Abstract: A method for determining machine learning training parameters is disclosed. The method can include a processor receiving a first input. The processor may receive a first response to the first input, determine a first intent, and identify a first action. The processor can then determine first trainable parameter(s) and determine whether the first trainable parameter(s) is negative or positive. Further, the processor can update a training algorithm based on the first trainable parameter(s). The processor can then receive a second input and determine a second intent for the second input. The processor can also determine a second action for the second intent and transmit the second action to a user. The processor can then determine second trainable parameter(s) and determine whether the second trainable parameter(s) is positive or negative. Finally, the processor can further update the training algorithm based on the second trainable parameter(s).

Abstract: Memory augmented neural networks may use one or more neural encoders to transform input data into distributed representations and a memory module to store the representations with individual addresses. Memory augmented neural networks allow for few-shot learning capabilities because latent representations are persistent between training examples and gradient-based updates affect only certain memory locations via content-based lookups. When a query vector is not found in memory and the memory is full, existing memories that are positively associated with a particular representation may be identified, redundant memories may be aged, and updated memories may be generated. These updated memories retain relevant information acquired during training and reduce redundancy in the memories stored using the memory module, thereby improving the efficiency of data storage and reducing overfitting of data typically encountered with existing neural networks using memory modules.

Abstract: Techniques for bi-modal learning in a financial context are described. These techniques are configured to improve a user's financial acumen and bring the user into an optimal financial state. Some of these techniques are embodied in a device that operates financial education lessons specifically configured for the improving the user's current financial state. These techniques may implement rewards/penalties (in tokens) for correct/incorrect user responses to financial decisions being presented in these lessons for user to make. By exploiting the user's desire for rewards and tokens and the desire to improve the user's current financial state, these techniques may leverage machine learning techniques to identify an appropriate financial education lesson that is most likely to have a positive effect on the user. Over time, administrating the financial education lessons builds customer loyalty to the device that implements these techniques. Other embodiments are described and claimed.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method. A method can include receiving observations and a corresponding class label, determining a candidate key based on the observations, determining a current memory state of a memory module based on a similarity of stored keys to the candidate key, generating a measurement vector based on the current memory state, concatenating the candidate key and measurement vector to form a state vector, determining, based on the state vector and an action distribution policy, an action of a plurality of actions such that the determined action maximizes an expected reduction in entropy as compared to the remaining actions of the plurality actions, executing the determined action, determining a value of the determined action, and update, based on the value, the action distribution policy.

Abstract: A system for using hash keys to preserve privacy across multiple tasks is disclosed. The system may provide training batch(es) of input observations each having a customer request and stored task to an encoder, and assign a hash key(s) to each of the stored tasks. The system may provide a new batch of input observations with a new customer request and new task to the encoder. The encoder may generate a new hash key assigned to the new customer request and determine whether any existing hash key corresponds with the new hash key. If so, the system may associate the new batch of input observations with the corresponding hash key and update the corresponding hash key such that it is also configured to provide access to the new batch of input observations. If not, the system may generate a new stored task and assign the new hash key to it.

Abstract: Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

Abstract: Techniques for bi-modal learning in a financial context are described. These techniques are configured to improve a user's financial acumen and bring the user into an optimal financial state. Some of these techniques are embodied in a device that operates financial education lessons specifically configured for the improving the user's current financial state. These techniques may implement rewards/penalties (in tokens) for correct/incorrect user responses to financial decisions being presented in these lessons for user to make. By exploiting the user's desire for rewards and tokens and the desire to improve the user's current financial state, these techniques may leverage machine learning techniques to identify an appropriate financial education lesson that is most likely to have a positive effect on the user. Over time, administrating the financial education lessons builds customer loyalty to the device that implements these techniques. Other embodiments are described and claimed.

Abstract: Techniques for bi-modal learning in a financial context are described. These techniques are configured to improve a user's financial acumen and bring the user into an optimal financial state. Some of these techniques are embodied in a device that operates financial education lessons specifically configured for the improving the user's current financial state. These techniques may implement rewards/penalties (in tokens) for correct/incorrect user responses to financial decisions being presented in these lessons for user to make. By exploiting the user's desire for rewards and tokens and the desire to improve the user's current financial state, these techniques may leverage machine learning techniques to identify an appropriate financial education lesson that is most likely to have a positive effect on the user. Over time, administrating the financial education lessons builds customer loyalty to the device that implements these techniques. Other embodiments are described and claimed.

Abstract: Systems, methods, and articles of manufacture for learning design policies based on user interactions. One example includes determining a first task for an environment, receiving data from a plurality of data sources, determining a first time step associated with the received data, determining a plurality of candidate actions for the determined first time step, computing a respective probability value of each candidate action achieving the first task at the first time step based on a first machine learning (ML) model, determining that a first candidate action has a greater probability value for achieving the first task at the first time step relative to the remaining plurality of candidate actions, determining that the first candidate action has not been implemented in the environment at the first time step, and generating an indication specifying to implement the first candidate action as part of a policy to achieve the first task.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method. A method can include receiving observations and a corresponding class label, determining a candidate key based on the observations, determining a current memory state of a memory module based on a similarity of stored keys to the candidate key, generating a measurement vector based on the current memory state, concatenating the candidate key and measurement vector to form a state vector, determining, based on the state vector and an action distribution policy, an action of a plurality of actions such that the determined action maximizes an expected reduction in entropy as compared to the remaining actions of the plurality actions, executing the determined action, determining a value of the determined action, and updated, based on the value, the action distribution policy.

Abstract: Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

Abstract: Systems, methods, and articles of manufacture to generate, by a neural network of a variational autoencoder, a latent vector for a first input image, generate, by the neural network of the variational autoencoder, a first reconstructed image by sampling the latent vector for the first input image, determine a reconstruction loss incurred in generating the first reconstructed image based at least in part on: (i) a difference of the first input image and the first reconstructed image, and (ii) a master model trained to detect a sensitive attribute in images, determine a total loss based at least in part on the reconstruction loss and a classification loss, and optimize a plurality of weights of the neural network of the variational autoencoder based on a backpropagation operation and the determined total loss, the optimized neural network trained to not consider the sensitive attribute in images.

Abstract: A system for using hash keys to preserve privacy across multiple tasks is disclosed. The system may provide training batch(es) of input observations each having a customer request and stored task to an encoder, and assign a hash key(s) to each of the stored tasks. The system may provide a new batch of input observations with a new customer request and new task to the encoder. The encoder may generate a new hash key assigned to the new customer request and determine whether any existing hash key corresponds with the new hash key. If so, the system may associate the new batch of input observations with the corresponding hash key and update the corresponding hash key such that it is also configured to provide access to the new batch of input observations. If not, the system may generate a new stored task and assign the new hash key to it.

Abstract: Systems, methods, and articles of manufacture to generate, by a neural network of a variational autoencoder, a latent vector for a first input image, generate, by the neural network of the variational autoencoder, a first reconstructed image by sampling the latent vector for the first input image, determine a reconstruction loss incurred in generating the first reconstructed image based at least in part on: (i) a difference of the first input image and the first reconstructed image, and (ii) a master model trained to detect a sensitive attribute in images, determine a total loss based at least in part on the reconstruction loss and a classification loss, and optimize a plurality of weights of the neural network of the variational autoencoder based on a backpropagation operation and the determined total loss, the optimized neural network trained to not consider the sensitive attribute in images.

Abstract: Embodiments of the invention relate to managing access to media files and content therein. In one embodiment, a first identifier representing a first media content component in a first set of media content components is identified. The first set of media content components is included within a first media file that has been received from a first source. The identifier is compared with at least a second identifier representing at least a second media content component in a second set of media content components. The second set of media content components is associated with at least a second media file received from a second source. The first source is different than the second source. Responsive to the first digital signature substantially matching the second identifier, the first media content component is replaced with the second media content component.