6 results about "Turing machine" patented technology

The invention relates to the technical field of data processing, in particular to a large model reasoning optimization method based on context incremental updating, which comprises the following steps: processing a multi-modal data stream through timestamp alignment and a filtering algorithm, extracting features by adopting a shared encoder and a private encoder, and realizing feature decoupling through a depth information bottleneck principle. A dynamic emotion map is constructed by using Gaussian process regression and a random process algorithm, and self-adaptive updating control is realized by combining meta-learning and Bayesian optimization. Incremental state management is realized by adopting a neural Turing machine, reasoning consistency is guaranteed through a generative adversarial network, model parameters are optimized in combination with a digital twin system and reinforcement learning, and a mental health service response is finally generated through a conditional generation model and hierarchical reinforcement learning. According to the method, the problem of asynchronism of multi-modal emotion feature dynamic evolution and context increment updating is effectively solved, accumulated drift of emotion state tracking is eliminated, and the continuity of reasoning logic is guaranteed.

A computer-implemented method, according to one approach, includes: causing a multi-layer neural network to evaluate a user query received from an endpoint device by processing the user query with one or more initial layers of the neural network. In response to receiving an output from the initial layers at a supplemental threat detection layer of the neural network, the user query processed with the threat detection layer. Processing the user query with the threat detection layer includes using a classifier to compare the user query to activation data outlining known illegitimate queries. Moreover, combinational reasoning is used to determine whether the user query is legitimate based at least in part on: an output of the classifier, and behavioral information received from a threat intel pattern. The user query is further intentionally rejected in response to the threat detection layer determining the user query is not legitimate.

The invention provides an intelligent question and answer method and system, and the method comprises the steps: carrying out the semantic search of a user question, obtaining a semantic search result, carrying out the preprocessing of the semantic search result and a PDF text collected by a system, obtaining target data, and finally, carrying out the processing of the target data. Processing the target data by utilizing an intelligent energy Turing machine combining algorithm control and intelligent energy collection to obtain reply information aiming at the user question; the purpose of quickly and accurately answering the questions of the user is achieved, and the user experience is greatly improved.

The invention discloses a digital teacher knowledge modeling and content generation method based on artificial intelligence. The method comprises the following steps: constructing a teaching knowledge graph and forming a capability graph; generating a node vector, an edge vector and a path mode set; inputting the node vector, the edge vector and the path mode set into a Hopfield network, and executing pre-training; outputting a corresponding teaching knowledge path sequence and an association weight thereof based on teaching task information extracted from the teaching knowledge graph and the capability graph; generating a teaching content organization plan; according to the teaching content organization plan, generating structured teaching content including teaching explanation text, teaching example text, interactive practice tasks and evaluation question content; and performing increment adjustment on the Hopfield network memory state and the external memory of the neural Turing machine. The method integrates knowledge modeling, intelligent memory and content generation capabilities, and is suitable for intelligent education scenes.

The application relates to a calculation method and system based on constrained network minimum action evolution, belonging to the cross field of computer science and computational physics. The technical problem to be solved by the application is to overcome the limitation that the existing calculation paradigm completely depends on instruction sequence execution. The technical solution of the application is as follows: receiving constraint declaration information input by a user, including calculation element declaration, partial order relation declaration and action contribution item declaration; constructing an initial coupling network and initializing coupling strength according to a chaotic rule; under the constraint of the partial order relation, optimizing the coupling network through an iterative search method, searching for a target network configuration which makes the total action approach minimization; and performing coarse-grained observation on the target network configuration and outputting an emergent result. The application breaks through the existing Turing machine calculation paradigm, for the first time, takes the minimum action principle as the core driving force of calculation, replaces the instruction sequence with the partial order relation as the sequential constraint of a program, makes the program automatically evolve into a macro result from the constraint, realizes the fundamental innovation of the calculation paradigm, and can be applied to the fields of physical simulation, material design, cosmology research, new type of calculation hardware design and the like.

The invention discloses a character processing method and device based on novel artificial intelligence, and relates to the technical field of character processing of artificial intelligence, the method can correct grammar and character errors of a user under the requirement of the user, provide sentence pattern rewriting suggestions, and can improve the character processing efficiency by understanding chapter writing intentions and summarizing the expression advantages of an original text. Defects and defects existing in character expression are pointed out, and corresponding improvement suggestions are given at the same time. And according to the intelligent energy Turing machine and corresponding improvement suggestions, carrying out defect checking and defect repairing on the optimized and rewritten text content so as to supplement missing key information of the optimized and rewritten text content, improve the rationality of a text structure and improve language coherence. By performing multiple rounds of correction and optimization on the text contents and expressions, a user is helped to more accurately and smoothly reflect deep context understanding of the text contents and expression intentions of chapters of the text in a text correction process.