Abstract: A general prediction model is based on an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is completely agnostic about any particular task being solved; it cares only about measurement locations and their values. A machine learning framework in which seemingly unrelated tasks can be solved by a single model is proposed, whereby input and output variables are embedded into a shared space. The approach is shown to (1) recover intuitive locations of variables in space and time, (2) exploit regularities across related datasets with completely disjoint input and output spaces, and (3) exploit regularities across seemingly unrelated tasks, outperforming task-specific single-task models and multi-task learning alternatives.

Abstract: An artificial intelligence (AI) prediction engine is used to correctly classify an entity based on a predetermined classification taxonomy, e.g., NAICS. The engine and process for using takes as inputs an entity's social presence (e.g., name, web address, etc.) and address. The AI prediction engine employs various machine learning models to make a classification prediction.

Abstract: An error detection framework, RED (Residual-based Error Detection), produces reliable confidence scores for detecting misclassification errors. RED calibrates the classifier's inherent confidence indicators and estimates uncertainty of the calibrated confidence scores using Gaussian Processes.

Abstract: Roughly described, anomalous behavior of a machine-learned computer-implemented individual can be detected while operating in a production environment. A population of individuals is represented in a computer storage medium, each individual identifying actions to assert in dependence upon input data. As part of machine learning, the individuals are tested against samples of training data and the actions they assert are recorded in a behavior repository. The behavior of an individual is characterized from the observations recorded during training. In a production environment, the individuals are operated by applying production input data, and the production behavior of the individual is observed and compared to the behavior of the individual represented in the behavior repository. A determination is made from the comparison of whether the individual's production behavior during operation is anomalous.

Abstract: Systems and processes for facilitating the sharing of models trained on a data set confined within a given firewall, i.e., a hidden data set, along with the model's performance metrics are described. The trained models may be used in further processes to improve the trained models to solve a predetermined problem or make a prediction.

Abstract: Roughly described, in an evolutionary technique for finding optimal solutions to a provided problem, a computer system uses a grouping algorithm that is better able to find diverse and optimum solutions in data mining environment with multiple solution landscapes and a plurality of candidate individuals. Each candidate individual identifies with a potential solution, and is associated with a testing experience level and one or more partition tags. Each candidate individual is assigned into one of a plurality of competition groups in dependence upon the individual's testing experience level and partition tag. During competition among candidate individuals, a candidate individual can only replace another candidate individual if both the candidate individuals have a common partition tag and are in the same competition group. A candidate individual cannot replace another candidate individual if they have different partition tags or are in different competition groups.

Abstract: In many environments, rules are trained on historical data to predict an outcome likely to be associated with new data. Described is a ruleset which predicts the probability of a particular outcome. Roughly described, an individual identifies a ruleset, where each of the rules has a plurality of conditions and also indicates a rule-level probability of a predetermined classification. The conditions indicate a relationship (e.g., ‘<’ or ‘!<’) between an input feature and a corresponding value. The rules are evaluated against input data to derive a certainty for each condition, and aggregated to a rule-level certainty. The rule probabilities are combined using the rule-level certainty values to derive a probability output for the ruleset, which can be used to provide a basis for decisions. In an embodiment, the per-condition certainty values are fuzzy values aggregated by fuzzy logic. A novel genetic algorithm can be used to derive the ruleset.

Abstract: Roughly described, a computer-implemented evolutionary system evolves candidate solutions to provided problems. It includes a memory storing a candidate gene database containing active and epigenetic individuals; a gene pool processor which tests only active individuals on training data and updates their fitness estimates; a competition module which selects active individuals for discarding in dependence upon both their updated fitness estimate and their testing experience level; and a gene harvesting module providing for deployment selected ones of the individuals from the gene pool. The gene database has an experience layered elitist pool, and individuals compete only with other individuals in their same layer. Certain individuals are made epigenetic in the procreation module, after which they are not subjected to testing and competition. Epigenetic individuals are retained in the candidate gene pool regardless of their fitness.

Abstract: The embodiments describe a technique for customizing activation functions automatically, resulting in reliable improvements in performance of deep learning networks. Evolutionary search is used to discover the general form of the function, and gradient descent to optimize its parameters for different parts of the network and over the learning process. The new approach discovers new parametric activation functions which improve performance over previous activation functions by utilizing a flexible search space that can represent activation functions in an arbitrary computation graph. In this manner, the activation functions are customized to both time and space for a given neural network architecture.

Abstract: The technology disclosed relates to evolving deep neural network structures. A deep neural network structure includes a plurality of modules with submodules and interconnections among the modules and the submodules. In particular, the technology disclosed relates to storing candidate genomes that identify respective values for a plurality of hyperparameters of a candidate genome. The hyperparameters include global topology hyperparameters, global operational hyperparameters, local topology hyperparameters, and local operational hyperparameters. It further includes evolving the hyperparameters by training, evaluating, and procreating the candidate genomes and corresponding modules and submodules.

Abstract: The technology disclosed relates to evolving a deep neural network based solution to a provided problem. In particular, it relates to providing an improved cooperative evolution technique for deep neural network structures. It includes creating blueprint structures that include a plurality of supermodule structures. The supermodule structures include a plurality of modules. The modules are neural networks. A first loop of evolution executes at the blueprint level. A second loop of evolution executes at the supermodule level. Further, multiple mini-loops of evolution execute at each of the subpopulations of the supermodules. The first loop, the second loop, and the mini-loops execute in parallel.

Abstract: Roughly described, a computer system uses a behavior-driven algorithm that is better able to find optimum solutions to a problem by balancing the use of fitness and novelty measures in evolutionary optimization. In competition among candidate individuals, a domination estimate between a pair of individuals is determined by both their fitness estimate difference and their behavior difference relative to one another. An increase in the fitness estimate difference of one individual of the pair over the other increases the domination estimate of the first individual. An increase in the behavior distance between the pair of individuals decreases the domination estimate of both of the individuals. Individuals with a higher domination estimate are more likely to survive competitions among the candidate individuals.

Abstract: The technology disclosed identifies parallel ordering of shared layers as a common assumption underlying existing deep multitask learning (MTL) approaches. This assumption restricts the kinds of shared structure that can be learned between tasks. The technology disclosed demonstrates how direct approaches to removing this assumption can ease the integration of information across plentiful and diverse tasks. The technology disclosed introduces soft ordering as a method for learning how to apply layers in different ways at different depths for different tasks, while simultaneously learning the layers themselves. Soft ordering outperforms parallel ordering methods as well as single-task learning across a suite of domains. Results show that deep MTL can be improved while generating a compact set of multipurpose functional primitives, thus aligning more closely with our understanding of complex real-world processes.

Abstract: The present invention relates to an ESP decision optimization system for epidemiological modeling. ESP based modeling approach is used to predict how non-pharmaceutical interventions (NPIs) affect a given pandemic, and then automatically discover effective NPI strategies as control measures. The ESP decision optimization system comprises of a data-driven predictor, a supervised machine learning model, trained with historical data on how given actions in given contexts led to specific outcomes. The Predictor is then used as a surrogate in order to evolve prescriptor, i.e. neural networks that implement decision policies (i.e. NPIs) resulting in best possible outcomes. Using the data-driven LSTM model as the Predictor, a Prescriptor is evolved in a multi-objective setting to minimize the pandemic impact.

Abstract: An explainable surrogate-assisted evolutionary optimization method, E-ESP, discovers rule-based decision strategies for which actions to take to achieve certain outcomes when historical training data is limited or unavailable. The resulting rules are human readable and thus facilitate explainability and trustworthiness unlike the black box solutions resulting from neural network solutions.

Abstract: A heat transfer process on to a stainless steel cup with a dark surface involves a stainless steel dark surface treatment layer arranged on a base layer of the outer circular surface of the cup body. The stainless steel dark surface treatment layer is sequentially arranged as at least one wire screen printing layer, a resin layer, and a thermal transfer image layer. The is one or several screen printing layers on the dark rough surface to fill up the rough pits on the dark surface to form a smooth surface and then a thermal transfer resin coating is applied, followed by heat transfer printing. A colorful personalized product can thus be produced from the cup.

Abstract: A system and method for evolving a recurrent neural network (RNN) that solves a provided problem includes: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes representing a neural network as a unique tree structure; an assembly module that assembles N RNN layers; an evolution module that evolves the H candidate RNN nodes of each respective RNN layer; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that evaluates a performance of each candidate RNN node of each RNN layer using validation data and assigns a fitness value to each candidate RNN node; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.

Abstract: A system and process for generalizing an evolutionary process applied to a particular domain involving different problems includes a researcher module for generating a configuration specification applicable to a particular problem. An evolution module parses the configuration specification into a representative tree structure, assembles policies for each node in the tree structure, and generates candidate genomes using the policies for each node in the tree structure. The policies may be applied to new data or data from prior runs to generate candidate genomes. The evolution module translates internal representations of the generated candidate genomes into known representations of the candidate genome for evaluation in accordance with the particular domain parameters by a candidate evaluation module.

Abstract: Described is a blower system. The blower system includes one or more blowers. Each blower has an intake for providing air into an air chamber which houses an impeller. A gate covers each intake. Each gate is movable between a closed position in which it is pulled against the intake and an open position in which the gate is lifted off of the intake. A controller system is included that is operable for causing the gates to move from the closed to the open position when a vehicle is present or approaching and cause the gates to return to the closed position when no vehicle is present or leaving.

Abstract: A tundish with improved flow characteristics for molten metal has an outlet in its base. The outlet is spaced longitudinally in the tundish from a pour zone. The pour zone is positioned to receive a stream of molten steel from a ladle. The outlet is provided with a refractory barrier at its upper end. A portion of the floor of the tundish circumferential to the outlet is provided with a refractory structure having an interior free volume. Structures within the tundish, such as a dam extending upwardly from the tundish floor between the pour zone and the outlet, or a well in the tundish floor surrounding the outlet, may be used to affect the flow of molten metal in the tundish.