12 New Intel AI Reference Kits for a Total of 34 Are Now Available!
The final 12 Intel AI reference kits may now be downloaded for free. To make AI development easier for solutions in consumer goods, energy & utilities, financial services, health & life sciences, manufacturing, retail, and telecommunications, Intel and Accenture developed 34 sets in total. This set completes the collection.
The following are included in each reference kit, which makes use of software improvements for well-known deep learning and machine learning frameworks and libraries including TensorFlow, PyTorch, scikit-learn, and XGBoost:
- Data for training
- A trained model that is open source
- User manuals, libraries, and Intel AI software
- To gain a jump start on resolving comparable business issues in your sector, download one or all of them.
The Intel AI reference kits may also be used with your own data.
12 Intel AI reference kits
Synthetic Data Generation
Synthetic data is being used to meet the need for AI solutions across sectors due to issues with data privacy, restricted data availability, data labeling, inefficient data governance, high cost, and the requirement for a large amount of data.
AI Structured Data Generation (Cross-industry)
Creating a model to artificially produce structured data, such as time series, numerical data, and categorical data, is the main use.
A popular tool in many different sectors, artificial intelligence (AI) structured data generation transforms unstructured or semi-structured data into structured, useful information. Organizations may improve decision-making, optimize operations, and simplify procedures with the use of this data translation.
Text Data Generation (Cross Industry)
Main Use: Using a large language model (LLM), create synthetic text that resembles the given source dataset.
The process by which algorithms produce meaningful, cohesive, and contextually relevant textual information is known as “text data generation using AI.” Natural language comprehension, data augmentation, and content production are just a few of the areas in which this technology finds extensive use.
Image Data Generation (Health and Life Sciences)
The main use of picture data generation is the creation of artificial images via the use of generative adversarial networks (GANs). In the health and life sciences, picture data generation is the process of creating or improving photographs for a range of medical and scientific purposes using artificial intelligence and machine learning.
Voice Data Generation (Cross Industry)
Main Use: Using transfer learning with VOCODER models to translate input text data into voice.
Voice data generation is the process of producing life like artificial speech for a range of applications in different sectors. It creates voice recordings that sound human by using AI and machine learning.
AI Data Protection (PII) (Cross Industry)
Main Purpose: Reducing privacy issues with personally identifiable information (PII) throughout the development and design phases.
To satisfy legal requirements and consumer expectations, data masking, data de-identification, and anonymization sanitization are used.
Computational Fluid Dynamics (Cross Industry)
The main purpose of computational fluid dynamics is to accurately simulate fluid flow in order to improve component engineering design, such as in the automotive, energy, and aerospace sectors.
The partial differential Navier Stokes (NS) equations regulating the environment and boundary conditions are usually solved numerically to determine fluid flow profiles. This process is iterative, time-consuming, and requires a lot of computation and memory. The design of a wind turbine blade, a Formula-1 car’s spoiler, or even the arrangement of server chips in a large data center, where wind flow will alter cooling patterns or create hot spots, are examples of infrastructure where these factors discourage the quick design and development of infrastructure where aerodynamics is essential to efficient operation.
Structural Damage Assessment (Cross Industry)
Main Use: Using satellite-captured photographs as input, a computer-image model is created to determine the extent of damage caused by a natural catastrophe.
Predictive insights and up-to-date knowledge about past or upcoming catastrophes are essential for effective disaster management. Artificial intelligence (AI)-based technical methods for interpreting satellite images of building structural damage hold enormous promise for disaster response and management.
Vertical Search Engine (Cross Industry)
The main application is the creation of a natural language processing (NLP) model for document semantic search.
Unlike conventional keyword-based search systems, semantic search engines allow the use of the contextual meaning inside a query to discover matched content more intelligently. A technique for converting text-based queries and documents into a format that captures semantic meaning is essential to creating efficient semantic search engines. Compared to conventional text search engines, users may discover answers, information, and goods more precisely using AI-powered semantic vector search.
Data Streaming Anomaly Detection (Cross Industry)
Creating a deep learning model to identify irregularities in sensor data that tracks equipment conditions is the main application for data streaming anomaly detection.
To handle vast volumes of data as they are created, organizations create apps that integrate streaming data from sensors, meters, mobile devices, social media, machine control systems, etc. More and more professionals are depending on these apps to provide them with up-to-date information so they can do their everyday tasks efficiently.
Visual Process Discovery (Cross Industry)
Main Use: Recording user-workflow interactions in real time and offering unbiased, data-driven insights to improve procedures.
From the provided website screenshots, this Intel AI Reference Kits assists in identifying user interface (UI) components (buttons, links, text, pictures, headers, forms, labels, and iframes) that people have interacted with.
Engineering Design Optimization (Manufacturing)
The main purpose is to assist engineers in creating practical production designs.
Engineers are being pushed by the increasing demands for innovation in manufacturing to use AI models in order to design a variety of complicated, high-performance components, lower manufacturing costs, and speed up the product development process.
Traffic Camera Object Detection (Government)
Main Use: Creating a computer vision model that uses real-time traffic camera picture analysis to forecast the likelihood of auto accidents.
By decreasing road congestion, increasing the precision of pedestrian and vehicle identification, enhancing device-to-device communication, and assisting in the reduction of accidents, AI-enabled traffic camera imaging aids assist in addressing traffic management difficulties.
Find Out More
All 34 of the Intel AI Reference Kits are available for free download and use in your coding pursuits, whether they be personal or professional. Examine them all, download the ones that look helpful, and then distribute the other ones to your coworkers.
