A search method for a neural network model structure, includes: generating an initial generation population of network model structure based on multi-objective optimization hyper parameters, as a current generation population of network model structure; performing selection and crossover on the current generation population of network model structure; generating a part of network model structure based on reinforcement learning mutation, and generating a remaining part of network model structure based on random mutation on the selected and crossed network model structure; generating a new population of network model structure based on the part of network model structure generated by reinforcement learning mutation and the remaining part of network model structure generated by random mutation; and searching a next generation population of network model structure based on the current generation population of network model structure and the new population of network model structure.

Systems and methods for emotionally intelligent automated chatting are provided. The systems and method provide emotionally intelligent automated (or artificial intelligence) chatting by determining a context and an emotion of a conversation with a user. Based on these determinations, the systems and methods may select one or more responses from a database of responses to a reply to a user query. Further, the systems and methods are able update or train based on user feedback and/or world feedback.

Systems and methods for emotionally intelligent automated chatting are provided. The systems and method provide emotionally intelligent automated (or artificial intelligence) chatting by determining a context and an emotion of a conversation with a user. Based on these determinations, the systems and methods may select one or more responses from a database of responses to a reply to a user query. Further, the systems and methods are able update or train based on user feedback and/or world feedback.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting intermediate reinforcement learning goals. One of the methods includes obtaining a plurality of demonstration sequences, each of the demonstration sequences being a sequence of images of an environment while a respective instance of a reinforcement learning task is being performed; for each demonstration sequence, processing each image in the demonstration sequence through an image processing neural network to determine feature values for a respective set of features for the image; determining, from the demonstration sequences, a partitioning of the reinforcement learning task into a plurality of subtasks, wherein each image in each demonstration sequence is assigned to a respective subtask of the plurality of subtasks; and determining, from the feature values for the images in the demonstration sequences, a respective set of discriminative features for each of the plurality of subtasks.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

Systems and methods are provided for performing operations including: receiving, by a messaging application, input that selects an option to generate a message using an avatar with a caption; presenting, by the messaging application, the avatar and a caption entry region proximate to the avatar; populating, by the messaging application, the caption entry region with a text string comprising one or more words; determining, by the messaging application, context based on the one or more words in the text string; and modifying, by the messaging application, an expression of the avatar based on the determined context.

Data channel parameter optimization with intelligent selection of initial data channel conditions and optimization algorithm hyperparameters for use of a black box optimizer to optimize one or more data channel parameters. It is currently identified that the initial data channel condition affects the ability of a black box optimizer to optimize data channel parameters. In turn, by use of an intelligent agent (e.g., employing artificial intelligence or machine learning) to iteratively select optimized initial data channel conditions, the optimization of the data channel may be improved. Moreover, the sensitivity of the data channel parameters may be determined, which allows for identification of a subset of data channel parameters that are varied in an optimization approach. This may result in improved performance of the optimization without sacrificing optimized performance of the data channel.

Embodiments for intelligent generation of customized questions or questionnaires by a processor. One or more customized questions or questionnaires may be generated according to a user profile, similar profiles of alternative users, one or more historical interactions with the alternative users, one or more goals defined by a domain experts, domain knowledge, historical questions or questionnaires, or a combination thereof using a machine learning operation.

Systems and methods are provided for efficient off-policy credit assignment (ECA) in reinforcement learning. ECA allows principled credit assignment for off-policy samples, and therefore improves sample efficiency and asymptotic performance. One aspect of ECA is to formulate the optimization of expected return as approximate inference, where policy is approximating a learned prior distribution, which leads to a principled way of utilizing off-policy samples. Other features are also provided.