Systems and methods for adaptive data processing associated with complex dynamics are provided. The method may include applying the two or more predictive algorithms or rule-sets to an atomized model to generate applied data models. After receipt of inputs, the method may further include processing at least two propositions during a learning mode based upon detection of an absolute pattern within the applied data models; wherein propositions are action proposals associated with each predictive algorithm. At least two propositions may compete against each other through the use of an associated rating cell, which may be updated based upon the detected patterns. The method may further include processing propositions during an execution mode based upon detection of an absolute condition, wherein the rating cells are updated based upon these detected conditions. Further, these updated rating cells may be provided as feedback to update the atomized model.

Example implementations described herein are directed to a novel Automated Machine Learning (AutoML) framework that is generated on an AutoML library so as to facilitate functionality to incorporate multiple machine learning model libraries within the same framework through a solution configuration file. The example implementations further involve a solution generator that identifies solution candidates and parameters for machine learning models to be applied to a dataset specified by the solution configuration file.

A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.

Aspects of the disclosure relate to design as a service for configuring custom software. A computing platform may receive natural language input from a user specifying a software customization request. The computing platform may convert the natural language input into a visual output corresponding to the software customization request. The computing platform may send the visual output to a user interface. The computing platform may receive a modification request from the user specifying, using natural language, one or more modifications to the visual output. The computing platform may modify, using natural language processing, the visual output based on the modification request. The computing platform may log the one or more modifications to the visual output in a tracking log. The computing platform may send the modified visual output to the user interface.

Information for a knowledge graph is accessed. The knowledge graph has nodes and edges of a network and has information about security incident(s) in the network. Related entities from the knowledge graph are grouped together, where the related entities that are grouped together are determined not only by types of the entities, but also by threat(s) impacting the entities. The threat(s) correspond to the security incident(s). The grouped related entities are arranged in visualization data in order that the visualization data are configured to provide a visualization of the knowledge graph with the grouped related entities. The visualization data are output. Methods, apparatus, and computer program products are disclosed.

Embodiments are directed to a method of performing autonomous learning for updating input features used for an artificial intelligence model, the method comprising receiving updated data of an information space that includes a graph of nodes having a defined topology, the updated data including historical data of requests to the artificial intelligence model and output results associated with the requests, wherein different categories of input data corresponds to different input nodes of the graph. The method may further comprise updating edge connections between the nodes of the graph by performing path optimizations that each use a set of agents to explore the information space over cycles to reduce a cost function, each connection including a strength value, wherein during each path optimization, path information is shared between the rest of agents at each cycle for determining a next position value for each of the set of agents in the graph.

An artificial intelligence (AI) system for simulating functions such as recognition, determination, and so forth of a human brain by using a mechanical learning algorithm such as deep learning, or the like, and an application thereof are provided. A method of filtering video by a device is provided. The method includes selecting at least one previous frame preceding a current frame being played from among a plurality of frames included in the video, generating metadata regarding the selected at least one previous frame, predicting harmfulness of at least one next frame to be displayed on the device after playback of the current frame, based on the generated metadata, and filtering the next frame based on the predicted harmfulness.

In one embodiment, a device detects a particular activity from sensor data generated by one or more sensors in a sensor network. The device identifies, using a semantic reasoning engine, relevant preceding activities to the particular activity that are relevant to the particular activity. The device makes, using the semantic reasoning engine, an inference about the relevant preceding activities and the particular activity. The device provides an activity timeline for display that indicates the particular activity, the relevant preceding activities, and the inference about the relevant preceding activities and the particular activity.

Dialog system training techniques using a simulated user system are described. In one example, a simulated user system supports multiple agents. The dialog system, for instance, may be configured for use with an application (e.g., digital image editing application). The simulated user system may therefore simulate user actions involving both the application and the dialog system which may be used to train the dialog system. Additionally, the simulated user system is not limited to simulation of user interactions by a single input mode (e.g., natural language inputs), but also supports multimodal inputs. Further, the simulated user system may also support use of multiple goals within a single dialog session

One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.