrating an AI apparatus according to an embodiment of the present disclosure.
FIG. 5 is a view illustrating an artificial intelligence system for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 6 is a view illustrating an artificial intelligence system for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 7 is a flowchart illustrating a method for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 8 is a flowchart illustrating an example of a step S 707 of determining suitability of input data and feedback for updating the artificial intelligence model illustrated in FIG. 7 .
FIG. 9 is a view illustrating examples of data unsuitable for updating an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 10 is a view illustrating an example of data unsuitable for updating an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 11 is a flowchart illustrating a first suitability determination method according to an embodiment of the present disclosure.
FIG. 12 is a view illustrating the first suitability determination method according to an embodiment of the present disclosure.
FIG. 13 is a flowchart illustrating a second suitability determination method according to an embodiment of the present disclosure.
FIG. 14 is a view illustrating a Jacobian norm score according to an embodiment of the present disclosure.
FIG. 15 is a flowchart illustrating a method for determining a second reference value for a Jacobian norm score for each class according to an embodiment of the present disclosure.
FIG. 16 is a flowchart illustrating a third suitability determination method according to an embodiment of the present disclosure.
FIG. 17 is a flowchart illustrating a fourth suitability determination method according to an embodiment of the present disclosure.
FIGS. 18 and 19 are views illustrating kernel analysis according to an embodiment of the present disclosure.
FIG. 20 is a view illustrating an artificial intelligence system for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
FIG. 21 is a view illustrating an artificial intelligence system for generating training data for an artificial intelligence model according to an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes âmoduleâ and âunitâ for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included.
It will be understood that the terms âfirstâ and âsecondâ are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components.
In this disclosure below, when one part (or element, device, etc.) is referred to as being âconnectedâ to another part (or element, device, etc.), it should be understood that the former can be âdirectly connectedâ to the latter, or âelectrically connectedâ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being âdirectly connectedâ or âdirectly linkedâ to another component, it means that no intervening component is present.
<Artificial Intelligence (AI)>
Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.
An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value.
The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse.
Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function.
The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network.
Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.
The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the training data is input to the artificial neural network. The unsupervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is not given. The reinforcement learning may refer to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state.
Machine learning, which is implemented as a deep neural network (DNN) including a plurality of hidden layers among artificial neural networks, is also referred to as deep learning, and the deep learning is part of machine learning. In the following, machine learning is used to mean deep learning.
<Robot>
A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot.
Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field.
The robot includes a traveling actuator including an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a traveling actuator, and may travel on the ground through the traveling actuator or fly in the air.
<Self-Driving>
Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user.
For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a predetermined route, and a technology for automatically setting and traveling a route when a destination is set.
The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but also a train, a motorcycle, and the like.
Here, the self-driving vehicle may be regarded as a robot having a self-driving function.
<eXtended Reality (XR)>
Extended reality is collectively referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The VR technology provides a real-world object and background only as a CG image, the AR technology provides a virtual CG image on a real object image, and the MR technology is a computer graphic technology that mixes and combines virtual objects into the real world.
The MR technology is similar to the AR technology in that the real object and the virtual object are illustrated together. However, in the AR technology, the virtual object is used in the form that complements the real object, whereas in the MR technology, the virtual object and the real object are used in an equal manner.
The XR technology may be applied to a head-mount display (HMD), a head-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop, a TV, a digital signage, and the like. A device to which the XR technology is applied may be referred to as an XR device.
FIG. 1 is a block diagram illustrating an AI apparatus 100 according to an embodiment of the present disclosure.
Hereinafter, the AI apparatus 100 may be referred to as a terminal.
The AI apparatus (or an AI device) 100 may be implemented by a stationary device or a mobile device, such as a TV, a projector, a mobile phone, a smartphone, a desktop computer, a notebook, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, a tablet PC, a wearable device, a set-top box (STB), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, and the like.
Referring to FIG. 1 , the AI apparatus 100 may include a communication interface 110 , an input interface 120 , a learning processor 130 , a sensor 140 , an output interface 150 , a memory 170 , and a processor 180 .
The communication interface 110 may transmit and receive data to and from external devices such as other AI apparatuses 100 a to 100 e and the AI server 200 by using wire/wireless communication technology. For example, the communication interface 110 may transmit and receive sensor information, a user input, a learning model, and a control signal to and from external devices.
The communication technology used by the communication interface 110 includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth⢠RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.
The input interface 120 may acquire various kinds of data.
Here, the input interface 120 may include a camera for inputting a video signal, a microphone for receiving an audio signal, and a user input interface for receiving information from a user. The camera or the microphone may be treated as a sensor, and the signal acquired from the camera or the microphone may be referred to as sensing data or sensor information.
The input interface 120 may acquire a training data for model learning and an input data to be used when an output is acquired by using learning model. The input interface 120 may acquire raw input data. Here, the processor 180 or the learning processor 130 may extract an input feature by preprocessing the input data.
The learning processor 130 may learn a model composed of an artificial neural network by using training data. The learned artificial neural network may be referred to as a learning model. The learning model may be used to an infer result value for new input data rather than training data, and the inferred value may be used as a basis for determination to perform a certain operation.
Here, the learning processor 130 may perform AI processing together with the learning processor 240 of the AI server 200 .
Here, the learning processor 130 may include a memory integrated or implemented in the AI apparatus 100 . Alternatively, the learning processor 130 may be implemented by using the memory 170 , an external memory directly connected to the AI apparatus 100 , or a memory held in an external device.
The sensor 140 may acquire at least one of internal information about the AI apparatus 100 , ambient environment information about the AI apparatus 100 , and user information by using various sensors.
Examples of the sensors included in the sensor 140 may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, and a radar.
The output interface 150 may generate an output related to a visual sense, an auditory sense, or a haptic sense.
Here, the output interface 150 may include a display for outputting time information, a speaker for outputting auditory information, and a haptic module for outputting haptic information.
The memory 170 may store data that supports various functions of the AI apparatus 100 . For example, the memory 170 may store input data acquired by the input interface 120 , training data, a learning model, a learning history, and the like.
The processor 180 may determine at least one executable operation of the AI apparatus 100 based on information determined or generated by using a data analysis algorithm or a machine learning algorithm. The processor 180 may control the components of the AI apparatus 100 to execute the determined operation.
To this end, the processor 180 may request, search, receive, or utilize data of the learning processor 130 or the memory 170 . The processor 180 may control the components of the AI apparatus 100 to execute the predicted operation or the operation determined to be desirable among the at least one executable operation.
When the connection of an external device is required to perform the determined operation, the processor 180 may generate a control signal for controlling the external device and may transmit the generated control signal to the external device.
The processor 180 may acquire intention information for the user input and may determine the user's requirements based on the acquired intention information.
The processor 180 may acquire the intention information corresponding to the user input by using at least one of a speech to text (STT) engine for converting speech input into a text string or a natural language processing (NLP) engine for acquiring intention information of a natural language.
At least one of the STT engine or the NLP engine may be configured as an artificial neural network, at least part of which is learned according to the machine learning algorithm. At least one of the STT engine or the NLP engine may be learned by the learning processor 130 , may be learned by the learning processor 240 of the AI server 200 , or may be learned by their distributed processing.
The processor 180 may collect history information including the operation contents of the AI apparatus 100 or the user's feedback on the operation and may store the collected history information in the memory 170 or the learning processor 130 or transmit the collected history information to the external device such as the AI server 200 . The collected history information may be used to update the learning model.
The processor 180 may control at least part of the components of AI apparatus 100 so as to drive an application program stored in memory 170 . Furthermore, the processor 180 may operate two or more of the components included in the AI apparatus 100 in combination so as to drive the application program.
FIG. 2 is a block diagram illustrating an AI server 200 according to an embodiment of the present disclosure.
Referring to FIG. 2 , the AI server 200 may refer to a device that learns an artificial neural network by using a machine learning algorithm or uses a learned artificial neural network. The AI server 200 may include a plurality of servers to perform distributed processing, or may be defined as a 5G network. Here, the AI server 200 may be included as a partial configuration of the AI apparatus 100 , and may perform at least part of the AI processing together.
The AI server 200 may include a communication interface 210 , a memory 230 , a learning processor 240 , a processor 260 , and the like.
The communication interface 210 can transmit and receive data to and from an external device such as the AI apparatus 100 .
The memory 230 may include a model storage 231 . The model storage 231 may store a learning or learned model (or an artificial neural network 231 a ) through the learning processor 240 .
The learning processor 240 may learn the artificial neural network 231 a by using the training data. The learning model may be used in a state of being mounted on the AI server 200 of the artificial neural network, or may be used in a state of being mounted on an external device such as the AI apparatus 100 .
The learning model may be implemented in hardware, software, or a combination of hardware and software. If all or part of the learning models are implemented in software, one or more instructions that constitute the learning model may be stored in memory 230 .
The processor 260 may infer the result value for new input data by using the learning model and may generate a response or a control command based on the inferred result value.
FIG. 3 is a view illustrating an AI system 1 according to an embodiment of the present disclosure.
Referring to FIG. 3 , in the AI system 1 , at least one of an AI server 200 , a robot 100 a , a self-driving vehicle 100 b , an XR device 100 c , a smartphone 100 d , or a home appliance 100 e is connected to a cloud network 10 . The robot 100 a , the self-driving vehicle 100 b , the XR device 100 c , the smartphone 100 d , or the home appliance 100 e , to which the AI technology is applied, may be referred to as AI apparatuses 100 a to 100 e.
The cloud network 10 may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure. The cloud network 10 may be configured by using a 3G network, a 4G or LTE network, or a 5G network.
That is, the devices 100 a to 100 e and 200 configuring the AI system 1 may be connected to each other through the cloud network 10 . In particular, each of the devices 100 a to 100 e and 200 may communicate with each other through a base station, but may directly communicate with each other without using a base station.
The AI server 200 may include a server that performs AI processing and a server that performs operations on big data.
The AI server 200 may be connected to at least one of the AI apparatuses constituting the AI system 1 , that is, the robot 100 a , the self-driving vehicle 100 b , the XR device 100 c , the smartphone 100 d , or the home appliance 100 e through the cloud network 10 , and may assist at least part of AI processing of the connected AI apparatuses 100 a to 100 e.
Here, the AI server 200 may learn the artificial neural network according to the machine learning algorithm instead of the AI apparatuses 100 a to 100 e , and may directly store the learning model or transmit the learning model to the AI apparatuses 100 a to 100 e.
Here, the AI server 200 may receive input data from the AI apparatuses 100 a to 100 e , may infer the result value for the received input data by using the learning model, may generate a response or a control command based on the inferred result value, and may transmit the response or the control command to the AI apparatuses 100 a to 100 e.
Alternatively, the AI apparatuses 100 a to 100 e may infer the result value for the input data by directly using the learning model, and may generate the response or the control command based on the inference result.
Hereinafter, various embodiments of the AI apparatuses 100 a to 100 e to which the above-described technology is applied will be described. The AI apparatuses 100 a to 100 e illustrated in FIG. 3 may be regarded as a specific embodiment of the AI apparatus 100 illustrated in FIG. 1 .
<AI+Robot>
The robot 100 a , to which the AI technology is applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like.
The robot 100 a may include a robot control module for controlling the operation, and the robot control module may refer to a software module or a chip implementing the software module by hardware.
The robot 100 a may acquire state information about the robot 100 a by using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, may determine the response to user interaction, or may determine the operation.
The robot 100 a may use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan.
The robot 100 a may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the robot 100 a may recognize the surrounding environment and the objects by using the learning model, and may determine the operation by using the recognized surrounding information or object information. The learning model may be learned directly from the robot 100 a or may be learned from an external device such as the AI server 200 .
Here, the robot 100 a may perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server 200 and the generated result may be received to perform the operation.
The robot 100 a may use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external device to determine the travel route and the travel plan, and may control the traveling actuator such that the robot 100 a travels along the determined travel route and travel plan.
The map data may include object identification information about various objects arranged in the space in which the robot 100 a moves. For example, the map data may include object identification information about fixed objects such as walls and doors and movable objects such as pollen and desks. The object identification information may include a name, a type, a distance, and a position.
In addition, the robot 100 a may perform the operation or travel by controlling the traveling actuator based on the control/interaction of the user. Here, the robot 100 a may acquire the intention information of the interaction due to the user's operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation.
<div id="p-0112
CLAIMS
Claims ( 13 )
What is claimed is:
1. An artificial intelligence apparatus for generating training data, comprising:
a memory configured to store an artificial intelligence model;
an input interface including a microphone or a camera; and
a processor configured to:
receive, via the input interface, input data,
generate an inference result corresponding to the input data by using the artificial intelligence model,
receive feedback corresponding to the inference result,
determine suitability of the input data and the feedback for updating the artificial intelligence model, and
generate training data based on the input data and the feedback if the input data and the feedback are determined as data suitable for updating of the artificial intelligence model,
wherein the processor is configured to determine the suitability of the input data and the feedback using at least one of a plurality of suitability determination methods,
wherein the plurality of suitability determination methods include a fourth suitability determination method which determines whether the input data and the feedback are unsuitable for updating the artificial intelligence model based on a kernel score in a class corresponding to the feedback through a kernel analysis, and
wherein the kernel score is a value obtained by dividing the sum of eigenvalues obtained through eigen decomposition by the maximum eigenvalue,
wherein the processor is further configured to:
calculate a first kernel score in a class corresponding to the feedback excluding the input data,
calculate a second kernel score in a class corresponding to the feedback including the input data, and
determine whether the input data and the feedback is suitable based on the first kernel score and the second kernel score.
2. The artificial intelligence apparatus of claim 1 ,
wherein the processor is configured to determine the input data and the feedback as data suitable which updates the artificial intelligence model if a suitability determination ratio to the plurality of suitability determination results according to the plurality of suitability determination methods is equal to or greater than a reference ratio.
3. The artificial intelligence apparatus of claim 1 ,
wherein the plurality of suitability determination methods includes a first suitability determination method for determining the input data and the feedback as data unsuitable for updating the artificial intelligence model if the input data is an outlier, and
wherein the processor is configured to:
if the first suitability determination method is used,
calculate similarity between a first training data corresponding to a class corresponding to the feedback among the training data used for the training of the artificial intelligence model and the input data, and
determine the input data as an outlier if the similarity is less than a first reference value.
4. The artificial intelligence apparatus of claim 3 ,
wherein the processor is configured to:
if the first suitability determination method is used,
map the input data and the first training data to a feature space by using the artificial intelligence model,
calculate a distance between the input data and the first training data on the feature space, and
calculate the similarity to be high as the calculated distance is short.
5. The artificial intelligence apparatus of claim 1 ,
wherein the plurality of suitability determination methods include a second suitability determination method which determines the input data and the feedback as data unsuitable for updating the artificial intelligence model if the sensitivity of the artificial intelligence model for the input data is greater than a second reference value corresponding to a class corresponding to the feedback.
6. The artificial intelligence apparatus of claim 5 ,
wherein the processor is configured to, if the second suitability determination method is used, calculate a Jacobian Norm Score of the artificial intelligence model for the input data as the sensitivity, and
wherein the Jacobian Norm score is an expected value of Frobenius Norm of the Jacobian matrix of the artificial intelligence model for the input data.
7. The artificial intelligence apparatus of claim 1 ,
wherein the plurality of suitability determination methods includes a third suitability determination method which generates a plurality of temporary inference results to the input data using the artificial intelligence model, generates an ensemble inference result from the plurality of temporary inference results, and determines the input data and the feedback as data unsuitable for updating the artificial intelligence model if the ensemble inference result is inconsistent with the feedback.
8. The artificial intelligence apparatus of claim 7 ,
wherein the processor is configured to:
if the third suitability determination method is used,
apply a dropout to the artificial intelligence model to generate the plurality of temporary inference results, and
generate the temporary inference result having the highest frequency among the plurality of temporary inference results as the ensemble inference result.
9. The artificial intelligence apparatus of claim 1 ,
wherein the processor is configured to:
determine the input data and the feedback as data unsuitable for updating the artificial intelligence model if an increasing amount from the first kernel score to the second kernel score exceeds a third reference value.
10. The artificial intelligence apparatus of claim 1 ,
wherein the feedback includes feedback generated from user input or feedback generated from operation information of the artificial intelligence apparatus.
11. The artificial intelligence apparatus of claim 1 ,
wherein the processor is configured to update the artificial intelligence model using the generated training data.
12. A method for generating training data comprising:
receiving input data through an input interface including a microphone or a camera;
generating an inference result corresponding to the input data using an artificial intelligence model;
receiving feedback corresponding to the inference result;
determining suitability of the input data and the feedback for updating the artificial intelligence model; and
generating training data based on the input data and the feedback if it is determined that the input data and the feedback are suitable for updating the artificial intelligence model,
wherein the determining step includes determining the suitability of the input data and the feedback using at least one of a plurality of suitability determination methods,
wherein the plurality of suitability determination methods include a fourth suitability determination method which determines whether the input data and the feedback are unsuitable for updating the artificial intelligence model based on a kernel score in a class corresponding to the feedback through a kernel analysis, and
wherein the kernel score is a value obtained by dividing the sum of eigenvalues obtained through eigen decomposition by the maximum eigenvalue,
wherein the method further comprises:
calculate a first kernel score in a class corresponding to the feedback excluding the input data,
calculate a second kernel score in a class corresponding to the feedback including the input data, and
determine whether the input data and the feedback are suitable based on the first kernel score and the second kernel score.
13. A non-transitory recording medium having recorded thereon a program for performing a method for generating training data, the method comprising:
receiving input data through an input interface including a microphone or a camera;
generating an inference result corresponding to the input data using an artificial intelligence model;
receiving feedback corresponding to the inference result;
determining suitability of the input data and the feedback for updating the artificial intelligence model; and
generating training data based on the input data and the feedback if it is determined that the input data and the feedback are suitable for updating the artificial intelligence model,
wherein the determining step includes determining the suitability of the input data and the feedback using at least one of a plurality of suitability determination methods,
wherein the plurality of suitability determination methods include a fourth suitability determination method which determines whether the input data and the feedback are unsuitable for updating the artificial intelligence model based on a kernel score in a class corresponding to the feedback through a kernel analysis, and
wherein the kernel score is a value obtained by dividing the sum of eigenvalues obtained through eigen decomposition by the maximum eigenvalue,
wherein the method further comprises:
calculate a first kernel score in a class corresponding to the feedback excluding the input data,
calculate a second kernel score in a class corresponding to the feedback including the input data, and
determine whether the input data and the feedback are suitable based on the first kernel score and the second kernel score.
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