Abstract: In some examples, special-purpose machines are provided that facilitate smart copy optimization in a network service or publication system, including software-configured computerized variants of such special-purpose machines and improvements to such variants, and to the technologies by which such special-purpose machines become improved compared to other special-purpose machines that facilitate adding the new features. Such technologies can include special artificial-intelligence (AI), machine-learning (ML), and natural-language-processing (NLP) techniques.

Abstract: Methods and systems are described herein for facilitating segmentation of training data using measures of statistical dispersion (e.g., Gini impurities) of dataset features. The system determines, from a training dataset, a target feature and candidate features. The system determines, for the target feature in relation to each candidate feature, first Gini impurities. The system selects a first and second feature having the lowest first Gini impurities. The system determines, for the target feature in relation to a first combination of the first and second features, a second Gini impurity. If the second Gini impurity does not satisfy a threshold, the system selects a third feature having the next lowest first Gini impurity and determines a third Gini impurity for a second combination of the first, second, and third features. If the third Gini impurity satisfies the threshold, the system trains a model using the target, first, second, and third features.

Abstract: The systems and methods disclosed herein generate responses using data retrieved in accordance with chunk-level access controls. An output generation request is received via a computing device and includes (1) an input with instructions to generate an output and (2) an access control metadata set indicating the degree of access to a content set within a vector database for the user associated with the request. A vector representation set of data chunks that are associated with generating the output is selected by comparing the vector representation of the input with corresponding vector representations of data chunks in the content set. Using a first artificial intelligence (AI) model set, the data chunk set is filtered to generate a subset in accordance with the access control metadata set. A second AI model set (same or different) is used to generate a response to the input based on the data chunk subset.

Abstract: Methods, systems, and apparatus for gradient-based quantum assisted Hamiltonian learning. In one aspect, a method includes obtaining, by a classical processor, multiple experimental data points, wherein each experimental data point is generated according to a Hamiltonian comprising parameters with unknown values; learning, by the classical processor, values of the parameters, comprising iteratively adjusting, by the classical processor and until predetermined completion criteria are met, estimated values of the parameters to minimize a cost function, wherein the cost function is dependent on the multiple experimental data points and at each iteration derivatives of the cost function with respect to respective estimated values of the parameters for the previous iteration are computed using a quantum computer.

Abstract: Aspects of the present disclosure relate to optimized generative machine learning systems. Embodiments include a generative machine learning model that comprises multiple sets of hidden decoder layers. In certain embodiments, a first set of decoder layers having a relatively small number of synaptic weights is used to generate reasoning steps for the model. The second set of decoder layers may have a larger number of synaptic weights than the first set, and the second set may be used to generate tokens of the response based on the reasoning steps. In some embodiments, each set of decoder layers comprise a plurality of neurons organized in an array, wherein each neuron comprises a register, a microprocessor, and at least one input. The neurons may be connected using synaptic circuitry.

Abstract: Systems and methods for monitoring and assessing crop health and performance can provide rapid screening of individual plants. The systems and methods have an automated component, and rely primarily on the detection and interpretation of plant-based signals to provide information about crop health. In some cases knowledge from human experts is captured and integrated into the automated crop monitoring systems and methods. Predictive models can also be developed and used to predict future health of plants in a crop.

Abstract: An interactive multimedia with multiple levels and a method for designing the interactive multimedia are provided. The interactive multimedia includes: a first level program, providing at least one first level interactive activity to determine one of multiple lines for the user to proceed from a first level to a next level; and a second level program, providing at least one second level interactive activity to determine one of multiple lines for the user to proceed from the second level to a next level. The levels and the lines of the interactive multimedia are designed at least partially based on a decision tree generated by a machine learning technique with a source data including predictor variables indicating attributes of respondents and a target variable indicating related outcomes of the respondents.

Abstract: A method and a system for improving classification of data samples, which may be considered as class outliers, are claimed. The method includes inferring a pretrained classifying ML-based model on the incoming data sample, to assign a particular class of a plurality of classes thereto; calculating a similarity metric value representing a degree of similarity between the incoming data sample and one or more previously classified data samples of the particular class; and validating assignment of the particular class to the incoming data sample, based on the calculated similarity metric value.

Abstract: Disclosed herein is a computing platform configured to (i) for a deep-learning AI model, determine a respective fairness-importance score of a respective parameter for at least a subset of the deep-learning AI model's parameters that quantifies how much the respective parameter influences generating fair predictions across a plurality of demographic groups, (ii) carry out an optimization process that produces and evaluates different quantized versions of the deep-learning AI model, (iii) based on the optimization process, select a given quantized version of the deep-learning AI model for deployment, (iv) fine-tune the given quantized version of the deep-learning AI model, and after fine-tuning the given quantized version of the deep-learning AI model, deploying the given quantized version of the deep-learning AI model.

Abstract: A method of forming an anomaly detection monitor includes obtaining data samples of operations performed on an application by a plurality of users and detecting, by a processor, anomalous behavior associated with a target user of the plurality of users with respect to the application based on a portion of the data samples associated with the target user and a portion of the data samples associated with a second user of the plurality of users, different from the target user.

Abstract: A student model may be trained in two stages by using two teacher models, respectively. The first teacher model has been trained with a pretraining dataset. The second teacher model has been trained with a training dataset that is specific to a task to be performed by the student model. In the first stage, the student model may be generated based on a structure of the first teacher model. Internal parameters of the student model are adjusted through a pretraining process based on the first teacher model and the pretraining dataset. Weights of the student model may be pruned during the pretraining process. In the second stage, a sparsity mask is generated for the student model to lock the sparsity pattern generated from the first stage. Further, some of the internal parameters of the student model are modified based on the second teacher model and the training dataset.

Abstract: An apparatus and method for efficiently creating less computationally intensive nodes for a neural network. In various implementations, a computing system includes a memory that stores multiple input data values for training a neural network, and a processor. Rather than determine a bit width P of an integer accumulator of a node of the neural network based on bit widths of the input data values and corresponding weight values, the processor selects the bit width P during training. The processor adjusts the magnitudes of the weight values during iterative stages of training the node such that an L1 norm value of the weight values of the node does not exceed a corresponding weight magnitude limit.

Abstract: Generally discussed herein are devices, systems, and methods for machine learning (ML) modeling of a system that operates on a multivector object. A method includes receiving, by an ML model, the multivector object as an input that represents a state of the multivector system. The method includes operating, by the ML model and using a Clifford layer that includes neurons that implement a multivector kernel, on the multivector input to generate a multivector output that represents the state of the multivector system responsive to the multivector input.

Abstract: A neural network system and an operation method for a neural network system are provided. The neural network system includes at least one edge device and a server. Each edge device stores a neural network architecture. The neural network architecture includes at least one operator and a model identifier, and the at least one operator of the neural network architecture stored in the each edge device includes an operator identifier. The server is connected to the each edge device. The each edge device is configured to, upon being powered on, transmit the operator identifier of each operator to the server to request the server to return parameters for the each operator; receive the parameters of the each operator and combine the parameters of the each operator with the neural network architecture to obtain a neural network model; and execute a predetermined task based on the neural network model.

Abstract: A method for automated therapy discovery includes: accessing a corpus of scientific publications; compiling a population of semantic concepts from the corpus of scientific publications into a vector space model; deriving domains of concepts in the vector space model based on proximity to domain descriptors in the vector space model; deriving association scores and action characteristics between connected concepts, based on proximity and action descriptors in the vector space model; generating a semantic network; receiving a query for a target concept and a target domain at a research portal; isolating a set of edges between a target node and a subset of nodes; identifying subsets of concepts along the set of edges; generating hypotheses for directions and magnitudes of effects of subsets of concepts on the target concept based on association scores and action characteristics stored in connections along the set of edges; and returning hypotheses to the research portal.

Abstract: Disclosed are a method and an apparatus for real-time data monitoring based on machine learning, the method including: training a multi-layer predictor on actual values of historical indicator data, each layer of the multi-layer predictor including a plurality of predictors of different types; outputting predicted values of future indicator data by inputting a future time period for prediction into the trained multi-layer predictor; calculating alarm thresholds from the predicted values of the future indicator data and historical prediction errors; and triggering an alarm when an actual value of the future indicator data exceeds the corresponding alarm threshold. The accuracy of the alarm thresholds can be improved, and the alarm thresholds can be well adapted to the constantly changing indicator data. There is no need to manually configure a fixed alarm threshold, the accuracy of the alarm can be ensured, and the number of missed and false alarms can be reduced.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating representations of input sequences. One of the methods includes obtaining an input sequence, the input sequence comprising a plurality of inputs arranged according to an input order; processing the input sequence using a first long short term memory (LSTM) neural network to convert the input sequence into an alternative representation for the input sequence; and processing the alternative representation for the input sequence using a second LSTM neural network to generate a target sequence for the input sequence, the target sequence comprising a plurality of outputs arranged according to an output order.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over radio frequency (RF) channels. One method includes: determining an encoder and a decoder, at least one of which is configured to implement an encoding or decoding that is based on at least one of an encoder machine-learning network or a decoder machine-learning network that has been trained to encode or decode information over a communication channel; determining first information; using the encoder to process the first information and generate a first RF signal; transmitting, by at least one transmitter, the first RF signal through the communication channel; receiving, by at least one receiver, a second RF signal that represents the first RF signal altered by transmission through the communication channel; and using the decoder to process the second RF signal and generate second information as a reconstruction of the first information.

Abstract: According to one embodiment, a training device trains a first model. The first model estimates a period of a task from time-series data of an operation of a human. The device acquires first time-series data to which a label of the task is assigned. The device extracts a pattern from a period indicated by the label in the first time-series data. The pattern is used as a feature. The device generates timing data of an appearance timing of the pattern in the first time-series data. The device trains the first model by using the label, the first time-series data, and the timing data.

Abstract: Systems and methods for emulating a physical quantum system with a quantum computation. A model Hamiltonian that approximates a first quantization Hamiltonian of the physical quantum system is stored in memory. The physical system includes a plurality of particles. The first quantization Hamiltonian includes a plurality of first quantization energy operators, and the model Hamiltonian includes a plurality of energy terms corresponding to respective ones of the plurality of first quantization energy operators. Each energy term includes a respective energy operator, a respective energy register operator, and a respective inverse energy operator. The physical quantum system is emulated by performing a quantum computation on a plurality of qubits of the quantum computing system to emulate time evolution using the model Hamiltonian.