An upper-level integrated processing device recognizes an inquiry, generates a search request in a primitive form to be output to each of the individual AI search devices in response to the inquiry, receives an individual reply and the probability thereof corresponding to the search request in the primitive form from a plurality of individual AI search devices, normalizes the probabilities of the individual replies from the individual AI search devices to acquire normalized probabilities, and generates an inquiry reply corresponding to the individual replies and the normalized probabilities thereof, and the lower-level individual AI search device searches Individual AI database using artificial intelligence in response to reception of the search request in the primitive form, acquires an individual reply and the probability thereof, and outputs the same in the primitive form to the integrated processing device. The upper-level devices output reply using a plurality of AI search devices in combination.

Generating granular clusters for real-time processing is provided. The systems can identify tokens based on aggregating input from computing devices over a time interval. The systems can identify, based on metrics, a subset of tokens for cluster generation. The systems can generate, via a clustering technique, token clusters from the subset of the tokens, each of the token clusters comprising two or more tokens from the subset of the tokens. The systems can apply a de-duplication technique to each of the token clusters. The systems can apply a filtering technique to the token clusters to remove tokens erroneously grouped in a token cluster. The systems can assign, based on a selection process, a label for each of the token clusters. The systems can activate, based on a number of remaining tokens in each of the token clusters, a subset of the token clusters for real-time content selection.

A network quantization method is a network quantization method of quantizing a neural network, and includes a database construction step of constructing a statistical information database on tensors that are handled by neural network, a parameter generation step of generating quantized parameter sets by quantizing values included in each tensor in accordance with the statistical information database and the neural network, and a network construction step of constructing a quantized network by quantizing the neural network with use of the quantized parameter sets. The parameter generation step includes a quantization-type determination step of determining a quantization type for each of a plurality of layers that make up the neural network.

A distillation system extracts knowledge from a large pre-trained sequence-to-sequence neural transformer model into a smaller bi-encoder. The pre-trained sequence-to-sequence neural transformer model is trained to translate data from a first domain into a second domain on a large corpus. A teacher model is generated from the pre-trained model by fine-tuning the pre-trained neural transformer model on a smaller translation task with true translation pairs. The fine-tuned model is then used to generate augmented data values which are used with the true translation pairs to train the bi-encoder. The bi-encoder is used for perform cross-domain searches.

A method is disclosed for reducing computation of a differentiable architecture search. An output node is formed having a channel dimension that is one-fourth of a channel dimension of a normal cell of a neural network architecture by averaging channel outputs of intermediate nodes of the normal cell. The output node is preprocessed using a 1×1 convolution to form channels of input nodes for a next layer of the cells in the neural network architecture. Forming the output node includes forming s groups of channel outputs of the intermediate nodes by dividing the channel outputs of the intermediate nodes by a splitting parameter s. An average channel output for each group of channel outputs is formed, and the output node is formed by concatenating the average channel output for each group of channels with channel outputs of the intermediate nodes of the normal cell.

The present disclosure relates to a method and apparatus for training artificial intelligence based on an episodic memory. According to an embodiment of the present disclosure, a method for training artificial intelligence based on an episodic memory may include: constructing an episodic memory by using a feature vector of a training dataset stored in a full memory; obtaining output data by inputting query data into an artificial intelligence model; deriving a similarity between the output data and a feature vector in the constructed episodic memory; and deriving an episode loss function based on the similarity.

An apparatus including circuitry configured to determine a probability by combining at least: a probability that an event is present within a current feature of interest given a first set of previous features of interest, and a probability that the event is present within the current feature of interest given a second set of previous features of interest, different to the first set of previous features of interest; circuitry configured to detect the event based on the determined probability; and circuitry configured to control, in dependence on the detection of the event, performance of an action.

Aspects described herein include a method of conversational machine reading comprehension, as well as an associated system and computer program product. The method comprises receiving a plurality of questions relating to a context, and generating a sequence of context graphs. Each of the context graphs includes encoded representations of: (i) the context, (ii) a respective question of the plurality of questions, and (iii) a respective conversation history reflecting: (a) one or more previous questions relative to the respective question, and (b) one or more previous answers to the one or more previous questions. The method further comprises identifying, using at least one graph neural network, one or more temporal dependencies between adjacent context graphs of the sequence. The method further comprises predicting, based at least on the one or more temporal dependencies, an answer for a first question of the plurality of questions.

Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.

Kits, diagnostic systems and methods are provided, which measure the distribution of colors of skin features by comparison to calibrated colors which are co-imaged with the skin feature. The colors on the calibration template (calibrator) are selected to represent the expected range of feature colors under various illumination and capturing conditions. The calibrator may also comprise features with different forms and size for calibrating geometric parameters of the skin features in the captured images. Measurements may be enhanced by monitoring over time changes in the distribution of colors, by measuring two and three dimensional geometrical parameters of the skin feature and by associating the data with medical diagnostic parameters. Thus, simple means for skin diagnosis and monitoring are provided which simplify and improve current dermatologic diagnostic procedures.