2684results about "Computer simulations" patented technology

This application describes, among other things, systems and methods for deploying a task-specific machine-learning model. An example method includes receiving a prompt associated with one or more commands and a plurality of tokens. A first task-specific machine-learning model is identified based on a first command of the one or more commands. Some or all of the tokens of the plurality of tokens are applied to a node associated with the first task-specific machine-learning model, and a plurality of restricted data is received. Based on evaluating the plurality of restricted data, a correlation between the first node and a second node is determined. If the correlation satisfies a threshold condition, a plurality of text data different than the prompt is generated, otherwise the some or all of the tokens are again provided to the first node. Some or all of the tokens are then traversed and applied to the second node.

This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components selected to provide a response to the prompt, where each respective task-specific components in the set of task-specific components is configured to assist with a respective clinical task of the one or more clinical tasks. The method further includes, determining an order in which each respective task-specific components of the set of task-specific components should be utilized to prepare a complete response to the prompt that address the one or more clinical tasks based on the obtained orchestration data about the set of task-specific components. The method also includes, in accordance with the determined order, providing first data related to the prompt to a first task-specific component and receiving a first response from the first task-specific component.

Construct multiple retrieval augmented generation (RAG) databases for different related fields as needed to increase the recording capacity of additional information. By referring to the entire text or image of the original document page where relevant content is described based on the chunks obtained from RAG retrieval, eliminate reference omissions. At the same time, reflect the background information and related information that may be described around the retrieved chunks in the context of the question text. Databaseize the records of the question text and the answer text to make them searchable, and provide a large language model using retrieval augmented generation that eliminates the need for costly and time-consuming regenerating answers. 【Solution means】The RAG database includes a page image acquisition means, a page text database recording means, a related feature vector extraction means, and a feature vector database recording means.

Methods and systems for generating digital threads that connect models and software tools for an interconnected digital engineering and certification ecosystem are provided. In one embodiment, a process for generating a software-code-defined digital thread includes receiving a first model representation of a first engineering model, a second model representation of a second engineering model, and an intent input. Determining, using a machine learning (ML) model, one or more model endpoints in the first and the second model representations. Generating, using a script-generating ML model, a platform orchestration script that accomplishes the intent input by connecting the first model representation and the second model representation, using the one or more model endpoints. Finally, storing the platform orchestration script as the software-code-defined digital thread. The digital threading of digital engineering models, with and without AI assistance, leads to exponential increases in efficiency of designing, testing, certifying, manufacturing, and operating physical products, allowing physical products to be made at digital speeds.

This application describes, among other things, machine-learning models for performing specific clinical tasks. An example method includes receiving a prompt at a first computing system in communication with a machine-learning model trained to assist in performing a clinic task that includes generating a report of a patient's medical records, guiding a patient through a care plan, creating patient care guidelines based on a patient's health profile, identifying patients requiring follow-up at a hospital, identifying changes in a standard of care for a disease setting, or evaluating unstructured data associated with a patient to identify a cohort of similar patients. Based on the prompt, a natural language response is generated that is responsive to the prompt and is based on an analysis by the machine-learning model of a repository of data that is determined to be relevant to the prompt. And the natural language response is provided to second computing system.

Systems and methods are disclosed for implementing advanced statistical models and machine-learning algorithms to generate predictions. The method includes receiving a plurality of data from one or more sources; processing the plurality of data to select one or more relevant variables; training one or more prediction models based on the one or more relevant variables, and a combination of an advanced statistical model and a machine-learning model; evaluating performance of the one or more trained prediction models based on one or more validation techniques; and deploying at least one prediction model based on the performance for generating predictions.

This application describes, among other things, systems and methods for interacting with a task-specific orchestration. An example method includes obtaining medical data from one or more data collections. A set of user interface elements is presented at a user interface, where each respective user interface element of the set of user interface elements represents a respective task-specific orchestration that includes one or more respective machine-learning models fine-tuned for a specific task or domain based on the medical data. When a user interface element representing a respective task-specific orchestration is selected, at least some of the medical data is provided to the respective task-specific orchestration, and a different user interface is presented for communicating with the respective task-specific orchestration. Based on receiving a prompt at the different user interface, a response object is generated by the task-specific orchestration based on the prompt and at least some of the medical data.

An illustrative method includes determining that a first data instance stored within a first data store is replicated as a second data instance stored within a second data store, determining that a second retention policy associated with the second data instance stored within the second data store is at least as stringent as a first retention policy associated with the first data instance stored within the first data store, and modifying, based on the determining that the second retention policy is as least as stringent as the first retention policy, the first retention policy to allow for the first data instance to be modified or deleted without the first set of one or more conditions each being satisfied.

The invention provides a deep learning self-calibration system for high-precision color management, and relates to the technical field of color calibration, a system architecture comprises an algorithm layer, a system architecture layer and a data processing and optimizing layer, through fusion of multiple advanced algorithms in the algorithm layer, the precision of color correction is effectively improved, and the accuracy of color correction is improved. The high consistency of colors of printed matters in the whole production process is ensured, the quality stability of printed products is greatly improved, data processing is carried out through all modules of the data processing and optimizing layer, real-time data in the printing production process are efficiently processed, a model is dynamically updated, and the printing production efficiency is improved. The design of the system architecture layer provides a comprehensive adaptation scheme for large-scale production, multiple links and multiple equipment materials in the printing industry, the universality and flexibility of the system are enhanced, and the system is suitable for large-scale production, multiple links and multiple equipment materials in the printing industry. The time and labor cost for readjusting the system due to the change of equipment materials is reduced, and the production strain capacity of enterprises is improved.

This application describes, among other things, methods of selecting a task-specific machine-learning model for addressing a clinical task. An example method includes receiving a prompt from a user. Based on determining that the prompt requests assistance with a clinical task, a machine-learning model trained to select from among a plurality of task-specific machine-learning models each trained to assist with one of a plurality of clinical tasks selects a respective task-specific machine-learning model from among the plurality of task-specific machine-learning models based on the prompt. The prompt is provided to the selected task-specific machine-learning model. And a response received from the selected task-specific machine-learning model is provided to the user.

The invention relates to a high-efficiency image recognition system based on embedded edge equipment, which belongs to the technical field of computer application and comprises a hardware acceleration module, an image recognition module and an image recognition module, reasoning tasks are distributed to different cores and a model quantification module through dual-core cooperation, quantification converts high-precision floating-point number representation into low-precision integers through weight and activation in a deep learning model, an automatic deployment module automatically converts the trained deep learning model into a format suitable for an embedded hardware platform, and the embedded hardware platform is used for realizing real-time reasoning. The data processing module is used for preprocessing and enhancing input data and ensuring that the data meet the input requirements of the deep learning model, and the reasoning engine module is responsible for loading and operating the optimized deep learning model on embedded equipment. And the efficient and reliable image recognition function is realized while the calculation, storage and energy consumption limitation of the embedded equipment is met.

Techniques for optimizing and deploying deep neural network (CNN) machine learning models for inference using static analysis are described. A method includes obtaining a deep neural network (DNN) machine learning (ML) model, generating an intermediate representation for the ML model, the intermediate representation including one or more nodes corresponding to one or more operators utilized by the ML model, identifying, for at least one node of the intermediate representation, an optimized schedule for at least one operator corresponding to the at least one node using a static analysis that is based on a hardware-specific cost model, generating an optimized intermediate representation using the optimized schedule that is optimized for execution on a hardware platform, and generating code corresponding to the ML model based at least in part on the optimized intermediate representation, wherein the code is specific to the hardware platform.

The invention discloses an operator migration method and device between hardware platforms and a migration model construction method. The migration method comprises the following steps: obtaining an operator code and a function label of an operator to be migrated; when the existing operator information of the target hardware platform does not contain the function label, obtaining first information of the target hardware platform and second information of the source hardware platform; training a large language model based on the first information and the second information, and taking the trained large language model as an operator automatic migration development model; generating an initial operator matched with the precision requirement of the target hardware platform based on the operator automatic migration development model and the operator code; analyzing the initial operator to obtain an operator precision difference; taking an initial operator corresponding to the operator precision difference passing verification as a target operator; and storing the target operator to a corresponding level of the target hardware platform. According to the method, automatic non-inductive migration of the model operator on different hardware platforms can be realized, and efficient and seamless adaptation with different platforms is realized.

Provided in the present invention is a parameter-efficient large-language fine-tuning federated learning framework, comprising the following steps: performing modeling on LoRA adapters of different edge clouds; since different weights exhibit different average performances on the LoRA adapters, using singular values to quantify the importance of the weights, and therefore, before each round of independent training of the LoRA adapters using N edge clouds, using a matrix singular value to decompose a BA matrix in the LoRA adapter for each trainable weight; configuring heterogeneous LoRA adapters on the basis of the importance of the weights; and using different numbers of quantization bits to quantize a pre-trained model, and performing high-precision inverse quantization on the pre-trained model only when matrix multiplication is executed, wherein the pre-trained model is quantized to the maximum number of quantization bits on the basis of the memory budget of the edge clouds. The present invention has the following beneficial effects: the present invention determines the optimal fine-tuning model structure, thereby improving the performance of LLM fine-tuning, and adapts to heterogeneous and resource-constrained edge clouds.

Embodiments of the present disclosure are directed to onboarding a model from a training platform to an inference platform and selecting parameters of the model to optimize performance of the model. For example, the onboarding of the model to the inference platform can be based on a series of interactions between a model onboarding systems at the training platform and at the inference platform. An optimization process can include a searching-based process to derive optimal settings for the model. The optimization process can simulate feature combinations of the model and identify an optimal combination of settings of the model for increased model performance.

This application describes, among other things, methods of providing third-party interactions to a set of task-specific components. An example method includes receiving, at a user interface of a computing device, a user identifier and a prompt related to an identified clinical task. The method includes determining a set of task-specific components and a set of databases to which the user identifier has access. The method includes selecting, by a machine-learning model trained to select from among the set of task-specific components, a task-specific component from among the set of task-specific components. The method includes communicatively coupling the task-specific component to a database from the set of databases based on the prompt. The method includes providing the prompt to the task-specific component. The method includes receiving a response to the prompt generated by the task-specific component using information from the database and providing the response to a user.

The invention discloses a model training configuration parameter determination method and device and a storage medium, and relates to the technical field of computers, and the method comprises the steps: dividing configuration items into fixed configuration items and undetermined configuration items, and under the constraint that the fixed configuration items adopt specified configuration, determining the undetermined configuration items; predicting the predicted training performance of the to-be-trained model when the to-be-trained configuration item is trained by adopting different configuration parameters, and selecting a target configuration parameter from multiple configuration parameters of a group of to-be-trained configuration items based on the predicted training performance corresponding to the multiple configuration parameters, when the to-be-trained model is subjected to model training, the target configuration parameters are selected by one group of to-be-determined configuration items, so that the cost required by model training can be remarkably reduced, the resource utilization efficiency is maximized, the resource waste is reduced, the training efficiency is improved, and the problems of high hardware resource demand and high cost in a large model training stage in the related technology are solved.

A method, an apparatus, and a computer readable medium for storing instructions are described for a user terminal and a base station for updating an AI / ML configuration in case of a handover. The method performed by a user equipment comprising operating a first AI / ML configuration in a coverage area of a first base station; receiving an AI / ML configuration information indicating a second AI / ML configuration; operating the second AI / ML configuration indicated by the AI / ML configuration information in the coverage area of a second base station. Operating a first AI / ML configuration comprises operating a first AI / ML Model or a first AI / ML Model with a first AI / ML Model configuration in the coverage area of the first base station. The first AI / ML Model is associated with a first AI / ML Model identifier and the first AI / ML Model configuration is associated with a first AI / ML Model configuration identifier. Operating the second AI / ML configuration comprises operating a second AI / ML Model or the first AI / ML Model with a second configuration in the coverage area of the second base station. The second AI / ML Model is associated with a second AI / ML Model identifier and the second AI / ML Model configuration is associated with a second AI / ML Model configuration identifier. Indicating the second AI / ML configuration comprises indicating a second AI / ML Model identifier and / or a second AI / ML Model configuration identifier.

The invention relates to the field of machine learning, and discloses a charging pile fault diagnosis method, which comprises the following steps: S1, preparing an environment, building a deep learning development environment, and providing a necessary technical environment for the training and deployment of a subsequent model; and S2, data acquisition and processing: installing a current sensor, a voltage sensor and a temperature sensor on a new energy automobile charging detection vehicle, and acquiring operation data of the charging pile in real time. And S3, model definition: a fault diagnosis model based on deep learning is constructed, a fully connected neural network (DNN) is adopted, and the model comprises an input layer, a hidden layer and an output layer. The key operation data of the charging pile are collected in real time through the sensor, the fault state is intelligently judged in combination with the deep learning model, rapid and accurate fault positioning and diagnosis are achieved, and the maintenance efficiency of the charging pile is remarkably improved. The system can automatically extract data features and analyze fault types, and manual intervention is reduced.

Implementations for receiving, by a GAI integration platform, a request from an application executed by an enterprise system of an enterprise, the application being executed remotely from the GAI integration platform, processing, through a control tier of the GAI integration platform, at least a portion of the request through a set of modules to generate a prompt that is responsive to the request, the set of modules including one or more of a prompt template module, a prompt quality module, and a personally identifiable information (PII) detection module, transmitting, by the GAI integration platform, the prompt to a GAI system of a plurality of GAI systems, receiving, by the GAI integration platform, a response from the GAI system, the response comprising content generated by the GAI system in response to the prompt, and transmitting, by the GAI integration platform, the response to the application.

The methods, systems, and computer networking apparatuses described herein enable language models to receive input (e.g., a query or a request) from a user or application, and without any additional training data or instructions, determine to generate a function call based on the received input from the user and generate the function call based on the determination. In some embodiments, a language model may further access an external tool or application to request an output as a response to the generated function call. The disclosed methods, systems, and networking apparatuses improve the technical field by incorporating language model capabilities within the function calling process, and allowing for function-related information to be provided to a language model via input received in any number of formats or types, including structured or unstructured input, language or non-language input, or any combination thereof.

A method is provided for processing data in a neural network system. The method includes receiving input data; processing the input data through a first set of neural network layers configured to perform data processing using MatMul-free techniques to produce intermediate data; further processing the intermediate data through a second set of neural network layers configured to simulate spiking neural network (SNN) functionalities using MatMul-free techniques; and outputting a result based on the processed data from the second set of neural network layers.

The invention discloses a topographic surveying and mapping method based on artificial intelligence. The topographic surveying and mapping method comprises the steps of (1) multi-source data fusion acquisition; (2) intelligently extracting topographic features; (3) three-dimensional reconstruction optimization; (4) dynamic terrain evolution modeling; the invention also discloses a system for implementing the topographic mapping method based on artificial intelligence, and the system comprises a data acquisition and preprocessing module, a topographic feature intelligent extraction module, a three-dimensional reconstruction optimization module, a dynamic topographic evolution modeling module and an interactive visualization and decision platform. The data acquisition and preprocessing module, the terrain feature intelligent extraction module, the three-dimensional reconstruction optimization module, the dynamic terrain evolution modeling module and the interactive visualization and decision-making platform are connected in sequence, and each module communicates through a gRPC interface and supports independent upgrading.

Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). The compiler of some embodiments receives a specification of a machine-trained network including multiple layers of computation nodes and generates a graph representing options for implementing the machine-trained network in the IC. In some embodiments, the graph includes nodes representing options for implementing each layer of the machine-trained network and edges between nodes for different layers representing different implementations that are compatible. In some embodiments, the graph is populated according to rules relating to memory use and the numbers of cores necessary to implement a particular layer of the machine trained network such that nodes for a particular layer, in some embodiments, represent fewer than all the possible groupings of sets of clusters.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include generating a digital twin network simulation of a physical computer network controlled through a software-defined-network (SDN) control system. The acts also can include training a routing agent model on the digital twin network simulation using a reinforcement-learning model on traffic that flows through nodes of the digital twin network simulation. The routing agent model includes a machine-learning model. The acts additionally can include deploying the routing agent model, as trained, from the digital twin network simulation to the SDN control system of the physical computer network. Other embodiments are described.

Workloads for executing a graph neural network (GNN) may be divided among various processing units, such as a central processing unit (CPU) and a neural processing unit (NPU). The NPU may include a data processing unit (DPU) and a digital signal processor (DSP). The CPU may perform precomputation, model optimization, hardware optimization, and compilation. For example, the CPU may precompute a parameter matrix and use the parameter matrix as internal parameters of a GNN. The CPU may also perform node padding, approximation computation, or transfer of DSP operations to DPU to optimize the GNN. The CPU may also perform sparsity data compute and storage, vertical fusion of DSP operations and DPU operations, or data quantization to optimize performance of the NPU. The compiled GNN may be provided to the NPU, and the DPU and DSP may perform the operations in the compiled GNN to produce a prediction of the GNN.

Evaluate the accuracy of the RAG system's answers. [Solution] The information processing method disclosed herein includes the steps of: (f) after all Q&A data corresponding to multiple target data satisfy a predetermined standard, a user terminal inputs a first question of a pair of a first question and a first answer to the first question contained in the Q&A data into a RAG system and has the RAG system provide a second answer to the first question; and (g) outputting a second judgment result regarding the accuracy of the second answer to the first answer.

The invention discloses a model training system, a model training task execution method, a model training task execution device and a medium, a management cluster in the model training system can obtain real-time state data when a training cluster executes a model training task, and the model training task is executed through the real-time state data. The state of the training cluster during execution of a model training task in a set time period in the future is predicted, the management cluster determines simulators corresponding to the devices included in the training cluster, and the virtual training cluster corresponding to the training cluster is initialized through the simulators. The management cluster generates a plurality of execution strategies for the virtual training cluster according to the prediction state, simulates and executes the model training task through the virtual training cluster according to at least a part of the execution strategies in the execution strategies, obtains performance indexes corresponding to the at least a part of the execution strategies, determines a target strategy according to the obtained performance indexes, and sends the target strategy to the virtual training cluster. And the model training task is executed according to the target strategy, so that the efficiency of the whole training cluster is effectively improved.