A method for automatically training a neural network includes at a trainer having a first communication device and a perception recorder, continuously recording the surroundings in the vicinity of the first object; receiving, at the trainer, a message from a communication device associated with an object in the vicinity of the trainer, the message including information about the position and the type of the object; identifying a recording corresponding to the time at which the message is received from the object; correlating the received positional information about the second object with a corresponding location in the recording to identify the object in the recording; classifying the identified object based on the type of information received in the message from the object; and using the classified recording to train the neural network.

A method for automatically training a neural network includes at a trainer having a first communication device and a perception recorder, continuously recording the surroundings in the vicinity of the first object; receiving, at the trainer, a message from a communication device associated with an object in the vicinity of the trainer, the message including information about the position and the type of the object; identifying a recording corresponding to the time at which the message is received from the object; correlating the received positional information about the second object with a corresponding location in the recording to identify the object in the recording; classifying the identified object based on the type of information received in the message from the object; and using the classified recording to train the neural network.

A tendency of an action of a robot may vary based on learning data used for training. The learning data may be generated by an agent performing an identical or similar task to a task of the robot. An apparatus and method for updating a policy for controlling an action of a robot may update the policy of the robot using a plurality of learning data sets generated by a plurality of heterogeneous agents, such that the robot may appropriately act even in an unpredicted environment.

A tendency of an action of a robot may vary based on learning data used for training. The learning data may be generated by an agent performing an identical or similar task to a task of the robot. An apparatus and method for updating a policy for controlling an action of a robot may update the policy of the robot using a plurality of learning data sets generated by a plurality of heterogeneous agents, such that the robot may appropriately act even in an unpredicted environment.

Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.

The disclosure herein describes training and using an uncorrectable error (UE) state prediction model based on telemetry error data. Sets of UE state labels and non-UE state labels are generated from a first set of collected telemetry data, wherein the UE state labels each reference a UE and telemetry data of an interval prior to the referenced UE. Statistical features are extracted from telemetry data of the sets of UE state labels and non-UE state labels, and the extracted statistical features are used to train a UE state prediction model. A second set of collected telemetry data is obtained, and a UE event is predicted based on the second set of collected telemetry data using the trained UE state prediction model. A preventative operation is performed on a memory page of the system based on the predicted UE event, whereby the predicted UE event is prevented from occurring.

A method of utilizing an artificial intelligence, AI, having language recognition software allowing the AI to have humanistic characteristics while interacting with the seller of used vehicle. The AI identifies online vehicles for sale and downloads the seller's contact information. The AI then validates or deletes the seller's contact information. The AI then interacts with a multiplicity of valid sellers to gather additional information on the vehicle as well as make an offer to purchase the vehicle.

A method of utilizing an artificial intelligence, AI, having language recognition software allowing the AI to have humanistic characteristics while interacting with the seller of used vehicle. The AI identifies online vehicles for sale and downloads the seller's contact information. The AI then validates or deletes the seller's contact information. The AI then interacts with a multiplicity of valid sellers to gather additional information on the vehicle as well as make an offer to purchase the vehicle.

An embodiment for automatically adapting an artificial intelligence (AI) system from a first environment to a second environment is provided. The embodiment may include receiving a digital model associated with each user of a plurality of users. The embodiment may also include identifying one or more characteristics of the digital model. The embodiment may further include executing a simulation of movements and activities for the digital model. The embodiment may also include creating a set of commands to be asked by the digital model. The embodiment may further include providing the set of commands to an AI virtual assistant. The embodiment may also include in response to determining the AI virtual assistant is not able to execute each command, identifying the digital model for each user whose command was not able to be executed. The embodiment may further include recommending one or more corrective actions.

An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.