Announcing the Intel® Certified Developer – MLOps Professional Curriculum

More detail behind the training course and certification program announcement.

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Enhanced Fraud Detection Using Graph Neural Networks with Intel Optimizations

Fraud detection challenges: how Intel’s tools & optimizations can help implement enhanced solutions

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From Keywords to Vector Embeddings: Weaviate’s Disruptive Approach to Scalable Search

In the new era of LLMs and genAI nothing is more important than good data, and Weaviate, an Intel Liftoff member, has the unique ability to bring structure to data and gather premium insights. Traditional search engines often rely on keyword matching, which can lead to irrelevant or misleading results. Fortunately, Weaviate is redefining the search landscape with its open-source, cloud-native vector database.

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Argilla: Bridging Human Intuition and Machine Efficiency with Intel®

Discover the groundbreaking technology behind Argilla, a trailblazing startup revolutionizing NLP. Proud member of Intel® Liftoff for Startups, Argilla’s journey from core consultancy to open-source innovation has captivated the tech world since 2017. Stay tuned for more on their remarkable journey

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Beewant and Intel®: Pioneering the Future of Multimodal AI

Beewant is a deep-tech startup and a member of Intel® Liftoff that has its roots in data management and annotation. Spurred by the genAI revolution, they have created a multimodal AI platform that enables enterprises to aggregate, process, and interact with a wide variety of data types, ranging from text and images to videos and audio.

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Efficient Inference and Training of Large Neural Network Models

oneAPI DevSummit for AI 2023: Efficient Inference and Training of Large Neural Network Models

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How Computer Vision and AI are Transforming Retail Technology

Many retailers operating brick-and-mortar stores are focused on creating convenient, frictionless shopping experiences while also improving their operational efficiency.  

With competition from online shopping and ongoing challenges such as loss prevention, stores are finding that advancing computer vision, AI, and IoT solutions can help address these issues. Additionally, the technology can create real-time promotions that are personalized to create more loyal customers. 

At Intel, we’re working with partners to offer optimized software, innovative hardware, and market-ready systems that help retailers differentiate themselves in ways that customers notice while also automating inventory and security management. 

Enhancing retail with computer vision and AI 

Let’s start at the end of most visits to a store: at the checkout line. Many stores have fewer cashiers, and the average self-checkout kiosk can create a hassle for shoppers. Long checkout lines can result in lost sales when shoppers decide a transaction isn’t worth the wait.  

A combination of computer vision, object recognition, and AI can solve that by empowering self-checkout kiosks that allow customers to simply set their objects on a counter. This already is a reality in places like Empower Field at Mile High. The stadium in Denver, Colorado, integrated AI-driven checkout kiosks into some of its concession areas. By using computer vision to generate 3D images of the products customers want to buy and matching them to an inventory database, the system can generate a total price in less than a second. The median transaction time is under 15 seconds. The stadium has seen a 34 percent increase in concession sales as well.  

Convenience stores and other retailers are adopting this type of AI-powered self-checkout to free up staff for other customer services. Alimentation Couche-Tard Inc. is deploying more than 10,000 Mashgin AI-powered Smart Checkout systems to over 7,000 of its Circle K and Couche-Tard convenience stores. As with the Denver stadium checkout system, customers can simply put their items down and pay as they normally would, speeding up checkout by up to eight times compared to traditional self-checkout. 

These types of systems can help track inventory without complication as well, which is a more common use of computer vision and AI in retail spaces to keep high-demand goods in stock, track sales trends, and help reduce theft and other loss of products. Smart camera systems can keep track of the number of sweaters left on a shelf and read barcodes on product boxes from a long distance.  

In addition to fixed computer vision systems, mobile robots also are starting to perform inventory tasks in stores as well. In only 30 minutes, one such Intel-powered robot can audit thousands of items in a store and note out-of-stock or misplaced items as well as incorrect or outdated sales prices. 

Using data collected from these systems, retailers can use analytics solutions to analyze in real time and over the long term what products consumers are buying quickly, and which products are languishing. Additionally, computer vision systems and AI can monitor for theft and alert employees or authorities as needed. Collecting and analyzing collected data can help retailers determine which high-value items are most likely to be taken and need to be secured. 

Data management and analytics systems, with assistance from AI, also can analyze a customer’s past purchases and reactions to past marketing campaigns to better tailor personalized promotions. Retailers can use this, for example, to give loyal shoppers special deals or create special marketing messages for fans of specific brands. 

How Intel and partners are helping retailers 

Our ecosystem of solution partners offers these and other innovative solutions for the retail sector using Intel technology that spans from the edge to the cloud. To power computer vision applications, Intel provides both hardware and software. Intel® Distribution of OpenVINO™ toolkit enables the development of vision applications on Intel platforms, including VPUs and CPUs, while Intel® RealSense™ technology is a reliable, multi-camera, vision-based system designed to understand the retail environment in 3D. This technology already is in use by mobile robots that can track inventory or automate order fulfillment.  

Our processors also power many retail digital solutions, as Intel® Xeon® Scalable processors can power high-performance analytics and include built-in features to accelerate AI. For retail solutions at the edge—including point-of-sale systems, interactive kiosks, digital signage, and more—Intel® Core™ and Intel Atom® processors come in a range of options. 

Intel® Arc™ graphics can power multi-monitor video walls in retail spaces and allow for the creation, sharing, and streaming of high-quality, AI-augmented graphics and images. The Intel vPro® platform also delivers performance, remote management, and security features to help ensure that critical retail devices keep running reliably.  

Learn more about the future of retail 

As retailers adjust operations to a more competitive, changed landscape for their stores, they’re trying to serve shoppers who want frictionless interactions and unique customer service experiences. Computer vision and AI tools can help deliver those things while also improving efficiency, reducing loss, and managing inventory. 

Intel can help with computer vision, AI, compute, storage, and networking technologies that let retailers collect, manage, and analyze data. We also work with a large ecosystem of partners that bring together our technology with their own market-ready innovations. Learn more about how we’re helping to transform retail operations.  

Article co-authored with @GeorgeLoranger

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Food Sales Prediction Model using scikit-learn* (sklearn): Developer Spotlight

Developer Spotlight: Sayan Malakar proposed an AI solution for food sales prediction

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Accelerating Codegen training and inference on Habana Gaudi2

Optimum Habana  makes it easy to achieve fast training and inference of large language models (LLMs) on Habana Gaudi2 accelerators. In this blog, we will walk through the process of performing Low-Rank Adaptation (LoRA) training of Codegen , an open-source LLM for program synthesis. We will also benchmark the training and inference efficiency of Habana Gaudi2 using Codegen

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FAENet: Intel Labs and Mila Collaborate on Data-Centric AI Model for Materials Property Modeling

Intel and Mila collaborated on FAENet, a new data-centric model paradigm that improves both modeling and compute efficiency across different types of materials modeling datasets.

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