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How LLMs are revolutionizing Supply Chain Industry?

by with No Comment AIDevelopMarketing

Introduction

Supply Chain Management or Industry is that one biased industry department where giants are dominating and small & micro players are continuously making huge losses. In this blog post we will try to understand the in the simple terms where is supply chain management needs to be done and how technology like AI, Gen AI can fly off any retail business using appropriate LLM models.

Untouched problems in retail business

Still in today’s business world especially when we talk about Retail Industry we always talks majorly about Sales, Marketing, Products, Marketing Trends, Digital Marketing, Finance, Taxes and much more but we barely talks about the supply chain management. Today we’re gonna considering the wholesale and retail business as our center point and how many small & micro business owners are making huge losses in not considering the supply chain management as a core department in their business. There is a huge misconception about the wholesaling or retailing business is, they are into sales business or they are having some products that need to be sold on large scale to make money. Many business owners are focused on increasing sales and for that they are using multiple sales techniques, discounts, offers etc., but they are missing one most important thing into their business is they are not in selling business instead they are in supply chain business especially when we consider the wholesale business. Wholesale business is never the business of selling wide variety of products in large scale, it is a business of using appropriate techniques of supply chain. A wholesaler’s core objective to provide right product to the right retailer in the right time. Here many wholesalers are missing one thing i.e., right time. Many wholesalers have figured out the right product for the right retailer but they are missing the right time especially in the dense populated cities. So if any wholesaler or retailer who is in B2B business they are not actually in product selling rather they are in supply chain business. Once the right product is supplied correctly to the right customer in the right time, boom the sales happened. And to reach out to the customer in the right time in competitive market it is necessary to sort the supply chain problem. And to find the answer to this problem we are trying figure out how Artificial Intelligence, Gen AI, and LLMs can be highly helpful in solving the supply chain management issues to multiply the revenue.

The Role of LLMs in Supply Chain Management

LLMs, like GPT-4, have the ability to understand and generate human-like text, making them powerful tools for various applications in supply chain management. Here are some key areas where LLMs are making a significant impact:

1. Demand Forecasting

Demand forecasting is the primary key to consider in the supply chain. A business owner must use historical data, previous bills, market trends, and other external factors like economic indicators, seasonal variations, demand and supply trends, etc., for better inventory management, avoiding overstocks, stockouts situations. And to manage the inventory here comes LLMs in the picture. LLMs are given all the previous data and models captures latest market trends from Google to get the perfect result in demand forecasting and reduce the operational costs.

2. Supplier Management

Supplier relationships for timely deliveries could be the most challenging task and here comes LLMs in the scenario for monitoring supplier performance, predicting general delays, suggesting alternative supplier in the same domain. In fact LLMs can analyze communication patterns and contractual terms to identify risks and opportunities for negotiation.

3. Real-time Inventory Tracking

Tracking the inventory in the real-time across multiple locations is a very complex task. Sometimes product theft is recognized, the most common problem that is seen regarding the inventory management is product miscalculation for the dispatch. General human error is a very common problem in inventory management. LLMs can save this time, efforts and save the human errors. LLMs can integrate with IoT devices to provide real-time update to the inventory management software from each rack. Also LLM can predict positive inventory shortage during special season time and create an automatic reordering process. This enhance visibility and control over the supply chain.

4. Logistics Optimization

Logistics is the most risky part in the supply chain. Most wholesalers had sunk their business due to so many loopholes in logistics process. Most common problems that are recorded for the loss of supply chain business is in the logistics. Some of the problems are mentioned below:

  • Unexpected traffic in the route results in delayed delivery.
  • Whether conditions.
  • Fuel Prices
  • Un-trackable stops during the routes.
  • Irregular schedules and unavailability of vehicles on time.

These are some of the common issues that are found in the logistics department. The list might be more but important is solution. Most of the wholesalers, supply chain businesses fails to solve these loopholes and LLMs can solve them easily. LLMs again can be integrated with IoT devices and attached to the vehicles that can track real-time motion, real-time traffic situations, whether conditions, unnecessary stops during the routes, time of halt, problems for the solution is only one LLM.

Technical Considerations for Integrating AI in Retail

Integrating AI, particularly LLMs, into the retail industry requires careful planning and execution. Here are some key technical considerations:

Data Quality and Quantity

The success of LLMs depends on the quality and quantity of data they are trained on. Wholesalers need to ensure they have access to comprehensive, clean, and up-to-date data. This may involve investing in data collection, storage, and preprocessing infrastructure.

Scalability

Wholesalers must consider the scalability of their AI solutions. As the business grows, the AI system should be able to handle increasing volumes of data and provide real-time insights without compromising performance.

Integration with Existing Systems

Seamless integration with existing supply chain management and ERP and CRM systems is crucial for maximizing the benefits of AI. Wholesalers or B2B Retailers need to ensure that their AI solutions can communicate effectively with other software platforms to provide a unified view of the supply chain.

Security and Privacy

With the increased use of AI comes the responsibility to protect sensitive data. Wholesalers must implement robust security measures to safeguard customer information and proprietary business data. Compliance with data privacy regulations is also essential. Hence it is a common practice while developing such systems and Priority Technologies Inc., is expert in developing the system for data privacy.

Continuous Learning and Adaptation

AI models need to be continuously updated with new data to remain effective. B2B Retailers should establish processes for ongoing learning and adaptation of their AI systems to keep up with changing market conditions and emerging trends.

Conclusion

The integration of Large Language Models into supply chain management is transforming the retail industry by addressing key challenges such as demand forecasting, supplier management, real-time inventory tracking, and logistics optimization. By considering the technical aspects of data quality, scalability, system integration, security, and continuous learning, retailers can harness the full potential of AI to drive efficiency and growth.

Priority Technologies Inc., is your perfect AI Development partner and excited to see how you as a wholesaler will continue to shape the future of supply chain management and help your retailers stay ahead in a competitive market by getting such AI powered systems developed with Priority Technologies Inc.

LLM For Retail Business.

What is LLM and why it is important in retail : A Guide for Business Owners

by with No Comment AIDevelop

If you are a business owner in the retail industry, it is important to keep up with the latest technology to maintain your competitive advantage. My name is Prankur Atre, an AI thought leader particularly interested in Generative AI and business leadership with a strong technical and entrepreneurial background. So, now I am going to tell you about these; the AI and its revolution that how it changes your business reality mostly in growth within Retail.

LLM Made Simple: A Door to Bright Retail Future

This new generation of advanced AI system that can understand, generate and respond to human language in a highly sophisticated way is created with the advent of Large Language Models (LLMs). LLMs are able to analyze non-structured data, such as text from customer reviews or social media posts without a specific field with information which is invaluable for understanding consumer behavior and market trends compared to traditional AI that relies heavily on structured inputs. This makes LLMs highly valuable to any retail business looking for ways to improve customer engagement and operational efficiency.

LLM at Retail: Turning Customer Touch-points into Significant Transformers

Customers have all the information they want and need from in-the-moment, real-time and personalized product recommendations to instant customer care response; analytics pinpoints exactly what changes your business needs. LLMs can take customer service a step further, giving data to chatbots and virtual assistants to answer in usable language 24/7. Used properly, these AI-fueled replicas can help manage a large number of customer interactions ranging from general product inquiries to facilitating ecommerce sales — thereby increasing consumer satisfaction and retention.

Increasing Sales with AI-backed Insights

Analyzing large datasets quickly and accurately — in retail, this is one of the most promising applications of LLM. With AI assortment planning, retailers can utilize data to expand their inventory management capabilities by analyzing sales data, customer preferences and market trends all in one place which leads to more informed decisions on pricing strategy and promotional campaigns. For example, businesses can use predictive analytics to predict demand for certain goods or services and limit unnecessary storage for a portion of the costs. The degree of granularity in inventory control can improve sales and margins.

Driving business growth through Operational Efficiency

Beyond customer engagement, Artificial Intelligence can streamline various operational aspects of retail business. From managing supply chain department to automating routine tasks, Artificial Intelligence can reduce manual efforts and lower down the possibility of errors. This leads to significant cost savings and allows your team to focus on more strategic initiatives. Moreover, AI-driven analytics provide actionable insights that can inform business strategy, helping you to identify new opportunities and mitigate potential risks.

Beautifying the future of retail with AI

Incorporating AI, particularly LLMs, into your retail business strategy is not just a trend but a transformative move towards future-proofing your operations. As Artificial Intelligence technology continues to evolve, its applications in retail will expand, offering even more innovative ways to enhance customer experience, increase efficiency, and sales. By embracing Artificial Intelligence today, you position your business to thrive in an increasingly digital and competitive marketplace.

Conclusion

Integration of AI, especially LLMs, into your retail operations can unlock unprecedented growth and efficiency. As a seasoned expert in AI and business development, I encourage you to explore these technologies and harness their potential to propel your business forward. The future of retail is here, and it’s powered by AI. Meet virtually to implement AI to your retail business with the help of Priority Technologies Inc. Fill the form below and our team will reach out to help you.

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AI in Retail

Generative AI for Retail.

by with 2 Comments AIDevelop

Introduction on Generative AI for Retailers:

Do you know that The Diamond Sutra, a Buddhist book from Dunhuang, China, is the oldest known printed book from 868 AD? It was printed during the Tang Dynasty. The reason I started my article with a printed book is directly connected with today’s topic: Generative AI for Retailers. It is human psychology that anything new introduced to the world stems from innovation. That is partly true, but not entirely. Yes, anything introduced to the world comes from innovation, but before that, it comes from a series of events in history.

Today, in our blog, we are going to learn how Generative AI, Artificial Intelligence, Machine Learning, and Deep Learning can help small, medium, and large retail enterprises. Before we delve into finding answers to our questions, we need to travel back in history and find the actual question about technology. What is the actual question, you may ask? Then I would say the biggest and global question in front of small and large business owners about AI is whether AI will lead to unemployment. Will AI tend to shut down businesses? Will AI disrupt the economy and GDP as the pandemic did? And corporate layoff news plays like salt on a wound, deepening the doubt. So many questions arise about Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI. Stay tuned as you will explore a lot on this topic.

Consequences in History about Technology:

Before the 18th century, common people generally took a pessimistic view of technology upgrades, linking them to massive unemployment. With a low literacy rate and high unemployment due to industrialization and technological inventions, people globally feared an increase in unemployment rates due to technological advancement. The term “Luddite Fallacy” was introduced, describing the belief that innovation would have a harmful effect on business and employment. However, some economic thinkers began to argue against these fears, claiming that overall innovation would not have a negative effect on employment. During the second half of the 19th century, these fears gradually disappeared, and new standards emerged showing that technological innovation benefits the overall sectors of the economy and society. People began to upgrade their learning according to new innovations. This change led to an increase in education and literacy rates among common people regardless of category or financial state.

There are many examples explaining the fear of innovation, doubts about employment rates, and further innovations that set remarkable standards without which we cannot imagine our lives today. When the printing press was introduced, people feared it would steal their employment; later, those printing presses came under pressure when floppy disks and compact disks were introduced to store large amounts of information without wasting paper. From compact disks to pen drives, the journey continued but never shut down the printing press. However, there is a catch: the way printing presses operated previously is not the same as today; obviously, there are some changes in the technology of machines and their designs. Upgrades were made over time, helping to ensure continued existence. Meanwhile, there are other examples of businesses that did not adopt these innovative changes and had to leave the industry, despite having substantial bank balances.

Kodak, Nokia, and Blackberry were some of the biggest giants of their time but failed due to the Luddite Fallacy. A former vice-president of Kodak, Don Strickland, says, “We developed the world’s first consumer digital camera, but we could not get approval to launch or sell it because of fear of the effects on the film market.” Fear is the biggest enemy of any business. Today, the same fear exists within small and medium-sized retailers who believe Artificial Intelligence and Generative AI may disrupt the market, and they lack the technical skills to integrate them into their business.

Do you have Cloud Technology?

Generative AI for Retails

What is Cloud Technology?

In a nutshell, cloud technology is the on-demand access to computing resources such as virtual servers, data storage, networking capabilities, application development tools, software development and deployment tools, and AI-powered tools, with pay-per-use pricing. Servers are expensive to purchase and require support for flexible data storage, development environments, internet connectivity services, and more. So, some service providers have large data centers that are highly secure and provide free or paid resources based on your requirements. There are many service providers available in the market, offering various tools and services. One of them is AWS (Amazon Web Services). We will discuss more about AWS in the sections below.

What is AWS?

AWS stands for Amazon Web Services, which provides cloud computing services. It is a set of tools and resources that you can use over the internet to help you build and manage websites, mobile apps, and software, among other things.

How does AWS work?

  • Services and Resources: AWS offers various services like storage for your files, servers to run your websites, databases to store all your digital information, and even tools to analyze data. You can freely use these services according to your requirements.
  • Cloud Infrastructure: Previously, many businesses had to buy physical servers or storage devices with fixed storage limits. AWS provides the same services with more flexibility and security. You can now rent servers as per your needs. You can increase or decrease the storage limit anytime, anywhere, for a minimal fee.
  • Regions: AWS has data centers around the world called regions. Before starting to use AWS, you choose the region where you want your service or website hosted. This helps in many factors, such as reaching your local target audience easily and quickly without facing local law constraints.

How to Develop Software on AWS?

Anyone can easily develop a website or software on AWS with some development and programming knowledge. Below are the steps explaining how you can develop software:

  • Choose Services: AWS provides various services, so before starting, choose services according to your needs. For developing a website, you need a server (Amazon EC2), a database (Amazon RDS), and storage (Amazon S3).
  • Set up an Environment: An environment is like a platform where a developer can write code and logic according to requirements. To create an environment, AWS provides the AWS Management Console, which acts as a control panel. Just as a plinth is necessary to construct the rest of the building, an environment is essential.
  • Deploying Code: Once the resources are ready, you can now upload the website code if it was written in any other environment. Many developers write code in different environments based on their comfort and choice. So, you can now upload all the code here to deploy on the server.
  • Monitor and Manage: AWS provides tools to monitor your resources, track performance, and update settings as needed.

Integrating Generative AI on AWS:

As mentioned earlier, AWS provides AI tools and frameworks that can be integrated into the software or website hosted on the server. This is an important part of our article, where you can learn how to integrate AI algorithms and frameworks on AWS services you rented.

Integrate AI on AWS, Gen AI for Retail

  • Use AI Services: AWS offers AI services such as Amazon Comprehend for natural language processing, Amazon Rekognition for image and video analysis, and more. These services can be used and added to your project.
  • Choose Generative AI Models: Before obtaining any generative results, it is important to train the software or project as per the requirements. To integrate a generative AI model, AWS provides Amazon SageMaker, which trains and deploys machine learning (ML) models that can generate fresh content such as text and images. It also offers an environment to integrate APIs of various AI tools to fetch customer data from the database and create insights using prompt engineering.
  • Automate Business Processes: In the previous section, you integrated various AI tools into the AWS server using SageMaker. By using prompt engineering, you trained the machine learning (ML) models to generate insights such as personalized promotional content for social media, targeting customers, creating personalized offers based on past shopping, and integrating AI chatbots on the website, where a user can input problems, suggestions, or requirements. These inputs are saved in the AWS database, which the ML model uses to generate insights. Thus, promotional ads, personalized offers, and many other tasks can be automated, increasing customer trust and engagement with the business.
  • Connect AI Servers: As mentioned earlier, a visitor entered a query or suggestion in the AI chatbot, and that value is stored in an S3 bucket (database). Later, you need that information for the ML model to analyze and create insights. AWS provides Amazon Comprehend as an AI service to connect artificial intelligence (AI) or machine learning (ML) models with the database and fetch the correct information without errors.

Conclusion:

To conclude my views on Generative AI for Retailers, it will soon be impossible to ignore integrating Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI into existing systems to open new doors to success. Artificial Intelligence will not shut down your business as long as you adopt it correctly. Otherwise, your business will eventually be in danger. Now is the perfect time to set up a meeting with Priority Technologies’ AI expert and discuss how you can integrate Generative AI for your retail business. Stay connected with us to learn how LLM and NLP work.

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Artificial Intelligence in Retail: Revolutionizing Shopping Experiences

by with No Comment AIDevelop

Today, we’re going to explore the fascinating world of artificial intelligence (AI) and how it’s reshaping the retail industry. AI, often portrayed in sci-fi movies as futuristic robots, is not just a concept for the distant future—it’s already here, transforming the way we shop and interact with brands. So, let’s dive into the exciting applications of AI in retail and how it’s revolutionizing the shopping experience!

Understanding AI in Retail:

  1. Introduction to AI: Artificial intelligence refers to the use of computer systems to perform tasks that typically require human intelligence. In retail, AI algorithms analyze vast amounts of data to understand customer preferences and behaviors, leading to personalized shopping experiences.
  2. Key Applications of AI:
    • Personalized Recommendations: AI algorithms analyze customer data to provide tailored product recommendations, enhancing customer satisfaction and increasing sales.
    • Dynamic Pricing: Retailers utilize AI to adjust prices in real-time based on demand, competitor pricing, and other factors, optimizing revenue.
    • Inventory Management: AI-powered systems predict demand and optimize inventory levels, reducing stockouts and minimizing overstock situations.
    • Customer Service: Chatbots powered by AI provide instant assistance to customers, answering queries and guiding them through the purchasing process.

Case Study: The Impact of AI on retail (Villagewale).

Let’s take a closer look at Villagewale, a leading e-Commerce Grocery brand, and how they leveraged AI to enhance their operations and customer experiences.

Background:

Villagewale faced challenges in understanding customer preferences, managing inventory efficiently, and delivering personalized experiences to shoppers.

Implementation of AI Solutions:

  1. Personalized Recommendations: Villagewale then implemented AI-powered recommendation engines on their e-commerce platform. By analyzing past purchase history and browsing behavior, the system provided personalized product recommendations to each customer, resulting in a significant increase in conversion rates and average order value.
  2. Dynamic Pricing Strategy: AI algorithms were deployed to analyze competitor pricing, demand patterns, and market trends. Villagewale adjusted their prices dynamically, offering competitive rates while maximizing profits. As a result, they saw improved revenue margins and increased customer loyalty.
  3. Inventory Optimization: Villagewale implemented AI-powered inventory management systems that predicted demand accurately. By optimizing inventory levels, they reduced stockouts and excess inventory, resulting in cost savings and improved customer satisfaction.
  4. Enhanced Customer Service: AI chatbots were integrated into Villagewale’s website and mobile app to provide instant assistance to customers. These chatbots answered product-related queries, guided customers through the purchasing process, and even provided styling tips, leading to enhanced customer satisfaction and retention.

Results:

By leveraging AI technologies across various aspects of their business, Villagewale achieved remarkable results:

  • Increased sales revenue by 25% through personalized recommendations.
  • Improved profit margins by 15% with dynamic pricing strategies.
  • Reduced inventory holding costs by 20% through optimized inventory management.
  • Enhanced customer satisfaction scores by 30% with AI-powered customer service solutions.

In conclusion, the implementation of AI technologies transformed Villagewale’s operations, enabling them to stay competitive in the ever-evolving retail landscape while providing exceptional shopping experiences to their customers.

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Conclusion:

Artificial intelligence is not just a buzzword—it’s a game-changer for the retail industry. By harnessing the power of AI, retailers can unlock new opportunities for growth, optimize operations, and deliver unparalleled customer experiences. As future leaders in the retail industry, it’s essential to embrace AI and its transformative potential to stay ahead of the curve. And Priority Technologies Inc., could be the only trustworthy company for you to develop a dynamic platform. Priority’s dynamic team of AI and Machine Learning Experts are for you to solve all your problems and give your business a boost along with that your customer will be delighted by this technology.

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Unveiling the Power of Convolutional Neural Networks in Real Estate and Retail Businesses.

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Introduction: In today’s digital age, recognizing patterns and objects is vital for businesses, especially in the real estate and retail sectors. Convolutional Neural Networks (CNNs) are at the forefront of this revolution, enabling computers to identify visual cues with astonishing accuracy. Let’s dive into how CNNs are reshaping the landscape for real estate and retail businesses.

Understanding CNNs: CNNs are like supercharged detectives for images, meticulously scanning for visual clues that humans might overlook. Instead of processing images pixel by pixel, CNNs break them down into smaller, digestible chunks, allowing for efficient pattern recognition.

How CNNs Work: Imagine a CNN as a team of investigators analyzing a photograph of a house. The first step is the convolutional layers, where filters sweep across the image, searching for key features like windows, doors, and roofs. Next, pooling layers condense the findings, focusing on the most important details. Finally, fully connected layers piece everything together, making sense of the visual data.

Pattern Recognition in Action: Consider a real estate agent using a CNN to scan property images. The CNN quickly identifies common features like landscaping, architectural styles, and interior layouts, providing valuable insights for marketing and sales efforts.

Business Applications of CNNs: For real estate businesses, CNNs offer a myriad of possibilities:

  • Property Marketing: Generating visually appealing listings with accurate descriptions and immersive virtual tours.
  • Property Analysis: Analyzing market trends, property values, and investment opportunities with precision.
  • Customer Engagement: Personalizing the shopping experience with targeted recommendations and interactive visualizations.

In the retail sector, CNNs are equally transformative:

  • Visual Merchandising: Optimizing product placement, store layouts, and window displays to attract customers.
  • Inventory Management: Automating stock monitoring, shelf replenishment, and order fulfillment for greater efficiency.
  • Customer Service: Enhancing online and in-store experiences with AI-powered chatbots and virtual assistants.

Conclusion: Convolutional Neural Networks are not just tools; they’re game-changers for real estate and retail businesses. By harnessing the power of CNNs, companies can gain a competitive edge, drive sales, and delight customers in ways previously unimaginable. Embrace the future of visual intelligence with CNNs and watch your business soar. For developing a software on Deep Learning and Machine Learning Technology there is one and only one trustable company i.e. Priority Technologies Inc., that will help you in reaching the goals with trust and loyalty.

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Embracing Generative AI: Transforming Real Estate and Retail Business Operations.

by with No Comment AIDevelop

Introduction: In today’s rapidly evolving digital landscape, staying ahead of the curve is essential for real estate and retail business owners. With the advent of Generative AI, a groundbreaking technology that predicts future data and generates outputs to match expectations, a new era of innovation has dawned upon us. In this article, we will explore the transformative capabilities of Generative AI and its profound impact on real estate and retail businesses.

Understanding Generative AI: Generative AI stands apart from other forms of artificial intelligence by its ability to predict future data and generate outputs that closely match desired expectations. Unlike traditional AI models, which are limited to specific tasks, Generative AI harnesses the power of predictive analytics to drive decision-making processes and enhance operational efficiency.

Applications in Real Estate and Retail: Generative AI holds immense potential for revolutionizing various aspects of real estate and retail operations. Here are some key applications:

  1. Property Descriptions: Generative AI can generate compelling property descriptions based on historical data and market trends, saving time for real estate agents and captivating potential buyers.
  2. Visual Search Optimization: By identifying the right keywords for visual search, Generative AI helps retailers improve product discoverability and enhance the shopping experience for customers.
  3. AI-Powered Visualizations: Real estate developers can leverage Generative AI to create immersive 3D visualizations of properties, enabling potential buyers to explore virtual spaces with ease.
  4. Predictive Analytics: Generative AI algorithms can analyze vast amounts of data to predict property prices and market trends, empowering investors and business owners to make informed decisions.
  5. Customer Support and Operations: AI-powered chatbots and virtual assistants can provide personalized customer support and assist property managers in analyzing operations and acquisitions.

Economic Impact: The adoption of Generative AI has the potential to drive significant economic growth by providing accurate insights and predictions for business decision-making. By enabling real estate and retail businesses to make data-driven decisions, Generative AI contributes to enhanced productivity, increased revenue, and improved competitiveness in the marketplace.

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Addressing Concerns: While the benefits of Generative AI are undeniable, it’s essential to address potential concerns such as data privacy and accuracy. Real estate and retail business owners must prioritize data security and transparency in their AI implementations to build trust with customers and stakeholders.

Embracing Innovation: Incorporating Generative AI into business operations requires a strategic approach and a commitment to ongoing learning and adaptation. Real estate and retail business owners can leverage the expertise of AI developers and technology partners to implement tailored solutions that align with their goals and objectives.

Conclusion: Generative AI represents a paradigm shift in the way real estate and retail businesses operate, offering unprecedented opportunities for growth and innovation. By embracing this transformative technology, business owners can unlock new levels of efficiency, productivity, and profitability in an increasingly competitive marketplace. For this Priority Technologies Inc., is the only company that can help you in developing the desired software. Fill the form and fix an online meeting to discuss the requirements.

software

Understanding the Role of Convolutional Neural Networks (CNNs) in Business Growth

by with No Comment AIDevelopUncategorized

What is a convolutional neural network? What is the impact of CNN in general businesses? How can CNN help a business to grow? What impact will a CNN make in business growth? These are some basic questions of a business owner who wants to involve A.I in their business but is completely knowledgeless on A.I and its subsets. This article will explain what a CNN is and how it can be used for business growth?

Before understanding directly about CNN or Convolutional Neural Networks, it is way more important to understand the concept of Deep Learning and Machine Learning. What is Machine Learning? Machine Learning is a subset of Artificial Intelligence where machines are given knowledge using commands of Python and are asked to work accordingly. At the same moment, Deep Learning is a subset of Machine Learning. In Deep Learning, machines are given input in the form of an image or video and asked to read that image, generate insights, and give suggestions to act based on that insight.

Deep Learning is a very new technology which is commonly used by many large companies to generate insights of consumer purchase decisions and much more. But local business owners are completely unaware of the power of Deep Learning that can be used for their business. In this article, you will come to know how Deep Learning can be used in simple Retail Businesses and give it a boost.

Let’s understand some technical specifications of Deep Learning in the simplest form. Deep Learning is a subset of Machine Learning. It is used to analyze the image. It detects the pattern to make it sense and do classification. CNN has hidden layers called a convolutional layer of the model. The transformation of input to one layer and then its output as an input to another layer is called a convolutional operation.

Block Diagram

 

How can CNN help in a retail store:

Let’s move ahead with an idea as a model and will try to understand the use of CNN in it. Let’s consider a grocery store where customers come in and pick up products of their own choice. So, there are several racks filled with products. Users pick one out of that and camera placed in the rack will capture the image of the hand and product that is been picked up. Now that image will be sent to the software of the store where CNN Model is integrated. Now that image will be sent and CNN will transform the image from one layer to another depending upon the layers given during the development of the software. Then during the transformation of the image, the important features of the image will be given as an input to the other layer. In the same way to the third and so on. This way important features will be extracted and transformed to reduce the dimension of the image. At the final step of feature extraction, the image will be flattened and finally the output using previous data and current data will be extracted. Now the output that is received will be first saved in the database of the system for future reference and in the same manner the output will be sent to the system for billing with product information like product type, rack number, product value, total quantity, new reduced quantity and much more. The system will first generate the invoice of the product and will store this information.

How system will come to know the customer is regular or new?

For the above question Deep Learning’s CNN model is enough on its own. While capturing the image of the product, the camera will capture the details of the hand or face of the customer and will be sent to the database to read the previous data. In this way, if the image matches with the previous record, then the customer is regular with all the frequency and if the image doesn’t match the previous records then it is obvious the customer is new.

Why to capture the customer images?

It is important for businesses to create offers for the customers; it is important for customer experience in the stores. The system will analyze the data and get the report with the types of products often picked from the rack by the same customer along with the number of times in specific intervals of time. In this way, software will automatically send the regular marketing offers customized for the customer. Using previous record Machine Learning Model will then generate the list of the regular customer with the name of the products and the number of items that are frequently purchased on a specific date. In this way store marketing model will send the personalized message to the customer that the product quantity is about to end and get them purchased with new discounted rates.

Customer Security:

Sometimes it is important to get the consent of the customer for capturing their images. Then to solve this issue, the customer image is captured only for one time to generate the invoice and match the record. Once the invoice is generated and the record is matched with the customer then the image will not be saved and only product information will be saved for future use. This will save the privacy of the customer and the model will only match the image so no image will be shown in the system this will create another layer of data security.

Get the customized A.I software for your businesses:

Priority Technologies Inc. will be your perfect partner to develop software for your grocery or retail store. Fill the form and get a meeting arranged to discuss the requirements. This new era of business will be only for those who will integrate AI into their businesses rest will have to shut. Be the one out of competition and destroy your competition.

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Navigating the Impact of Artificial Intelligence on Jobs

by with No Comment AIDevelop

We are currently in the era of Artificial Intelligence (AI), which is autonomously handling daily repetitive tasks. This raises a significant question among those less familiar with the technology: will AI replace jobs? In this article, I will delve into the functionality of AI and elucidate its limitations.

Before delving deeper into AI, let’s define it. AI stands for Artificial Intelligence, a term commonly understood. However, is artificial intelligence truly possible? Intelligence is typically considered a natural process acquired through practice and knowledge. But can computers replicate this process through programmed algorithms? Can they store and extract information from books effortlessly? Indeed, this capability is widely accepted without resistance.

Considering these points, it’s essential to remember that computers require input to function. Without data input, there can be no output—a fundamental rule akin to plants requiring water and sunlight for photosynthesis, or humans needing to read a book to gain knowledge.

To illustrate, let’s use a plant analogy. Plants perform photosynthesis using air, water, sunlight, and chlorophyll. Similarly, computers rely on algorithms (chlorophyll), commands (air), continuous power supply (sunlight), and data (water). Just as plants need these elements for growth, computers require them for operation and output generation.

As a new business owner, it’s crucial to understand that expecting output without input is futile. Learning about AI and its requirements is far more beneficial than merely complaining about it. AI can be a valuable asset to your business, and partnering with Priority Technologies Inc. can elevate your business operations with AI integration.

Next-Level Data Science: GPTs That Will Transform Your Workflow

Next-Level Data Science: GPTs That Will Transform Your Workflow

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Unlocking the Power of GPTs: Transforming Your Data Science Workflow In 2024

In the realm of data science, staying at the forefront of technological advancements is essential for driving innovation and maintaining a competitive edge. One such groundbreaking advancement is the emergence of Generative Pre-trained Transformers (GPTs), which have revolutionized the way data scientists approach natural language processing (NLP), text generation, and a myriad of other tasks.

Understanding Generative Pre-trained Transformers (GPTs)

Generative Pre-trained Transformers (GPTs) are a class of deep-learning models that leverage the transformer architecture to generate human-like text based on input prompts. These models, developed by OpenAI, are pre-trained on vast amounts of text data from the internet and fine-tuned on specific tasks using transfer learning. GPTs excel at a wide range of NLP tasks, including text generation, language translation, sentiment analysis, and more, making them versatile tools for data scientists.

Streamlining Data Preprocessing and Augmentation

One of the key advantages of GPTs is their ability to streamline data preprocessing and augmentation tasks. Traditional data preprocessing tasks, such as tokenization, stemming, and lemmatization, can be time-consuming and resource-intensive. With GPTs, data scientists can leverage pre-trained models to automate these tasks, saving time and effort in the data preparation phase. Additionally, GPTs can generate synthetic data to augment existing datasets, helping to address issues such as data scarcity and imbalance.

Enhancing Natural Language Understanding

GPTs excel at natural language understanding (NLU) tasks, thanks to their ability to generate contextually relevant responses to input prompts. Data scientists can use GPTs to perform sentiment analysis, entity recognition, and text classification tasks with remarkable accuracy. By fine-tuning pre-trained GPT models on domain-specific data, data scientists can tailor these models to specific use cases, further enhancing their performance and relevance in real-world applications.

Facilitating Text Generation and Summarization

Text generation and summarization are essential tasks in data science, with applications ranging from content creation to data summarization and report generation. GPTs excel at generating coherent and contextually relevant text based on input prompts, making them invaluable tools for automating these tasks. Data scientists can use GPTs to generate product descriptions, news articles, email responses, and more, with minimal human intervention.

Enabling Conversational AI and Chatbots

Conversational AI and chatbots have become increasingly prevalent in customer service, virtual assistants, and other applications where natural language interaction is key. GPTs can be used to power these conversational agents, enabling them to understand and respond to user queries in a human-like manner. By fine-tuning pre-trained GPT models on conversational datasets, data scientists can create chatbots that are capable of engaging in meaningful and contextually relevant conversations with users.

Pushing the Boundaries of Data Science

Overall, GPTs are pushing the boundaries of what’s possible in data science, enabling data scientists to tackle complex NLP tasks with unprecedented ease and efficiency. By leveraging pre-trained GPT models and fine-tuning them on specific tasks, data scientists can streamline their workflows, enhance their models’ performance, and unlock new opportunities for innovation and discovery. As GPTs continue to evolve and improve, they will undoubtedly play a central role in shaping the future of data science and AI.

Conclusion

Generative Pre-trained Transformers (GPTs) represent a significant advancement in the field of data science, offering unparalleled capabilities in natural language processing and text generation. By harnessing the power of GPTs, data scientists can streamline their workflows, enhance their models’ performance, and unlock new possibilities for innovation and discovery. As GPTs continue to evolve and become more accessible, they will undoubtedly transform the data science landscape and drive new breakthroughs in AI and machine learning.

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Sundar Pichai’s Vision for Tech

By Prankur Atre

In a recent candid interview, Sundar Pichai, the CEO of Google and Alphabet, shared insights into the world of technology, his vision for the future, and the responsible integration of artificial intelligence (AI) into our lives. The conversation touched upon various topics, from the challenges posed by the pandemic to the future of AI and the role of tech companies in shaping a responsible technological landscape.

Pandemic Impact and Future Visions

Pichai began by acknowledging the unexpected silver lining brought about by the pandemic. With more people working from home, the studio shared by Google with other tenants became quieter, allowing for a more focused working environment. He expressed gratitude for the opportunity to discuss the future of tech, emphasizing that at Google and Alphabet, the focus is on advancing computer science and AI for practical applications that enhance daily life.

AI and Responsible Innovation

When asked about the future of AI, Pichai stressed the importance of having a framework for application, emphasizing the company’s commitment to being “helpful” across four key attributes: knowledge, success, health, and happiness. He acknowledged the dual nature of technology, with both positive and negative aspects, and highlighted the need for transparent progress, engagement in conversations, and the evolution of frameworks over time.

The discussion also delved into the role of governments and private companies in regulating AI. Pichai advocated for a combination of efforts, emphasizing the responsibility of companies to be at the forefront of technology while acknowledging the importance of public-private partnerships and global frameworks.

Practical AI Applications in Smartphones

Pichai expressed excitement about recent developments, such as Project Douglas, which addresses issues related to representation in computational photography. He highlighted the wider applicability of features designed for accessibility, such as live captioning, extending their usefulness beyond the disabled community.

Project Lambda and Ethical Considerations

The conversation turned to Project Lambda, an AI research initiative that explores conversational aspects of AI. Pichai emphasized that it’s still in the research phase and outlined the importance of clear AI principles. He underscored the focus on using Lambda to understand language deeply, particularly in the context of conversing with Google Assistant.

Sundar Pichai’s Smartphone Preferences

A lighthearted moment in the interview was when Pichai was asked about his smartphone preferences. He shared that, currently, he uses a Pixel but regularly tests various devices, including the S21, iPhones, and others. He highlighted the compatibility of Google products across different devices.

Tech Leadership and Reflection

Reflecting on his journey from growing up in India without access to the internet to becoming the CEO of Google, Pichai emphasized the impact of technology on people’s lives. He drew inspiration from projects like the One Laptop per Child initiative and sees contemporary advancements like Chromebooks and Android phones as a continuation of that transformative journey.

The Tech CEO as a Public Figure

Addressing the evolving role of tech CEOs as public figures, Pichai acknowledged the responsibility that comes with the position. He emphasized the importance of accountability, engagement, and transparency both internally and externally. Despite the increasing celebrity-like status of tech CEOs, Pichai indicated that he doesn’t dwell too much on it in his day-to-day approach.

Legacy and Future Aspirations

Looking ahead 50 years into the future, Pichai shared his aspirations for his tech legacy. He expressed a desire to be remembered for enabling broader access to information and computing for people worldwide. Responsible AI development was highlighted as a crucial aspect of driving technological progress.

In conclusion, Sundar Pichai’s insights provide a glimpse into the intricate balance between technological innovation, responsibility, and the profound impact of AI on society. As Google and Alphabet continue to shape the tech landscape, Pichai’s vision reflects a commitment to a future where technology is not only advanced but also accessible and beneficial for all.