Since the fall of 2022, Intel has collaborated with Accenture to introduce 22 AI reference kits (and counting!) covering industries such as energy & utilities, financial services, health & life sciences, retail, semiconductor, and telecommunications. There are also kits that apply cross-industry including chatbots, intelligent document processing, and network intrusion detection.
Each reference kit includes the following:
- Training data
- An open source, trained model
- Libraries
- User guides
- Intel® AI software products
These kits take advantage of the software optimizations for popular frameworks and libraries in deep learning and machine learning such as TensorFlow*, PyTorch*, scikit-learn*, and XGBoost. These kits are designed to give you a head start to solve similar business problems in your industry, and you can use the reference kits with your own data.
The Rundown – the 6 New Kits to Streamline Your Business Solutions
Automate Processing of Trade Promotion Deductions (Consumer Products)
Primary Use: Developing a model to help automate the process of claims.
One of the most important finance processes for a consumer products company is the resolution of trade promotion deductions (claims) by their account receivables (AR) department. Trade promotion deductions are special incentives that are offered to retailers to increase the demand for products. For example, a tire manufacturer may send a special offer to tire retailers like buy three, get one free. Rather than pay for all the tires ordered, the tire retailer only pays for a portion of the tires, per the details of the special offer.
The AR team for the consumer products company must confirm the payment and reconcile what is due from the tire retailer based on the offer. It is a complex process that involves many parties and systems and requires time to assess and validate.
Extract Text from Engineering Documents for Utility Assets (Energy & Utilities)
Primary Use: Developing a deep learning model to perform text extraction from scanned documents such as those containing engineering-related details for utility company assets.
Utility companies need to manage and maintain the energy infrastructure for efficient and economical delivery of utilities. Maintaining the engineering integrity of their assets (power lines and utility poles) can be a challenge. Engineering data is stored in documents, drawings, and models. This data can be structured, semi-structured, and unstructured; most often it’s paper-based.
Using AI technology to digitize engineering information is critical for utility companies to scale and maintain their infrastructure.
Identify Drone Landing Areas (Energy & Utilities)
Primary Use: Developing a model to help identify the segments of paved areas available for landings.
In the energy and utilities sector, inspecting the growing numbers of towers, powerlines, and wind turbines is difficult and can be dangerous to personnel. This creates prime opportunities for drones to replace human inspection with computer vision-based inspection and diagnosis. However, if a drone meets with an accident while landing, it could damage assets and injure personnel.
Drone landings can be done more safely when a paved area is identified or dedicated to drone landings.
Improve Powerline Fault Detection (Energy & Utilities)
Primary Use: Developing a machine learning model to process and analyze the signals from a three-phase power supply system used in power lines to predict whether or not a signal has a partial discharge.
Faults in overhead electric transmission lines can lead to a destructive phenomenon called partial discharge (PD). If left unaddressed, PDs can eventually destroy equipment.
Overhead electric transmission lines run for hundreds of thousands of miles all over the US alone, so manually inspecting the lines for damages that don’t cause an immediate outage is expensive. However, if left alone, undetected faulty lines can lead to PDs. Using machine learning to proactively detect PDs can reduce maintenance costs and prevent power outages and fires.
Strengthen Customer Engagement with Product Recommendations (Retail)
Primary Use: Helping e-commerce retailers present targeted product recommendations to customers by applying textual clustering analysis to the product descriptions.
AI-based applications can help e-commerce retailers better understand who their customers are, what they like and dislike, and how they shop. This knowledge enables retailers with many opportunities to improve customer experience by creating more personalized interactions such as unique product recommendations and reminders to re-purchase regularly used items. There are cases in which a new customer does not have any previous purchase history.
Retain Customers with Better Churn Prediction (Telecommunications)
Primary Use: A reference to help companies craft a solution for predicting customer churn status, which can be part of an overall customer-service improvement system.
Losing customers can negatively impact a company’s bottom line. Churn rate is an important metric for telecommunications (telecom) providers to capture as it reflects the annual percentage rate of customers that stop subscribing to its service. Churn rate is monitored by company investors as well. Communications service providers (CSPs) should look for early indicators of customer attrition. However, the amount of customer data to examine attrition can run into the petabytes, thus requiring considerable computational costs.
Learn More and Go Get Them
At this writing, there are 22 AI Reference Kits, all of which are free to download and use in your personal or professional coding endeavors. So … explore them all, download what seems useful, and share the rest with your colleagues.