Abstract: An artificial intelligence (AI) model includes one or more feature models coupled to one or more observer models in a hierarchical fashion. The feature models are configured to process an input to detect different features within that input. The observer models are configured to analyze the operation of the feature models during processing of the input to generate various types of observations. One type of observation includes a natural language expression that conveys how various architectural and/or functional characteristics of a given feature model influence the processing of the input to detect features, thereby exposing the underlying mechanisms via which the given feature model operates.

Abstract: One embodiment of the present invention sets forth a technique for creating a machine learning model. The technique includes generating a user interface comprising one or more components for visually generating the machine learning model. The technique also includes modifying source code specifying a plurality of mathematical expressions that define the machine learning model based on user input received through the user interface. The technique further includes compiling the source code into compiled code that, when executed, causes one or more parameters of the machine learning model to be learned during training of the machine learning model.

Abstract: As described, an artificial intelligence (AI) design application exposes various tools to a user for generating, analyzing, evaluating, and describing neural networks. The AI design application includes a network generator that generates and/or updates program code that defines a neural network based on user interactions with a graphical depiction of the network architecture. The AI design application also includes a network analyzer that analyzes the behavior of the neural network at the layer level, neuron level, and weight level in response to test inputs. The AI design application further includes a network evaluator that performs a comprehensive evaluation of the neural network across a range of sample of training data. Finally, the AI design application includes a network descriptor that articulates the behavior of the neural network in natural language and constrains that behavior according to a set of rules.

Abstract: As described, an artificial intelligence (AI) design application exposes various tools to a user for generating, analyzing, evaluating, and describing neural networks. The AI design application includes a network generator that generates and/or updates program code that defines a neural network based on user interactions with a graphical depiction of the network architecture. The AI design application also includes a network analyzer that analyzes the behavior of the neural network at the layer level, neuron level, and weight level in response to test inputs. The AI design application further includes a network evaluator that performs a comprehensive evaluation of the neural network across a range of sample of training data. Finally, the AI design application includes a network descriptor that articulates the behavior of the neural network in natural language and constrains that behavior according to a set of rules.

Abstract: An artificial intelligence (Al) design application that exposes various tools to a user for generating, analyzing, evaluating, and describing neural networks. The AI design application includes a network generator that generates and/or updates program code that defines a neural network based on user interactions with a graphical depiction of the network architecture. The network generator enables a developer to define the neural network architecture using a pipeline of mathematical expressions that can be directly compiled without the need of a complex software stack. The compilation process allows for the variables to be learned during the training process to be left unassigned when the neural network is instantiated. In particular, the compiler identifies such unassigned variables as variables having values that will be determined during the training process.

Abstract: As described, an artificial intelligence (AI) design application exposes various tools to a user for generating, analyzing, evaluating, and describing neural networks. The AI design application includes a network generator that generates and/or updates program code that defines a neural network based on user interactions with a graphical depiction of the network architecture. The AI design application also includes a network analyzer that analyzes the behavior of the neural network at the layer level, neuron level, and weight level in response to test inputs. The AI design application further includes a network evaluator that performs a comprehensive evaluation of the neural network across a range of sample of training data. Finally, the AI design application includes a network descriptor that articulates the behavior of the neural network in natural language and constrains that behavior according to a set of rules.

Abstract: As described, an artificial intelligence (AI) design application exposes various tools to a user for generating, analyzing, evaluating, and describing neural networks. The AI design application includes a network generator that generates and/or updates program code that defines a neural network based on user interactions with a graphical depiction of the network architecture. The AI design application also includes a network analyzer that analyzes the behavior of the neural network at the layer level, neuron level, and weight level in response to test inputs. The AI design application further includes a network evaluator that performs a comprehensive evaluation of the neural network across a range of sample of training data. Finally, the AI design application includes a network descriptor that articulates the behavior of the neural network in natural language and constrains that behavior according to a set of rules.