Abstract: Methods and systems that allow neural network systems to maintain or increase operational accuracy while being able to operate in various settings. A set of training data is collected over each of at least two different settings. Each setting has a set of characteristics. Examples of setting characteristic types can be time, geographical location, and/or weather condition. Each set of training data is used to train a neural network resulting in a set of coefficients. For each setting, the setting characteristics are associated with the corresponding neural network having the resulting coefficients and neural network structure. A neural network, having the coefficients and neural network structure resulted after training using the training data collected over a setting, would yield optimal results when operated in/under the setting. A database management system can store information relating to, for example, the setting characteristics, neural network coefficients, and/or neural network structures.

Abstract: A method of operating an apparatus using a control system that includes at least one neural network. The method includes receiving an input value captured by the apparatus, processing the input value using the at least one neural network of the control system implemented on first one or more solid-state chips, and obtaining an output from the at least one neural network resulting from processing the input value. The method may also include processing the output with another neural network implemented on solid-state chips to determine whether the output breaches a predetermined condition that is unchangeable after an initial installation onto the control system. The aforementioned another neural network is prevented from being retrained. The method may also include the step of using the output from the at least one neural network to control the apparatus unless the output breaches the predetermined condition. Similar corresponding apparatuses are described.

Abstract: Methods and systems for controlling an autonomous machine. The autonomous machine has sensors generating input data, while the controller includes two or more neural networks that inference using the input data and generate output data. The neural networks can be trained using an identical set of training data set. The output data from each of the neural networks are monitored to ensure that the integrity of the operation is maintained by, for example, the output data from one neural network is compared with the output data from another neural network to verify the consistency. If the comparison yields that the integrity of the system is not maintained at an acceptable level, the controller can stop using the output in controlling the autonomous machine.

Abstract: Methods and systems that allow neural network systems to maintain or increase operational accuracy while being able to operate in various settings that may include predicting information. A set of training data is collected over each of at least two different settings. Each setting has a set of characteristics. Examples of setting characteristic types can be time, geographical location, and/or weather condition. Each set of training data is used to train a neural network resulting in a set of coefficients. For each setting, the setting characteristics are associated with the corresponding neural network having the resulting coefficients and neural network structure. A neural network, having the coefficients and neural network structure resulted after training using the training data collected over a setting, would yield optimal results when operated in/under the setting.

Abstract: A method of operating an apparatus using a control system that includes at least one neural network. The method includes receiving an input value captured by the apparatus, processing the input value using the at least one neural network of the control system implemented on first one or more solid-state chips, and obtaining an output from the at least one neural network resulting from processing the input value. The method may also include processing the output with another neural network implemented on solid-state chips to determine whether the output breaches a predetermined condition that is unchangeable after an initial installation onto the control system. The aforementioned another neural network is prevented from being retrained. The method may also include the step of using the output from the at least one neural network to control the apparatus unless the output breaches the predetermined condition. Similar corresponding apparatuses are described.

Abstract: An apparatus having components implemented on one or more solid-state chips. The apparatus includes an input device constructed to generate an input data value (input value), and a neural network implemented on solid-state chips trained to generate an output to control the apparatus by processing the input value. The apparatus also includes another neural network implemented on solid-state chips and configured to receive the output from the neural network. The another neural network is trained to determine whether the output of the neural network corresponds to a predetermined condition and generate a control output from the output of the neural network. The apparatus includes a processor configured receive the control output from the aforementioned another neural network, and in response to the control output indicating the output of the first neural network corresponds to a predetermined condition, and control an operation of the neural network. Corresponding methods are also disclosed.

Abstract: A device implemented on solid-state chips for an autonomous machine with sensors. The device includes a neural network on the autonomous machine, trained with a first training data set that includes training data generated by a sensor located remote from the autonomous machine, and configured to generate output data after processing input data. The device also includes a processor coupled to the neural network, and a detector to receive the output data and determine whether the output data breaches a predetermined condition, and a neural network manager coupled to the neural network and adapted to re-train the first neural network using another training data set if the detector determines the output data breach the first predetermined condition; and another neural network structured and trained identical to the first neural network to generate a second output data by processing the set of input data, wherein the neural networks are executed simultaneously.

Abstract: Methods and systems that allow neural network systems to maintain or increase operational accuracy while being able to operate in various settings. A set of training data is collected over each of at least two different settings. Each setting has a set of characteristics. Examples of setting characteristic types can be time, geographical location, and/or weather condition. Each set of training data is used to train a neural network resulting in a set of coefficients. For each setting, the setting characteristics are associated with the corresponding neural network having the resulting coefficients and neural network structure. A neural network, having the coefficients and neural network structure resulted after training using the training data collected over a setting, would yield optimal results when operated in/under the setting. A database management system can store information relating to, for example, the setting characteristics, neural network coefficients, and/or neural network structures.

Abstract: Systems and methods for automatically self-correcting or correcting in real-time one or more neural networks after detecting a triggering event, or breaching boundary conditions are provided. Such a triggering event may indicate incorrect output signal or data being generated by the one or more neural networks. In particular, machine controllers of the invention limit the operations of neural networks to be within boundary conditions. Autonomous machines of the invention can be self-corrected after a breach of a boundary condition is detected. Autonomous land vehicles of the invention are capable of determining the timing of automatic transition to the manual control from automated driving mode. The controller of the invention filters and saves input-output data sets that fall within boundary conditions for later training of neural networks. The controllers of the invention include security architectures to prevent damages from virus attacks or system malfunctions.

Abstract: Systems and methods for automatically self-correcting or correcting in real-time one or more neural networks after detecting a triggering event, or breaching boundary conditions are provided. Such a triggering event may indicate incorrect output signal or data being generated by the one or more neural networks. In particular, machine controllers of the invention limit the operations of neural networks to be within boundary conditions. Autonomous machines of the invention can be self-corrected after a breach of a boundary condition is detected. Autonomous land vehicles of the invention are capable of determining the timing of automatic transition to the manual control from automated driving mode. The controller of the invention filters and saves input-output data sets that fall within boundary conditions for later training of neural networks. The controllers of the invention include security architectures to prevent damages from virus attacks or system malfunctions.

Abstract: Systems and methods for automatically self-correcting or correcting in real-time one or more neural networks after detecting a triggering event, or breaching boundary conditions are provided. Such a triggering event may indicate incorrect output signal or data being generated by the one or more neural networks. In particular, machine controllers of the invention limit the operations of neural networks to be within boundary conditions. Autonomous machines of the invention can be self-corrected after a breach of a boundary condition is detected. Autonomous land vehicles of the invention are capable of determining the timing of automatic transition to the manual control from automated driving mode. The controller of the invention filters and saves input-output data sets that fall within boundary conditions for later training of neural networks. The controllers of the invention include security architectures to prevent damages from virus attacks or system malfunctions.

Abstract: Systems and methods for automatically self-correcting or correcting in real-time one or more neural networks after detecting a triggering event, or breaching boundary conditions are provided. Such a triggering event may indicate incorrect output signal or data being generated by the one or more neural networks. In particular, machine controllers of the invention limit the operations of neural networks to be within boundary conditions. Autonomous machines of the invention can be self-corrected after a breach of a boundary condition is detected. Autonomous land vehicles of the invention are capable of determining the timing of automatic transition to the manual control from automated driving mode. The controller of the invention filters and saves input-output data sets that fall within boundary conditions for later training of neural networks. The controllers of the invention include security architectures to prevent damages from virus attacks or system malfunctions.

Abstract: Systems and methods for automatically self-correcting or correcting in real-time one or more neural networks after detecting a triggering event, or breaching boundary conditions are provided. Such a triggering event may indicate incorrect output signal or data being generated by the one or more neural networks. In particular, machine controllers of the invention limit the operations of neural networks to be within boundary conditions. Autonomous machines of the invention can be self-corrected after a breach of a boundary condition is detected. Autonomous land vehicles of the invention are capable of determining the timing of automatic transition to the manual control from automated driving mode. The controller of the invention filters and saves input-output data sets that fall within boundary conditions for later training of neural networks. The controllers of the invention include security architectures to prevent damages from virus attacks or system malfunctions.

Abstract: Systems and methods for automatically self-correcting or correcting in real-time one or more neural networks after detecting a triggering event, or breaching boundary conditions are provided. Such a triggering event may indicate incorrect output signal or data being generated by the one or more neural networks. In particular, machine controllers of the invention limit the operations of neural networks to be within boundary conditions. Autonomous machines of the invention can be self-corrected after a breach of a boundary condition is detected. Autonomous land vehicles of the invention are capable of determining the timing of automatic transition to the manual control from automated driving mode. The controller of the invention filters and saves input-output data sets that fall within boundary conditions for later training of neural networks. The controllers of the invention include security architectures to prevent damages from virus attacks or system malfunctions.