Abstract: A system includes a computing platform having a hardware processor and a memory storing software code and a machine learning (ML) model trained to predict the next element of a sequence. The software code is executed to receive input data identifying an element of the sequence, determine, using the input data, at least one mood driver(s) of the sequence, and predict, based on input data and the mood driver(s), one or more candidate next element(s) of the sequence using the ML model. The software code further obtains expertise data relating to the sequence, evaluates the candidate next element(s), using the expertise data, the input data, and the mood driver(s), to provide aptness score(s) each corresponding to a respective one candidate next element, and determines, using the aptness score(s) and a respective probability assigned to each of the candidate next element(s) by the ML model, the next element of the sequence.

Abstract: A system includes a computing platform having a hardware processor and a memory storing software code, a memory data structure storing memory features for an artificial intelligence interactive character (AIIC), and a trained machine learning (ML) model. The software code is executed to elicit, using the AIIC, a reminiscence from a user, predict, using the trained ML model and the reminiscence, one or more user memory feature(s) of the reminiscence, identify, using the memory data structure, one or more of the memory features for the AIIC as corresponding to the user memory feature(s), and determine, using the user memory feature(s), a mood modifier for a creative composition. The software code is further executed to produce, based on the mood modifier and the corresponding one or more of the plurality of memory features for the AIIC, the creative composition, and provide the creative composition to the AIIC.

Abstract: Systems and methods capture image dynamics and use those captured image dynamics for image feature recognition and classification. Other methods and systems train a neural network to capture image dynamics. An image vector representing image dynamics is extracted from an image of an image stream using a first neural network. A second neural network, predicts a previous and/or subsequent image in the image stream from the image vector. The predicted previous and/or subsequent image is compared with an actual previous and/or subsequent image from the image stream. The first and second neural networks are trained using the result of the comparison.

Abstract: Systems and methods for training a neural network to predict states of a robotics device are disclosed. Robotics data is received for a robotics device, including indications of a set of components, a digital simulation of the robotics device, and measurement data received from a sensor associated with the robotics device. The set of components includes an actuator and a structural element. A training dataset is generated using the received robotics data. Generating the training dataset includes comparing the measurement data with simulated measurement data based on the digital simulation. A neural network is trained using the generated training dataset to modify the digital simulation of the robotics device to predict a state of the robotics device, such as a position, motion, electrical quantity, or other. When trained, the neural network is applied to predict states of the robotics device or a different robotics device.

Abstract: A system for monitoring and improving social agent interaction quality includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive, from a social agent, interaction data describing an interaction of the social agent with a user, and to perform an assessment of the interaction, using the interaction data, as one of successful or including a flaw. When the assessment indicates that the interaction includes the flaw, the processing hardware is further configured to execute the software code to identify an interaction strategy for correcting the flaw, and to deliver, to the social agent, one or both of the assessment and the interaction strategy to correct the flaw in the interaction.

Abstract: A device provides audible output pertaining to audio video (AV) content such as a video game based on a section of the AV content that is being presented.

Abstract: A system for providing a situationally aware social agent includes processing hardware and a memory storing a software code. The processing hardware executes the software code to receive radar data and audio data, process the radar data and the audio data to obtain radar-based location data and audio-based location data each corresponding to a location of one or more user(s), and process the radar data and the audio data to obtain radar-based venue data and audio-based venue data each corresponding to an environment surrounding the user(s). The software code further determines, using the radar-based location data and the audio-based location data, the location of the user(s), determines, using the radar-based venue data and the microphone-based venue data, the environment surrounding the user(s), and identifies, based on the location and the environment, an interactive expression for use by the situationally aware social agent to interact with the user(s).

Abstract: Systems and methods are presented for immersive and simultaneous animation in a mixed reality environment. Techniques disclosed represent a physical object, present at a scene, in a 3D space of a virtual environment associated with the scene. A virtual element is posed relative to the representation of the physical object in the virtual environment. The virtual element is displayed to users from a perspective of each user in the virtual environment. Responsive to an interaction of one user with the virtual element, an edit command is generated and the pose of the virtual element is adjusted in the virtual environment according to the edit command. The display of the virtual element to the users is then updated according to the adjusted pose. When simultaneous and conflicting edit commands are generated by collaborating users, policies to reconcile the conflicting edit commands are disclosed.

Abstract: A system for performing machine language (ML) model based condition and property detection includes a computing platform having processing hardware and a system memory storing a software code that includes a trained ML model. The processing hardware is configured to execute the software code to receive a dataset, and perform an analysis of the dataset, using a first stage of the trained ML model, to detect a presence of a predetermined data attribute. The processing hardware is further configured to execute the software code to predict, using a second stage of the trained ML model when the analysis of the dataset detects the presence of the predetermined data attribute, a probability that the predetermined data attribute is indicative of a condition or a property.

Abstract: A system for monitoring and improving social agent interaction quality includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive, from a social agent, interaction data describing an interaction of the social agent with a user, and to perform an assessment of the interaction, using the interaction data, as one of successful or including a flaw. When the assessment indicates that the interaction includes the flaw, the processing hardware is further configured to execute the software code to identify an interaction strategy for correcting the flaw, and to deliver, to the social agent, one or both of the assessment and the interaction strategy to correct the flaw in the interaction.

Abstract: A system for incremental natural language understanding includes a media module, a memory storing a software code, and a hardware processor communicatively coupled to the media module. The hardware processor is configured to execute the software code to receive an audio stream including a first utterance, and generate a first and second incremental speech recognition outputs based on first and second portions of the first utterance. In addition, the hardware processor is configured to execute the software code to determine, prior to generating the second incremental speech recognition output, a first intent of the first utterance based on the first incremental speech recognition output. The hardware processor is further configured to execute the software code to retrieve a first resource based on the determined first intent, and incorporate the first resource in the media content to be played by the media module.

Abstract: A system for incremental natural language understanding includes a media module, a memory storing a software code, and a hardware processor communicatively coupled to the media module. The hardware processor is configured to execute the software code to receive an audio stream including a first utterance, and generate a first and second incremental speech recognition outputs based on first and second portions of the first utterance. In addition, the hardware processor is configured to execute the software code to determine, prior to generating the second incremental speech recognition output, a first intent of the first utterance based on the first incremental speech recognition output. The hardware processor is further configured to execute the software code to retrieve a first resource based on the determined first intent, and incorporate the first resource in the media content to be played by the media module.

Abstract: An audio processing system for generating audio including emotionally expressive synthesized content includes a computing platform having a hardware processor and a memory storing a software code including a trained neural network. The hardware processor is configured to execute the software code to receive an audio sequence template including one or more audio segment(s) and an audio gap, and to receive data describing one or more words for insertion into the audio gap. The hardware processor is configured to further execute the software code to use the trained neural network to generate an integrated audio sequence using the audio sequence template and the data, the integrated audio sequence including the one or more audio segment(s) and at least one synthesized word corresponding to the one or more words described by the data.

Abstract: An audio processing system for generating audio including emotionally expressive synthesized content includes a computing platform having a hardware processor and a memory storing a software code including a trained neural network. The hardware processor is configured to execute the software code to receive an audio sequence template including one or more audio segment(s) and an audio gap, and to receive data describing one or more words for insertion into the audio gap. The hardware processor is configured to further execute the software code to use the trained neural network to generate an integrated audio sequence using the audio sequence template and the data, the integrated audio sequence including the one or more audio segment(s) and at least one synthesized word corresponding to the one or more words described by the data.

Abstract: A device provides audible output pertaining to audio video (AV) content such as a video game based on a section of the AV content that is being presented.

Abstract: A device provides audible output pertaining to audio video (AV) content such as a video game based on a section of the AV content that is being presented.

Abstract: A device provides audible output pertaining to audio video (AV) content such as a video game based on a section of the AV content that is being presented.

Abstract: A system and method for multimodal classification of user characteristics is described. The method comprises receiving audio and other inputs, extracting fundamental frequency information from the audio input, extracting other feature information from the video input, classifying the fundamental frequency information, textual information and video feature information using the multimodal neural network.

Abstract: Systems and methods capture image dynamics and use those captured image dynamics for image feature recognition and classification. Other methods and systems train a neural network to capture image dynamics. An image vector representing image dynamics is extracted from an image of an image stream using a first neural network. A second neural network, predicts a previous and/or subsequent image in the image stream from the image vector. The predicted previous and/or subsequent image is compared with an actual previous and/or subsequent image from the image stream. The first and second neural networks are trained using the result of the comparison.