#Computer vision solutions for retail

[assertai]

Heatmapping with Computer Vision Solutions for Retail

Ever wondered if you know exactly where to place those enticing promotions and displays, where to allocate the staff, or what product is to be placed where? It's not magic—it's computer vision solutions for retail at work! Heatmapping is one of the coolest tech innovations transforming the retail world, making shopping experiences smoother and more engaging for you.

What Is Heatmapping?

In a nutshell, heatmapping is a method that uses computer vision to visualize customer behavior in a store. By analyzing foot traffic and dwell times, it provides retailers with invaluable insights into how shoppers interact with various areas of the store. Imagine having a map that shows where people spend the most time—heatmapping does just that!

The Benefits of Heatmapping in Retail

Optimize Store Layout: With heatmaps, retailers can see which areas of the store attract the most attention and which areas are neglected. This data allows them to optimize store layout and product placement to enhance the shopping experience. According to a study by Forrester, 77% of retailers who use heatmapping reported an increase in sales due to improved store layout (Source: Forrester).

Boost Sales with Strategic Product Placement: By understanding customer behavior patterns, retailers can strategically position high-margin products in high-traffic areas. This strategic placement can significantly boost sales, as noted by a report from the National Retail Federation, which found that 63% of retailers saw increased sales from improved product placement (Source: NRF).

Enhance Customer Experience: Computer vision is transforming retail by making the shopping experience more intuitive. Retailers can use heatmaps to identify areas where customers face difficulties, such as crowded checkout lines or confusing store layouts. By addressing these issues, they can create a more pleasant shopping environment, leading to increased customer satisfaction and loyalty.

Efficient Staff Allocation: Heatmaps help in understanding peak shopping times and high-traffic areas. This information allows retailers to allocate staff more efficiently, ensuring that assistance is available where it's needed most. According to a survey by RetailDive, 68% of retailers using heatmaps reported improved staff efficiency (Source: RetailDive).

The Future of Heatmapping in Retail

As technology evolves, heatmapping will continue to advance, offering even more sophisticated insights. With the integration of AI and machine learning, future heatmaps will provide deeper analysis and predictive insights, further enhancing retail strategies and customer experiences.

Computer vision solutions for retail are not just a trend—they're a game-changer in how retailers understand and cater to their customers. As this technology evolves, expect even more exciting innovations to transform your shopping experience.

[gravityengineering]

AI & Machine Learning Services: Driving Innovation in Business

In today’s fast-paced digital world, AI and Machine Learning (ML) are revolutionizing industries across the globe. These technologies are no longer futuristic concepts but integral parts of modern business operations. From automating routine tasks to making data-driven decisions, AI and ML are transforming how companies operate, innovate, and compete.

Why AI & Machine Learning Matter

AI and ML empower businesses to process massive amounts of data quickly and accurately, uncovering patterns and insights that would otherwise remain hidden. This capability opens the door to smarter decision-making, predictive analytics, and automation of tasks that previously required human intervention. In industries such as healthcare, finance, retail, and manufacturing, AI and ML are enhancing efficiency, reducing costs, and enabling personalization at scale.

For example, in retail, AI-driven recommendation systems help businesses offer personalized product suggestions to customers, boosting engagement and sales. In healthcare, machine learning algorithms analyze medical data to assist in diagnosing diseases earlier and more accurately. These applications are just the beginning—AI and ML can be tailored to meet the specific needs of almost any industry.

The Role of Gravity in AI & ML Services

At Gravity Engineering, we understand the transformative potential of AI and ML and are committed to helping businesses harness these technologies to their fullest potential. Our AI & Machine Learning services are designed to address the unique challenges of our clients, offering customized solutions that drive innovation, efficiency, and growth.

We assist businesses by implementing AI and ML solutions that automate processes, improve decision-making, and uncover actionable insights from complex data. Whether it's predictive modeling, natural language processing, or computer vision, we leverage advanced algorithms to solve real-world problems. At Gravity, we also ensure that our solutions are easy to integrate with existing systems, minimizing disruption while maximizing impact.

Our expertise extends across industries, from streamlining supply chains to enhancing customer experiences. By working with Gravity, businesses gain a competitive edge through smart technologies that enable them to stay ahead in an ever-evolving market.

Unlocking New Opportunities

AI and Machine Learning are more than just tools; they represent a new era of possibilities for businesses. By embracing these technologies, companies can innovate faster, make smarter decisions, and unlock new revenue streams. At Gravity, we believe in the power of AI to create a better future, not just for businesses, but for society as a whole.

In partnering with Gravity, businesses are not just adopting new technologies; they are investing in a strategic partner that understands the “gravity” of change and is dedicated to helping them thrive in this AI-driven world.

[advertflair-3d]

The Evolution of Augmented Reality Design: From Concept to Creation

In recent years, the field of augmented reality (AR) has witnessed a remarkable evolution, transforming from a futuristic concept into a tangible and transformative technology that is shaping various industries. This shift has been greatly propelled by the innovative work of augmented reality design agencies, which have played a pivotal role in refining and actualizing AR experiences. Let's delve into the fascinating journey of AR design, exploring how it has evolved from concept to creation.

The Early Days: Conceptualizing AR

Augmented reality, as a concept, emerged with ambitious visions of overlaying digital information onto the real world through advanced technology. The earliest ideas stemmed from science fiction and speculative research, envisioning a future where digital elements seamlessly integrate with our physical environment. It was a concept that sparked the imagination of tech enthusiasts and designers alike.

Pioneering Technologies

The evolution of AR design was closely tied to the development of enabling technologies. Key milestones included the advent of smartphones with sophisticated sensors and processing power, which made AR accessible through mobile apps. Additionally, advancements in computer vision, 3D modeling, and spatial tracking systems laid the groundwork for more immersive and responsive AR experiences.

AR Design Agencies: Shaping the Landscape

As the potential of AR became apparent, specialized design agencies began to emerge, dedicated to pushing the boundaries of this technology. These agencies brought together multidisciplinary teams comprising UX/UI designers, 3D artists, software engineers, and AR specialists. Their mission: to bridge the gap between concept and reality, crafting compelling and functional AR solutions.

From Concept to Creation

The journey of an AR project typically begins with ideation and conceptualization. Design agencies collaborate closely with clients to understand objectives, target audience, and context. This phase involves sketching out user journeys, storyboarding interactions, and defining the visual style.

Next comes prototyping and iterative design. AR designers leverage tools like Unity, Unreal Engine, and specialized AR development kits to bring concepts to life in a virtual space. They refine interactions, test usability, and iterate based on feedback to ensure a seamless and engaging user experience.

Challenges and Innovations

AR design isn't without its challenges. Designers must contend with technical constraints, such as device compatibility and performance optimization, while maintaining a focus on user-centric design principles. However, these challenges fuel innovation, prompting agencies to explore novel solutions and experiment with emerging technologies like spatial computing and wearable AR devices.

The Impact on Industries

Today, AR design agencies are transforming industries across the board. From retail and marketing to healthcare and education, AR is revolutionizing how businesses engage with their customers and stakeholders. Immersive product experiences, virtual try-ons, interactive training modules—these are just a few examples of AR applications that are reshaping traditional practices.

Looking Ahead: The Future of AR Design

The evolution of augmented reality design is far from over. As technology continues to advance, we can expect even more sophisticated AR experiences that blur the lines between digital and physical realities. Design agencies will continue to lead this charge, harnessing creativity and innovation to unlock the full potential of AR across diverse sectors.

In conclusion, the evolution of augmented reality design—from its conceptual origins to its current state of innovation—demonstrates the transformative power of human imagination and technological progress. As we embrace this exciting era of AR, we can anticipate that design agencies will remain at the forefront, shaping the way we interact with and experience the world around us.

If you're considering embarking on an AR project or seeking to leverage AR for your business, partnering with a specialized augmented reality design agency can be the key to unlocking groundbreaking experiences that captivate and inspire. As we witness the evolution of AR design, one thing is certain: the future promises to be even more immersive and extraordinary than we can imagine.

[govindhtech]

Genio 510: Redefining the Future of Smart Retail Experiences

Genio IoT Platform by MediaTek

Genio 510

Manufacturers of consumer, business, and industrial devices can benefit from MediaTek Genio IoT Platform’s innovation, quicker market access, and more than a decade of longevity. A range of IoT chipsets called MediaTek Genio IoT is designed to enable and lead the way for innovative gadgets. to cooperation and support from conception to design and production, MediaTek guarantees success. MediaTek can pivot, scale, and adjust to needs thanks to their global network of reliable distributors and business partners.

Genio 510 features

Excellent work

Broad range of third-party modules and power-efficient, high-performing IoT SoCs

AI-driven sophisticated multimedia AI accelerators and cores that improve peripheral intelligent autonomous capabilities

Interaction

Sub-6GHz 5G technologies and Wi-Fi protocols for consumer, business, and industrial use

Both powerful and energy-efficient

Adaptable, quick interfaces

Global 5G modem supported by carriers

Superior assistance

From idea to design to manufacture, MediaTek works with clients, sharing experience and offering thorough documentation, in-depth training, and reliable developer tools.

Safety

IoT SoC with high security and intelligent modules to create goods

Several applications on one common platform

Developing industry, commercial, and enterprise IoT applications on a single platform that works with all SoCs can save development costs and accelerate time to market.

MediaTek Genio 510

Smart retail, industrial, factory automation, and many more Internet of things applications are powered by MediaTek’s Genio 510. Leading manufacturer of fabless semiconductors worldwide, MediaTek will be present at Embedded World 2024, which takes place in Nuremberg this week, along with a number of other firms. Their most recent IoT innovations are on display at the event, and They’ll be talking about how these MediaTek-powered products help a variety of market sectors.

They will be showcasing the recently released MediaTek Genio 510 SoC in one of their demos. The Genio 510 will offer high-efficiency solutions in AI performance, CPU and graphics, 4K display, rich input/output, and 5G and Wi-Fi 6 connection for popular IoT applications. With the Genio 510 and Genio 700 chips being pin-compatible, product developers may now better segment and diversify their designs for different markets without having to pay for a redesign.

Numerous applications, such as digital menus and table service displays, kiosks, smart home displays, point of sale (PoS) devices, and various advertising and public domain HMI applications, are best suited for the MediaTek Genio 510. Industrial HMI covers ruggedized tablets for smart agriculture, healthcare, EV charging infrastructure, factory automation, transportation, warehousing, and logistics. It also includes ruggedized tablets for commercial and industrial vehicles.

The fully integrated, extensive feature set of Genio 510 makes such diversity possible:

Support for two displays, such as an FHD and 4K display

Modern visual quality support for two cameras built on MediaTek’s tried-and-true technologies

For a wide range of computer vision applications, such as facial recognition, object/people identification, collision warning, driver monitoring, gesture and posture detection, and image segmentation, a powerful multi-core AI processor with a dedicated visual processing engine

Rich input/output for peripherals, such as network connectivity, manufacturing equipment, scanners, card readers, and sensors

4K encoding engine (camera recording) and 4K video decoding (multimedia playback for advertising)

Exceptionally power-efficient 6nm SoC

Ready for MediaTek NeuroPilot AI SDK and multitasking OS (time to market accelerated by familiar development environment)

Support for fanless design and industrial grade temperature operation (-40 to 105C)

10-year supply guarantee (one-stop shop supported by a top semiconductor manufacturer in the world)

To what extent does it surpass the alternatives?

The Genio 510 uses more than 50% less power and provides over 250% more CPU performance than the direct alternative!

The MediaTek Genio 510 is an effective IoT platform designed for Edge AI, interactive retail, smart homes, industrial, and commercial uses. It offers multitasking OS, sophisticated multimedia, extremely rapid edge processing, and more. intended for goods that work well with off-grid power systems and fanless enclosure designs.

EVK MediaTek Genio 510

The highly competent Genio 510 (MT8370) edge-AI IoT platform for smart homes, interactive retail, industrial, and commercial applications comes with an evaluation kit called the MediaTek Genio 510 EVK. It offers many multitasking operating systems, a variety of networking choices, very responsive edge processing, and sophisticated multimedia capabilities.

SoC: MediaTek Genio 510

This Edge AI platform, which was created utilising an incredibly efficient 6nm technology, combines an integrated APU (AI processor), DSP, Arm Mali-G57 MC2 GPU, and six cores (2×2.2 GHz Arm Cortex-A78& 4×2.0 GHz Arm Cortex-A55) into a single chip. Video recorded with attached cameras can be converted at up to Full HD resolution while using the least amount of space possible thanks to a HEVC encoding acceleration engine.

FAQS

What is the MediaTek Genio 510?

A chipset intended for a broad spectrum of Internet of Things (IoT) applications is the Genio 510.

What kind of IoT applications is the Genio 510 suited for?

Because of its adaptability, the Genio 510 may be utilised in a wide range of applications, including smart homes, healthcare, transportation, and agriculture, as well as industrial automation (rugged tablets, manufacturing machinery, and point-of-sale systems).

What are the benefits of using the Genio 510?

Rich input/output choices, powerful CPU and graphics processing, compatibility for 4K screens, high-efficiency AI performance, and networking capabilities like 5G and Wi-Fi 6 are all included with the Genio 510.

Read more on Govindhtech.com

[uvation]

How AI is Beneficial for Humanity in Different Sectors?

Artificial intelligence (AI) is revolutionizing the world we live in. From healthcare to finance to transportation, AI is being used to develop innovative solutions to complex problems. In this blog post, we will explore some of the most promising AI solutions and their potential impact on various industries.

Healthcare: AI is being used to develop solutions that can help doctors and researchers make more informed decisions about patient care. One example is IBM Watson, which uses natural language processing and machine learning algorithms to analyze patient data and provide personalized treatment recommendations.

Finance: AI is being used to automate financial processes and improve fraud detection. One solution is H20.ai, which uses machine learning algorithms to analyze large amounts of financial data and identify fraudulent transactions.

Transportation: AI is being used to develop autonomous vehicles that can improve road safety and reduce traffic congestion. Companies like Tesla and Waymo are using machine learning algorithms to develop self-driving cars that can navigate complex roadways.

Retail: AI is being used to personalize customer experiences and improve sales. One solution is Sentient AI, which uses deep learning algorithms to analyze customer data and provide personalized recommendations.

Manufacturing: AI is being used to improve production efficiency and quality control. One solution is Sight Machine, which uses computer vision and machine learning algorithms to monitor manufacturing processes and identify potential issues.

Education: AI is being used to personalize learning experiences and improve student outcomes. One solution is Carnegie Learning, which uses machine learning algorithms to create customized learning paths for students based on their individual strengths and weaknesses.

Agriculture: AI is being used to optimize crop yields and reduce waste. One solution is Agro Intelligence, which uses machine learning algorithms to analyze soil and weather data and provide farmers with recommendations on planting and harvesting.

In conclusion, artificial intelligence solutions is a powerful tool that can be used to develop innovative solutions to complex problems in various industries. From healthcare to finance to transportation, AI is transforming the way we live and work, and the possibilities for future developments are endless.

[winklix]

Custom Enterprise Mobile App Development Company for Tailored Business Solutions

In today’s fast-paced business environment, enterprises require tailored solutions to stay competitive and meet the evolving needs of their customers and employees. Custom mobile apps have emerged as a powerful tool for businesses, enabling them to streamline operations, enhance user experiences, and drive growth. Partnering with a reliable enterprise mobile app development company can help you unlock the full potential of your business through innovative and scalable app solutions.

Why Choose Custom Mobile Apps for Your Business?

Custom mobile apps offer numerous advantages over off-the-shelf solutions. Here’s why businesses are increasingly opting for bespoke app development:

Tailored Functionality: Custom apps are designed to meet your specific business needs, ensuring a perfect fit for your processes and goals.

Scalability: Bespoke apps can be scaled to accommodate your growing business, integrating new features as needed.

Enhanced Security: Enterprise apps are built with robust security measures to protect sensitive data and comply with industry regulations.

Improved Efficiency: Automating workflows and providing real-time insights helps businesses save time and reduce costs.

Better User Engagement: A custom app designed with your users in mind delivers a seamless and engaging experience, fostering loyalty.

Key Features of Custom Enterprise Mobile Apps

A successful enterprise mobile app is more than just functional; it’s a strategic tool that drives value. Key features include:

Integration with Existing Systems: Custom apps seamlessly integrate with your current software and tools, ensuring smooth operations.

Real-Time Analytics: Gain actionable insights through data visualization and reporting features.

Cloud-Based Solutions: Leverage the power of cloud computing for scalability and accessibility.

User-Centric Design: Prioritize intuitive navigation and engaging interfaces for a better user experience.

Offline Capabilities: Ensure app functionality even in low or no connectivity areas.

How to Choose the Right Enterprise Mobile App Development Company

Selecting the right development partner is crucial for the success of your app. Here are some factors to consider:

Experience and Expertise: Look for a company with a proven track record in enterprise app development and expertise in your industry.

Portfolio and Case Studies: Review past projects to understand their capabilities and quality of work.

Technical Proficiency: Ensure they have a skilled team proficient in the latest technologies, including AI, IoT, and cloud computing.

Customization Capability: The company should focus on tailoring solutions to your unique business needs.

Post-Launch Support: Reliable post-launch maintenance and updates are essential for app longevity.

Benefits of Partnering with a Custom App Development Company

When you work with a dedicated enterprise mobile app development company, you gain access to:

Expert Guidance: Professional developers help translate your vision into a functional and impactful app.

Faster Time-to-Market: An experienced team ensures timely delivery without compromising quality.

Cost Efficiency: Custom solutions eliminate unnecessary features, saving resources and optimizing ROI.

Future-Ready Solutions: Stay ahead of the curve with apps designed for long-term growth and adaptability.

Industries Leveraging Custom Enterprise Apps

From streamlining internal processes to enhancing customer interactions, custom mobile apps are transforming industries such as:

Healthcare: Patient management systems and telemedicine platforms.

Retail: Inventory tracking and personalized shopping experiences.

Finance: Mobile banking and secure payment gateways.

Logistics: Real-time tracking and fleet management tools.

Manufacturing: Automation and predictive maintenance solutions.

Conclusion

A custom enterprise mobile app in New York is a strategic investment that can revolutionize the way your business operates and interacts with its stakeholders. By partnering with an experienced mobile app development company, you gain a trusted ally in navigating the complexities of app creation while ensuring a solution tailored to your unique needs. Whether you’re aiming to improve operational efficiency, enhance user engagement, or drive innovation, a custom mobile app is the key to achieving your business objectives.

[globose0987]

Mastering Video Annotation: Techniques and Tools for Success

Introduction:

Video annotation plays a pivotal role in the advancement of contemporary artificial intelligence (AI) and machine learning (ML), especially within the field of computer vision. It is fundamental to applications ranging from self-driving vehicles to video content analysis, as accurate video annotation lays the groundwork for training sophisticated models capable of comprehending and interpreting visual information. This article explores the various techniques and tools necessary for excelling in video annotation, providing guidance for achieving success in your AI initiatives.

What Is Video Annotation?

Video Annotation refers to the process of labeling or tagging objects, actions, or scenes in a video on a frame-by-frame basis. These annotations generate structured data that machine learning models utilize to identify patterns and make informed predictions. The procedure demands precision, consistency, and a clear understanding of the specific objectives of the AI system being developed.

Key Uses of Video Annotation

Autonomous Vehicles: Recognizing pedestrians, other vehicles, traffic signs, and road markings.

Healthcare: Evaluating medical imaging videos for diagnostic purposes and treatment planning.

Retail: Observing customer behavior and enhancing store layouts.

Sports Analytics: Monitoring player movements and assessing game strategies.

Surveillance: Identifying anomalies and potential threats in security footage.

Methods for Efficient Video Annotation

Frame-by-Frame Annotation

This method entails annotating each frame of a video separately. Although it guarantees high precision, it can be labor-intensive and is most appropriate for projects that demand meticulous annotations.

2. Interpolated Annotation

This technique involves manually annotating key frames, with algorithms automatically interpolating the intermediate frames. This approach conserves time while achieving acceptable accuracy.

3. Object Tracking

Object tracking utilizes artificial intelligence to follow annotated objects throughout the frames, minimizing manual labor. Tools equipped with effective tracking algorithms can greatly improve efficiency.

4. Semantic Segmentation

This sophisticated technique assigns a class label to every pixel in the video, resulting in detailed and accurate annotations. It is commonly applied in fields such as medical imaging and autonomous vehicle technology.

5. Crowdsourcing

For extensive projects, annotation tasks can be allocated among a collective of workers, often via platforms like Amazon Mechanical Turk or specialized annotation services.

Video Annotation Tools

Selecting an appropriate tool can enhance the annotation workflow and increase precision. Below are some of the leading tools currently available:

Labelbox

Labelbox features an intuitive interface that supports object tracking, segmentation, and collaborative efforts. Its compatibility with well-known machine learning frameworks makes it a flexible option.

2. CVAT (Computer Vision Annotation Tool)

CVAT is an open-source solution tailored for the annotation of images and videos, offering functionalities such as automatic interpolation and object tracking.

3. Vatic

This tool specializes in frame-by-frame annotation and is frequently utilized in research initiatives that demand high accuracy.

4. SuperAnnotate

SuperAnnotate merges user-friendly annotation capabilities with project management tools, making it suitable for larger teams.

5. Annotell

Targeted at the automotive sector and other safety-sensitive fields, Annotell facilitates scalable and efficient annotation processes.

Best Practices for Achieving Success

Establish Clear Protocols: Provide annotators with comprehensive instructions to ensure uniformity in their work.

Quality Assurance: Conduct regular evaluations and utilize automated systems to identify inaccuracies.

Utilize Automation: Employ AI-driven tools to aid in repetitive tasks, thereby lessening the manual workload.

Foster Effective Collaboration: Encourage open communication among team members and stakeholders to ensure alignment on project objectives.

Iterate and Enhance: Continuously refine your annotation approach based on model outcomes and received feedback.

The Future of Video Annotation

Innovations in AI are paving the way for advanced video annotation techniques, which include:

Automated Annotation: Utilizing pre-trained models to perform data annotation with minimal human involvement.

3D Video Annotation: Expanding capabilities into three-dimensional environments for uses in virtual reality and robotics.

Real-Time Annotation: Enabling the annotation of videos instantaneously for live-streamed content.

Conclusion

Achieving proficiency in video annotation necessitates a combination of appropriate techniques, tools, and established best practices. By comprehending the unique requirements of your project and utilizing advanced tools, you can develop high-quality datasets that contribute to the success of artificial intelligence and machine learning endeavors.

Video annotation is a cornerstone of modern AI and machine learning projects, enabling precise data labeling for computer vision tasks. By mastering advanced techniques and leveraging the right tools, organizations can ensure accuracy, efficiency, and scalability in their annotation workflows. Collaborating with Globose Technology Solutions experts further elevates this process, offering industry-leading insights, tailored solutions, and cutting-edge technology to address even the most complex challenges.

[assertai]

Empowering Retail Transformation: The Impact of Computer Vision Solutions for Retail

The surge in interest towards computer vision solutions for retail sector is unsurprising, given the substantial data generation and visual feedback requirements inherent in retail operations.

According to Benchmark research, while only 3% of retail companies have adopted computer vision, a significant 40% plan to do so within the next two years.

Employing computer vision solutions for retail services allows retailers to address numerous pain points and enhance both employee and customer experiences. For instance, utilizing real-life data and heat maps for store layout improvements proves more effective than relying solely on intuition.

In today's consumer landscape, brick-and-mortar stores are expected to offer the same level of personalization and convenience as online retailers. This is driving the growing popularity of virtual reality and computer vision in retail. Features like virtual mirrors in fitting rooms elevate offline retail experiences to new levels of personalization. Additionally, self-checkout systems equipped with computer vision cameras streamline operations, while automation of routine tasks frees up time for customer-focused activities.

With the aid of machine learning consulting and an integrative approach to computer vision solutions for retail industry implementation, the digital transformation of retail companies becomes increasingly feasible.

The Rise of Computer Vision Solutions in Retail

Embracing Innovation for Competitive Advantage

The retail industry is witnessing a transformative shift with the integration of computer vision solutions. These advanced technologies utilize machine learning algorithms and real-time imaging to provide invaluable insights into customer behaviour, optimize inventory management, and streamline operations.

Optimizing Inventory Management

Efficiency through Automation

One of the key applications of computer vision solutions in retail industry is optimizing inventory management. By automating tasks such as stock monitoring, product tracking, and shelf replenishment, retailers can ensure accurate inventory counts, reduce stock outs, and improve overall operational efficiency.

Enhancing Customer Experience

Personalization and Convenience

Computer vision solutions enable retailers to offer personalized shopping experiences to customers. Through features like virtual mirrors in fitting rooms and real-time product recommendations based on customer preferences, retailers can enhance engagement and drive sales.

Improving Security and Loss Prevention

Ensuring a Safe Shopping Environment

Another crucial aspect of computer vision solutions in retail is security and loss prevention. By deploying surveillance cameras equipped with computer vision technology, retailers can detect and deter theft, minimize losses, and ensure the safety of both customers and employees.

Conclusion: Embracing the Future of Retail

Driving Growth and Innovation

As technology continues to evolve, computer vision solutions will play an increasingly vital role in shaping the future of retail. By embracing these innovative technologies, retailers can stay ahead of the competition, drive growth, and deliver exceptional customer experiences in the dynamic retail landscape.

[cleverhottubmiracle]

Ask the AI-powered “answer engine” Perplexity what’s the best handbag under $1,500 and you won’t see a grid of sponsored results or links to Reddit posts asking the same question.Instead, you’ll get a concise selection of five bags that fit the description, such as Chloé’s Kiss Small bag and Strathberry’s Mosaic, culled from online stories, blog posts and YouTube videos on the subject, as well as prices and a brief rundown of each bag’s key features. Perplexity provides links to where some bags can be purchased, and in certain instances, through its “Buy with Pro” option, can even complete checkout for you.The new shopping feature, available to paying subscribers, launched in November, and the results aren’t perfect. A query about the best zip boots for men returned one result with stiletto heels that it described as a women’s boot. Sometimes the products to buy aren’t the same as those highlighted in the research summary, and as TechCrunch discovered, letting Perplexity handle checkout can be slow, taking hours in one instance. But it illustrates how tech companies are thinking about the next way AI could transform the way consumers find and buy products online — via AI “agents” that can carry out complex tasks for users. Think of them like self-driving cars, but more open-ended in their abilities.In recent months, agents have become the next topic of excitement in AI. Sam Altman, OpenAI’s chief executive, called them “the thing that will feel like the next giant breakthrough” during a question-and-answer session on Reddit. Several of the industry’s largest players are actively developing agents, including ones to help consumers with shopping, or even do it for them.“Imagine taking a photo of something that you like, having the agent understand the style, find similar items across retailers and complete the purchase,” said Vince Koh, head of global solutions for digital commerce at Amazon’s Web Services division, or AWS. Koh described agents that could do things like use computer vision to help organise a user’s closet while learning their style through the visual data to make better product recommendations. “The biggest opportunity, I would say, is in creating seamless experiences that combine all these different types of interactions,” he added.While Perplexity took a lead in the field with its shopping launch, others aren’t far behind. Google and OpenAI are looking into potential uses of agents. Amazon is in the process of prototyping AI agents that could suggest products on its site, or even add them to a user’s shopping cart, Wired reported. In a sign of how valuable the company believes agents could be, AWS aims to provide agents as a service to customers, including brands and retailers. Salesforce already introduced its own platform for the purpose, called Agentforce, with Saks Fifth Avenue among its clients. Shopping with Perplexity. (Perplexity) If agents catch on, they have the potential to reshape online shopping. Many consumers are suffering from information overload and looking for solutions that can do the work of researching and selecting products for them. A report by Salesforce found that AI chatbots helped boost US online sales over the holidays by nearly 4 percent versus the prior year, suggesting consumers are using this simpler form of AI assistance.Agents would offer even more robust abilities. The consultancy Gartner forecasts that, by 2027, just over half of consumers will routinely shop for goods and services using AI shopping agents funded by companies that produce and distribute products.Before that happens, tech companies need to prove agents can actually improve shopping — and convince consumers to use them. It’s easier to see how someone might hand off buying commodity items like batteries, or purely functional ones like microwaves, to a bot versus fashion or beauty, where personal preferences and brand are so important.The Next Big Thing in AIThe idea of AI that shops for you sounds like science fiction, but in a sense it’s an evolution of the efforts retailers have made to remove friction from commerce, such as the one-click checkout Amazon pioneered decades ago. According to Dmitry Shevelenko, Perplexity’s chief business officer, the first phase for the company was changing search by giving people answers to their questions rather than links to read and digest themselves. With shopping, it’s taking the next step by letting consumers transact directly from those answers. Shevelenko said the percentage of shopping queries Perplexity gets has increased by “several multiples” since the launch, though he declined to provide exact numbers.Eventually, Perplexity envisions an agent that can behave proactively, knowing enough about a user to anticipate their needs and wants.“I don’t think the first manifestation of that is we’re just going to go buy something for you and it just shows up at your door,” Shevelenko said. “They start to come in the form of these nudges and notifications and pushes where it’s like, ‘Hey, you should really look at this’ … and then once you have that presented to you, you then have that one-click action [to purchase].”Some people actually enjoy shopping, however, particularly when it comes to fashion or beauty. They might not want to outsource the job to a bot. Shevelenko said the goal for agentic AI isn’t to automate away the joy in shopping but to remove the annoying parts.Google has agents in mind as well. They won’t be the solution for every problem, said Sean Scott, the company’s vice president and general manager of consumer shopping, in an emailed statement. The future of shopping is “assistive, personalised and seamless — in whatever form is most helpful,” he noted. But Google is pondering possibilities for agents like checking nearby stores for you to see if an item is in stock, or initiating a return and arranging a package pickup.Amazon, for one, believes they’ll prove valuable enough to shoppers that brands and retailers will want their own. The company is exploring how AWS can support clients in building agents, said Justin Honaman, head of worldwide retail, restaurants and consumer goods business development at Amazon. Agents don’t just have to be customer-facing, either. They could be focused on internal tasks, like analysing social-media trends or chatter and running visual analysis of inventory to find products that match, to take just one example. Obstacles to OvercomeImplementing them isn’t simple, however. For an agent to check if a product is available nearby, for example, it would need real-time inventory data for every store.“One of the things that we find challenging with legacy retailers is their systems are either not connected or not set up to enable the agent to get access to data,” Honaman said.That’s not the only challenge they face. The large language models that underlie agents work by making probabilistic predictions, without any genuine understanding of the world or their source content. Perplexity’s CEO, Aravind Srinivas, admitted to Fortune that the company doesn’t fully understand how its AI ranks and recommends products. That probabilistic nature makes LLMs prone to errors known as hallucinations. Some experts believe they’re an inherent side effect of how LLMs function, which would mean agents always display some rate of error. An agent could potentially give a shopper incorrect information, or try to purchase something that doesn’t exist. Amazon’s Koh said companies he’s spoken to are concerned about the issue, though he feels as the technology progresses the rate of hallucinations will decline. “I don’t view it as a principal barrier, and I certainly don’t view it as something that has to get managed to zero,” said Jason Goldberg, a retail expert and chief commerce strategy officer for the communications giant Publicis Groupe.Goldberg thinks companies will have to implement measures to mitigate hallucinations, but that shouldn’t prevent them from embracing the technology, which he believes is poised to have a dramatic impact on retail. Replenishment categories, like toilet paper, will be the first affected, but fashion and beauty aren’t beyond reach. An agent that could scrape social media and create a design brief based on the data would be valuable for brands, while one that could sift through the troves of information online to provide an item’s carbon footprint would be desirable for consumers. One unanswered question about shopping agents, however, is how effective they might be in categories where purchases are often emotional and not purely rational.“There’s been a lot of debate about this,” said Gartner analyst Andrew Frank. “I happen to be on the side of the debate that says, as AI learns more about these intangible preferences that people have for brands, it will learn how to reflect those.”One of the superpowers of LLMs is their ability to decipher relationships between nebulous qualities from smells to music styles, which is part of what makes them effective recommendation engines. But Frank sees other issues to be aware of if agents proliferate. “You wonder whose side these agents are really going to be on,” he said. Companies like Google, Amazon and others that shape shopping online make vast sums from advertising, which often means putting advertisers’ sponsored products at the top of search results. If agents are doing the filtering, will they do so to serve consumers or the advertisers? The answer might depend on the specific company and agent. Perplexity’s Shevelenko said, for now at least, the company isn’t even taking affiliate fees on sales because it doesn’t want users to feel like it’s pushing shopping simply to make money.“All it takes is one bad experience for people to give up on this technology,” he said. Source link

[norajworld]

Ask the AI-powered “answer engine” Perplexity what’s the best handbag under $1,500 and you won’t see a grid of sponsored results or links to Reddit posts asking the same question.Instead, you’ll get a concise selection of five bags that fit the description, such as Chloé’s Kiss Small bag and Strathberry’s Mosaic, culled from online stories, blog posts and YouTube videos on the subject, as well as prices and a brief rundown of each bag’s key features. Perplexity provides links to where some bags can be purchased, and in certain instances, through its “Buy with Pro” option, can even complete checkout for you.The new shopping feature, available to paying subscribers, launched in November, and the results aren’t perfect. A query about the best zip boots for men returned one result with stiletto heels that it described as a women’s boot. Sometimes the products to buy aren’t the same as those highlighted in the research summary, and as TechCrunch discovered, letting Perplexity handle checkout can be slow, taking hours in one instance. But it illustrates how tech companies are thinking about the next way AI could transform the way consumers find and buy products online — via AI “agents” that can carry out complex tasks for users. Think of them like self-driving cars, but more open-ended in their abilities.In recent months, agents have become the next topic of excitement in AI. Sam Altman, OpenAI’s chief executive, called them “the thing that will feel like the next giant breakthrough” during a question-and-answer session on Reddit. Several of the industry’s largest players are actively developing agents, including ones to help consumers with shopping, or even do it for them.“Imagine taking a photo of something that you like, having the agent understand the style, find similar items across retailers and complete the purchase,” said Vince Koh, head of global solutions for digital commerce at Amazon’s Web Services division, or AWS. Koh described agents that could do things like use computer vision to help organise a user’s closet while learning their style through the visual data to make better product recommendations. “The biggest opportunity, I would say, is in creating seamless experiences that combine all these different types of interactions,” he added.While Perplexity took a lead in the field with its shopping launch, others aren’t far behind. Google and OpenAI are looking into potential uses of agents. Amazon is in the process of prototyping AI agents that could suggest products on its site, or even add them to a user’s shopping cart, Wired reported. In a sign of how valuable the company believes agents could be, AWS aims to provide agents as a service to customers, including brands and retailers. Salesforce already introduced its own platform for the purpose, called Agentforce, with Saks Fifth Avenue among its clients. Shopping with Perplexity. (Perplexity) If agents catch on, they have the potential to reshape online shopping. Many consumers are suffering from information overload and looking for solutions that can do the work of researching and selecting products for them. A report by Salesforce found that AI chatbots helped boost US online sales over the holidays by nearly 4 percent versus the prior year, suggesting consumers are using this simpler form of AI assistance.Agents would offer even more robust abilities. The consultancy Gartner forecasts that, by 2027, just over half of consumers will routinely shop for goods and services using AI shopping agents funded by companies that produce and distribute products.Before that happens, tech companies need to prove agents can actually improve shopping — and convince consumers to use them. It’s easier to see how someone might hand off buying commodity items like batteries, or purely functional ones like microwaves, to a bot versus fashion or beauty, where personal preferences and brand are so important.The Next Big Thing in AIThe idea of AI that shops for you sounds like science fiction, but in a sense it’s an evolution of the efforts retailers have made to remove friction from commerce, such as the one-click checkout Amazon pioneered decades ago. According to Dmitry Shevelenko, Perplexity’s chief business officer, the first phase for the company was changing search by giving people answers to their questions rather than links to read and digest themselves. With shopping, it’s taking the next step by letting consumers transact directly from those answers. Shevelenko said the percentage of shopping queries Perplexity gets has increased by “several multiples” since the launch, though he declined to provide exact numbers.Eventually, Perplexity envisions an agent that can behave proactively, knowing enough about a user to anticipate their needs and wants.“I don’t think the first manifestation of that is we’re just going to go buy something for you and it just shows up at your door,” Shevelenko said. “They start to come in the form of these nudges and notifications and pushes where it’s like, ‘Hey, you should really look at this’ … and then once you have that presented to you, you then have that one-click action [to purchase].”Some people actually enjoy shopping, however, particularly when it comes to fashion or beauty. They might not want to outsource the job to a bot. Shevelenko said the goal for agentic AI isn’t to automate away the joy in shopping but to remove the annoying parts.Google has agents in mind as well. They won’t be the solution for every problem, said Sean Scott, the company’s vice president and general manager of consumer shopping, in an emailed statement. The future of shopping is “assistive, personalised and seamless — in whatever form is most helpful,” he noted. But Google is pondering possibilities for agents like checking nearby stores for you to see if an item is in stock, or initiating a return and arranging a package pickup.Amazon, for one, believes they’ll prove valuable enough to shoppers that brands and retailers will want their own. The company is exploring how AWS can support clients in building agents, said Justin Honaman, head of worldwide retail, restaurants and consumer goods business development at Amazon. Agents don’t just have to be customer-facing, either. They could be focused on internal tasks, like analysing social-media trends or chatter and running visual analysis of inventory to find products that match, to take just one example. Obstacles to OvercomeImplementing them isn’t simple, however. For an agent to check if a product is available nearby, for example, it would need real-time inventory data for every store.“One of the things that we find challenging with legacy retailers is their systems are either not connected or not set up to enable the agent to get access to data,” Honaman said.That’s not the only challenge they face. The large language models that underlie agents work by making probabilistic predictions, without any genuine understanding of the world or their source content. Perplexity’s CEO, Aravind Srinivas, admitted to Fortune that the company doesn’t fully understand how its AI ranks and recommends products. That probabilistic nature makes LLMs prone to errors known as hallucinations. Some experts believe they’re an inherent side effect of how LLMs function, which would mean agents always display some rate of error. An agent could potentially give a shopper incorrect information, or try to purchase something that doesn’t exist. Amazon’s Koh said companies he’s spoken to are concerned about the issue, though he feels as the technology progresses the rate of hallucinations will decline. “I don’t view it as a principal barrier, and I certainly don’t view it as something that has to get managed to zero,” said Jason Goldberg, a retail expert and chief commerce strategy officer for the communications giant Publicis Groupe.Goldberg thinks companies will have to implement measures to mitigate hallucinations, but that shouldn’t prevent them from embracing the technology, which he believes is poised to have a dramatic impact on retail. Replenishment categories, like toilet paper, will be the first affected, but fashion and beauty aren’t beyond reach. An agent that could scrape social media and create a design brief based on the data would be valuable for brands, while one that could sift through the troves of information online to provide an item’s carbon footprint would be desirable for consumers. One unanswered question about shopping agents, however, is how effective they might be in categories where purchases are often emotional and not purely rational.“There’s been a lot of debate about this,” said Gartner analyst Andrew Frank. “I happen to be on the side of the debate that says, as AI learns more about these intangible preferences that people have for brands, it will learn how to reflect those.”One of the superpowers of LLMs is their ability to decipher relationships between nebulous qualities from smells to music styles, which is part of what makes them effective recommendation engines. But Frank sees other issues to be aware of if agents proliferate. “You wonder whose side these agents are really going to be on,” he said. Companies like Google, Amazon and others that shape shopping online make vast sums from advertising, which often means putting advertisers’ sponsored products at the top of search results. If agents are doing the filtering, will they do so to serve consumers or the advertisers? The answer might depend on the specific company and agent. Perplexity’s Shevelenko said, for now at least, the company isn’t even taking affiliate fees on sales because it doesn’t want users to feel like it’s pushing shopping simply to make money.“All it takes is one bad experience for people to give up on this technology,” he said. Source link

[ellajme0]

Ask the AI-powered “answer engine” Perplexity what’s the best handbag under $1,500 and you won’t see a grid of sponsored results or links to Reddit posts asking the same question.Instead, you’ll get a concise selection of five bags that fit the description, such as Chloé’s Kiss Small bag and Strathberry’s Mosaic, culled from online stories, blog posts and YouTube videos on the subject, as well as prices and a brief rundown of each bag’s key features. Perplexity provides links to where some bags can be purchased, and in certain instances, through its “Buy with Pro” option, can even complete checkout for you.The new shopping feature, available to paying subscribers, launched in November, and the results aren’t perfect. A query about the best zip boots for men returned one result with stiletto heels that it described as a women’s boot. Sometimes the products to buy aren’t the same as those highlighted in the research summary, and as TechCrunch discovered, letting Perplexity handle checkout can be slow, taking hours in one instance. But it illustrates how tech companies are thinking about the next way AI could transform the way consumers find and buy products online — via AI “agents” that can carry out complex tasks for users. Think of them like self-driving cars, but more open-ended in their abilities.In recent months, agents have become the next topic of excitement in AI. Sam Altman, OpenAI’s chief executive, called them “the thing that will feel like the next giant breakthrough” during a question-and-answer session on Reddit. Several of the industry’s largest players are actively developing agents, including ones to help consumers with shopping, or even do it for them.“Imagine taking a photo of something that you like, having the agent understand the style, find similar items across retailers and complete the purchase,” said Vince Koh, head of global solutions for digital commerce at Amazon’s Web Services division, or AWS. Koh described agents that could do things like use computer vision to help organise a user’s closet while learning their style through the visual data to make better product recommendations. “The biggest opportunity, I would say, is in creating seamless experiences that combine all these different types of interactions,” he added.While Perplexity took a lead in the field with its shopping launch, others aren’t far behind. Google and OpenAI are looking into potential uses of agents. Amazon is in the process of prototyping AI agents that could suggest products on its site, or even add them to a user’s shopping cart, Wired reported. In a sign of how valuable the company believes agents could be, AWS aims to provide agents as a service to customers, including brands and retailers. Salesforce already introduced its own platform for the purpose, called Agentforce, with Saks Fifth Avenue among its clients. Shopping with Perplexity. (Perplexity) If agents catch on, they have the potential to reshape online shopping. Many consumers are suffering from information overload and looking for solutions that can do the work of researching and selecting products for them. A report by Salesforce found that AI chatbots helped boost US online sales over the holidays by nearly 4 percent versus the prior year, suggesting consumers are using this simpler form of AI assistance.Agents would offer even more robust abilities. The consultancy Gartner forecasts that, by 2027, just over half of consumers will routinely shop for goods and services using AI shopping agents funded by companies that produce and distribute products.Before that happens, tech companies need to prove agents can actually improve shopping — and convince consumers to use them. It’s easier to see how someone might hand off buying commodity items like batteries, or purely functional ones like microwaves, to a bot versus fashion or beauty, where personal preferences and brand are so important.The Next Big Thing in AIThe idea of AI that shops for you sounds like science fiction, but in a sense it’s an evolution of the efforts retailers have made to remove friction from commerce, such as the one-click checkout Amazon pioneered decades ago. According to Dmitry Shevelenko, Perplexity’s chief business officer, the first phase for the company was changing search by giving people answers to their questions rather than links to read and digest themselves. With shopping, it’s taking the next step by letting consumers transact directly from those answers. Shevelenko said the percentage of shopping queries Perplexity gets has increased by “several multiples” since the launch, though he declined to provide exact numbers.Eventually, Perplexity envisions an agent that can behave proactively, knowing enough about a user to anticipate their needs and wants.“I don’t think the first manifestation of that is we’re just going to go buy something for you and it just shows up at your door,” Shevelenko said. “They start to come in the form of these nudges and notifications and pushes where it’s like, ‘Hey, you should really look at this’ … and then once you have that presented to you, you then have that one-click action [to purchase].”Some people actually enjoy shopping, however, particularly when it comes to fashion or beauty. They might not want to outsource the job to a bot. Shevelenko said the goal for agentic AI isn’t to automate away the joy in shopping but to remove the annoying parts.Google has agents in mind as well. They won’t be the solution for every problem, said Sean Scott, the company’s vice president and general manager of consumer shopping, in an emailed statement. The future of shopping is “assistive, personalised and seamless — in whatever form is most helpful,” he noted. But Google is pondering possibilities for agents like checking nearby stores for you to see if an item is in stock, or initiating a return and arranging a package pickup.Amazon, for one, believes they’ll prove valuable enough to shoppers that brands and retailers will want their own. The company is exploring how AWS can support clients in building agents, said Justin Honaman, head of worldwide retail, restaurants and consumer goods business development at Amazon. Agents don’t just have to be customer-facing, either. They could be focused on internal tasks, like analysing social-media trends or chatter and running visual analysis of inventory to find products that match, to take just one example. Obstacles to OvercomeImplementing them isn’t simple, however. For an agent to check if a product is available nearby, for example, it would need real-time inventory data for every store.“One of the things that we find challenging with legacy retailers is their systems are either not connected or not set up to enable the agent to get access to data,” Honaman said.That’s not the only challenge they face. The large language models that underlie agents work by making probabilistic predictions, without any genuine understanding of the world or their source content. Perplexity’s CEO, Aravind Srinivas, admitted to Fortune that the company doesn’t fully understand how its AI ranks and recommends products. That probabilistic nature makes LLMs prone to errors known as hallucinations. Some experts believe they’re an inherent side effect of how LLMs function, which would mean agents always display some rate of error. An agent could potentially give a shopper incorrect information, or try to purchase something that doesn’t exist. Amazon’s Koh said companies he’s spoken to are concerned about the issue, though he feels as the technology progresses the rate of hallucinations will decline. “I don’t view it as a principal barrier, and I certainly don’t view it as something that has to get managed to zero,” said Jason Goldberg, a retail expert and chief commerce strategy officer for the communications giant Publicis Groupe.Goldberg thinks companies will have to implement measures to mitigate hallucinations, but that shouldn’t prevent them from embracing the technology, which he believes is poised to have a dramatic impact on retail. Replenishment categories, like toilet paper, will be the first affected, but fashion and beauty aren’t beyond reach. An agent that could scrape social media and create a design brief based on the data would be valuable for brands, while one that could sift through the troves of information online to provide an item’s carbon footprint would be desirable for consumers. One unanswered question about shopping agents, however, is how effective they might be in categories where purchases are often emotional and not purely rational.“There’s been a lot of debate about this,” said Gartner analyst Andrew Frank. “I happen to be on the side of the debate that says, as AI learns more about these intangible preferences that people have for brands, it will learn how to reflect those.”One of the superpowers of LLMs is their ability to decipher relationships between nebulous qualities from smells to music styles, which is part of what makes them effective recommendation engines. But Frank sees other issues to be aware of if agents proliferate. “You wonder whose side these agents are really going to be on,” he said. Companies like Google, Amazon and others that shape shopping online make vast sums from advertising, which often means putting advertisers’ sponsored products at the top of search results. If agents are doing the filtering, will they do so to serve consumers or the advertisers? The answer might depend on the specific company and agent. Perplexity’s Shevelenko said, for now at least, the company isn’t even taking affiliate fees on sales because it doesn’t want users to feel like it’s pushing shopping simply to make money.“All it takes is one bad experience for people to give up on this technology,” he said. Source link

[chilimili212]

Ask the AI-powered “answer engine” Perplexity what’s the best handbag under $1,500 and you won’t see a grid of sponsored results or links to Reddit posts asking the same question.Instead, you’ll get a concise selection of five bags that fit the description, such as Chloé’s Kiss Small bag and Strathberry’s Mosaic, culled from online stories, blog posts and YouTube videos on the subject, as well as prices and a brief rundown of each bag’s key features. Perplexity provides links to where some bags can be purchased, and in certain instances, through its “Buy with Pro” option, can even complete checkout for you.The new shopping feature, available to paying subscribers, launched in November, and the results aren’t perfect. A query about the best zip boots for men returned one result with stiletto heels that it described as a women’s boot. Sometimes the products to buy aren’t the same as those highlighted in the research summary, and as TechCrunch discovered, letting Perplexity handle checkout can be slow, taking hours in one instance. But it illustrates how tech companies are thinking about the next way AI could transform the way consumers find and buy products online — via AI “agents” that can carry out complex tasks for users. Think of them like self-driving cars, but more open-ended in their abilities.In recent months, agents have become the next topic of excitement in AI. Sam Altman, OpenAI’s chief executive, called them “the thing that will feel like the next giant breakthrough” during a question-and-answer session on Reddit. Several of the industry’s largest players are actively developing agents, including ones to help consumers with shopping, or even do it for them.“Imagine taking a photo of something that you like, having the agent understand the style, find similar items across retailers and complete the purchase,” said Vince Koh, head of global solutions for digital commerce at Amazon’s Web Services division, or AWS. Koh described agents that could do things like use computer vision to help organise a user’s closet while learning their style through the visual data to make better product recommendations. “The biggest opportunity, I would say, is in creating seamless experiences that combine all these different types of interactions,” he added.While Perplexity took a lead in the field with its shopping launch, others aren’t far behind. Google and OpenAI are looking into potential uses of agents. Amazon is in the process of prototyping AI agents that could suggest products on its site, or even add them to a user’s shopping cart, Wired reported. In a sign of how valuable the company believes agents could be, AWS aims to provide agents as a service to customers, including brands and retailers. Salesforce already introduced its own platform for the purpose, called Agentforce, with Saks Fifth Avenue among its clients. Shopping with Perplexity. (Perplexity) If agents catch on, they have the potential to reshape online shopping. Many consumers are suffering from information overload and looking for solutions that can do the work of researching and selecting products for them. A report by Salesforce found that AI chatbots helped boost US online sales over the holidays by nearly 4 percent versus the prior year, suggesting consumers are using this simpler form of AI assistance.Agents would offer even more robust abilities. The consultancy Gartner forecasts that, by 2027, just over half of consumers will routinely shop for goods and services using AI shopping agents funded by companies that produce and distribute products.Before that happens, tech companies need to prove agents can actually improve shopping — and convince consumers to use them. It’s easier to see how someone might hand off buying commodity items like batteries, or purely functional ones like microwaves, to a bot versus fashion or beauty, where personal preferences and brand are so important.The Next Big Thing in AIThe idea of AI that shops for you sounds like science fiction, but in a sense it’s an evolution of the efforts retailers have made to remove friction from commerce, such as the one-click checkout Amazon pioneered decades ago. According to Dmitry Shevelenko, Perplexity’s chief business officer, the first phase for the company was changing search by giving people answers to their questions rather than links to read and digest themselves. With shopping, it’s taking the next step by letting consumers transact directly from those answers. Shevelenko said the percentage of shopping queries Perplexity gets has increased by “several multiples” since the launch, though he declined to provide exact numbers.Eventually, Perplexity envisions an agent that can behave proactively, knowing enough about a user to anticipate their needs and wants.“I don’t think the first manifestation of that is we’re just going to go buy something for you and it just shows up at your door,” Shevelenko said. “They start to come in the form of these nudges and notifications and pushes where it’s like, ‘Hey, you should really look at this’ … and then once you have that presented to you, you then have that one-click action [to purchase].”Some people actually enjoy shopping, however, particularly when it comes to fashion or beauty. They might not want to outsource the job to a bot. Shevelenko said the goal for agentic AI isn’t to automate away the joy in shopping but to remove the annoying parts.Google has agents in mind as well. They won’t be the solution for every problem, said Sean Scott, the company’s vice president and general manager of consumer shopping, in an emailed statement. The future of shopping is “assistive, personalised and seamless — in whatever form is most helpful,” he noted. But Google is pondering possibilities for agents like checking nearby stores for you to see if an item is in stock, or initiating a return and arranging a package pickup.Amazon, for one, believes they’ll prove valuable enough to shoppers that brands and retailers will want their own. The company is exploring how AWS can support clients in building agents, said Justin Honaman, head of worldwide retail, restaurants and consumer goods business development at Amazon. Agents don’t just have to be customer-facing, either. They could be focused on internal tasks, like analysing social-media trends or chatter and running visual analysis of inventory to find products that match, to take just one example. Obstacles to OvercomeImplementing them isn’t simple, however. For an agent to check if a product is available nearby, for example, it would need real-time inventory data for every store.“One of the things that we find challenging with legacy retailers is their systems are either not connected or not set up to enable the agent to get access to data,” Honaman said.That’s not the only challenge they face. The large language models that underlie agents work by making probabilistic predictions, without any genuine understanding of the world or their source content. Perplexity’s CEO, Aravind Srinivas, admitted to Fortune that the company doesn’t fully understand how its AI ranks and recommends products. That probabilistic nature makes LLMs prone to errors known as hallucinations. Some experts believe they’re an inherent side effect of how LLMs function, which would mean agents always display some rate of error. An agent could potentially give a shopper incorrect information, or try to purchase something that doesn’t exist. Amazon’s Koh said companies he’s spoken to are concerned about the issue, though he feels as the technology progresses the rate of hallucinations will decline. “I don’t view it as a principal barrier, and I certainly don’t view it as something that has to get managed to zero,” said Jason Goldberg, a retail expert and chief commerce strategy officer for the communications giant Publicis Groupe.Goldberg thinks companies will have to implement measures to mitigate hallucinations, but that shouldn’t prevent them from embracing the technology, which he believes is poised to have a dramatic impact on retail. Replenishment categories, like toilet paper, will be the first affected, but fashion and beauty aren’t beyond reach. An agent that could scrape social media and create a design brief based on the data would be valuable for brands, while one that could sift through the troves of information online to provide an item’s carbon footprint would be desirable for consumers. One unanswered question about shopping agents, however, is how effective they might be in categories where purchases are often emotional and not purely rational.“There’s been a lot of debate about this,” said Gartner analyst Andrew Frank. “I happen to be on the side of the debate that says, as AI learns more about these intangible preferences that people have for brands, it will learn how to reflect those.”One of the superpowers of LLMs is their ability to decipher relationships between nebulous qualities from smells to music styles, which is part of what makes them effective recommendation engines. But Frank sees other issues to be aware of if agents proliferate. “You wonder whose side these agents are really going to be on,” he said. Companies like Google, Amazon and others that shape shopping online make vast sums from advertising, which often means putting advertisers’ sponsored products at the top of search results. If agents are doing the filtering, will they do so to serve consumers or the advertisers? The answer might depend on the specific company and agent. Perplexity’s Shevelenko said, for now at least, the company isn’t even taking affiliate fees on sales because it doesn’t want users to feel like it’s pushing shopping simply to make money.“All it takes is one bad experience for people to give up on this technology,” he said. Source link

[oliviajoyice21]

Ask the AI-powered “answer engine” Perplexity what’s the best handbag under $1,500 and you won’t see a grid of sponsored results or links to Reddit posts asking the same question.Instead, you’ll get a concise selection of five bags that fit the description, such as Chloé’s Kiss Small bag and Strathberry’s Mosaic, culled from online stories, blog posts and YouTube videos on the subject, as well as prices and a brief rundown of each bag’s key features. Perplexity provides links to where some bags can be purchased, and in certain instances, through its “Buy with Pro” option, can even complete checkout for you.The new shopping feature, available to paying subscribers, launched in November, and the results aren’t perfect. A query about the best zip boots for men returned one result with stiletto heels that it described as a women’s boot. Sometimes the products to buy aren’t the same as those highlighted in the research summary, and as TechCrunch discovered, letting Perplexity handle checkout can be slow, taking hours in one instance. But it illustrates how tech companies are thinking about the next way AI could transform the way consumers find and buy products online — via AI “agents” that can carry out complex tasks for users. Think of them like self-driving cars, but more open-ended in their abilities.In recent months, agents have become the next topic of excitement in AI. Sam Altman, OpenAI’s chief executive, called them “the thing that will feel like the next giant breakthrough” during a question-and-answer session on Reddit. Several of the industry’s largest players are actively developing agents, including ones to help consumers with shopping, or even do it for them.“Imagine taking a photo of something that you like, having the agent understand the style, find similar items across retailers and complete the purchase,” said Vince Koh, head of global solutions for digital commerce at Amazon’s Web Services division, or AWS. Koh described agents that could do things like use computer vision to help organise a user’s closet while learning their style through the visual data to make better product recommendations. “The biggest opportunity, I would say, is in creating seamless experiences that combine all these different types of interactions,” he added.While Perplexity took a lead in the field with its shopping launch, others aren’t far behind. Google and OpenAI are looking into potential uses of agents. Amazon is in the process of prototyping AI agents that could suggest products on its site, or even add them to a user’s shopping cart, Wired reported. In a sign of how valuable the company believes agents could be, AWS aims to provide agents as a service to customers, including brands and retailers. Salesforce already introduced its own platform for the purpose, called Agentforce, with Saks Fifth Avenue among its clients. Shopping with Perplexity. (Perplexity) If agents catch on, they have the potential to reshape online shopping. Many consumers are suffering from information overload and looking for solutions that can do the work of researching and selecting products for them. A report by Salesforce found that AI chatbots helped boost US online sales over the holidays by nearly 4 percent versus the prior year, suggesting consumers are using this simpler form of AI assistance.Agents would offer even more robust abilities. The consultancy Gartner forecasts that, by 2027, just over half of consumers will routinely shop for goods and services using AI shopping agents funded by companies that produce and distribute products.Before that happens, tech companies need to prove agents can actually improve shopping — and convince consumers to use them. It’s easier to see how someone might hand off buying commodity items like batteries, or purely functional ones like microwaves, to a bot versus fashion or beauty, where personal preferences and brand are so important.The Next Big Thing in AIThe idea of AI that shops for you sounds like science fiction, but in a sense it’s an evolution of the efforts retailers have made to remove friction from commerce, such as the one-click checkout Amazon pioneered decades ago. According to Dmitry Shevelenko, Perplexity’s chief business officer, the first phase for the company was changing search by giving people answers to their questions rather than links to read and digest themselves. With shopping, it’s taking the next step by letting consumers transact directly from those answers. Shevelenko said the percentage of shopping queries Perplexity gets has increased by “several multiples” since the launch, though he declined to provide exact numbers.Eventually, Perplexity envisions an agent that can behave proactively, knowing enough about a user to anticipate their needs and wants.“I don’t think the first manifestation of that is we’re just going to go buy something for you and it just shows up at your door,” Shevelenko said. “They start to come in the form of these nudges and notifications and pushes where it’s like, ‘Hey, you should really look at this’ … and then once you have that presented to you, you then have that one-click action [to purchase].”Some people actually enjoy shopping, however, particularly when it comes to fashion or beauty. They might not want to outsource the job to a bot. Shevelenko said the goal for agentic AI isn’t to automate away the joy in shopping but to remove the annoying parts.Google has agents in mind as well. They won’t be the solution for every problem, said Sean Scott, the company’s vice president and general manager of consumer shopping, in an emailed statement. The future of shopping is “assistive, personalised and seamless — in whatever form is most helpful,” he noted. But Google is pondering possibilities for agents like checking nearby stores for you to see if an item is in stock, or initiating a return and arranging a package pickup.Amazon, for one, believes they’ll prove valuable enough to shoppers that brands and retailers will want their own. The company is exploring how AWS can support clients in building agents, said Justin Honaman, head of worldwide retail, restaurants and consumer goods business development at Amazon. Agents don’t just have to be customer-facing, either. They could be focused on internal tasks, like analysing social-media trends or chatter and running visual analysis of inventory to find products that match, to take just one example. Obstacles to OvercomeImplementing them isn’t simple, however. For an agent to check if a product is available nearby, for example, it would need real-time inventory data for every store.“One of the things that we find challenging with legacy retailers is their systems are either not connected or not set up to enable the agent to get access to data,” Honaman said.That’s not the only challenge they face. The large language models that underlie agents work by making probabilistic predictions, without any genuine understanding of the world or their source content. Perplexity’s CEO, Aravind Srinivas, admitted to Fortune that the company doesn’t fully understand how its AI ranks and recommends products. That probabilistic nature makes LLMs prone to errors known as hallucinations. Some experts believe they’re an inherent side effect of how LLMs function, which would mean agents always display some rate of error. An agent could potentially give a shopper incorrect information, or try to purchase something that doesn’t exist. Amazon’s Koh said companies he’s spoken to are concerned about the issue, though he feels as the technology progresses the rate of hallucinations will decline. “I don’t view it as a principal barrier, and I certainly don’t view it as something that has to get managed to zero,” said Jason Goldberg, a retail expert and chief commerce strategy officer for the communications giant Publicis Groupe.Goldberg thinks companies will have to implement measures to mitigate hallucinations, but that shouldn’t prevent them from embracing the technology, which he believes is poised to have a dramatic impact on retail. Replenishment categories, like toilet paper, will be the first affected, but fashion and beauty aren’t beyond reach. An agent that could scrape social media and create a design brief based on the data would be valuable for brands, while one that could sift through the troves of information online to provide an item’s carbon footprint would be desirable for consumers. One unanswered question about shopping agents, however, is how effective they might be in categories where purchases are often emotional and not purely rational.“There’s been a lot of debate about this,” said Gartner analyst Andrew Frank. “I happen to be on the side of the debate that says, as AI learns more about these intangible preferences that people have for brands, it will learn how to reflect those.”One of the superpowers of LLMs is their ability to decipher relationships between nebulous qualities from smells to music styles, which is part of what makes them effective recommendation engines. But Frank sees other issues to be aware of if agents proliferate. “You wonder whose side these agents are really going to be on,” he said. Companies like Google, Amazon and others that shape shopping online make vast sums from advertising, which often means putting advertisers’ sponsored products at the top of search results. If agents are doing the filtering, will they do so to serve consumers or the advertisers? The answer might depend on the specific company and agent. Perplexity’s Shevelenko said, for now at least, the company isn’t even taking affiliate fees on sales because it doesn’t want users to feel like it’s pushing shopping simply to make money.“All it takes is one bad experience for people to give up on this technology,” he said. Source link

[gempertsindia]

GempertsIndia The Best Image Visioning Company in Lucknow for Cutting-Edge Technology

In today's rapidly evolving technological landscape, image visioning stands as a cornerstone of innovation. From advanced AI-powered systems to real-time data analysis, the demand for exceptional image visioning services has skyrocketed. Among the frontrunners in this space is GempertsIndia, widely recognized as the best image visioning company in Lucknow for its cutting-edge technology and unmatched expertise.

What Makes GempertsIndia Stand Out?

GempertsIndia has emerged as a trailblazer in image visioning by consistently delivering innovative solutions tailored to meet the dynamic needs of various industries. Their expertise encompasses state-of-the-art technology, skilled professionals, and an unwavering commitment to excellence. Here’s why GempertsIndia leads the field:

1. Cutting-Edge AI Integration

GempertsIndia leverages advanced AI algorithms to process, analyze, and interpret visual data with unparalleled accuracy. Their AI-driven systems ensure high precision in object detection, pattern recognition, and image classification, making them a trusted partner for businesses requiring sophisticated visual solutions.

2. Customizable Solutions

Recognizing that every industry has unique requirements, GempertsIndia offers bespoke image visioning solutions. From healthcare to retail, manufacturing to agriculture, their tailored services cater to diverse sectors, addressing specific needs and challenges.

3. Highly Skilled Team

At the core of GempertsIndia’s success lies a team of experienced professionals who are adept at blending creativity with technology. Their proficiency in machine learning, computer vision, and software development ensures exceptional results.

4. Advanced Infrastructure

With state-of-the-art tools and infrastructure, GempertsIndia handles complex image visioning projects efficiently. Their robust technological framework supports seamless integration with existing systems, enhancing operational efficiency for their clients.

Services Offered by GempertsIndia

GempertsIndia provides a wide array of image visioning services that empower businesses to thrive in a competitive environment. Here are some of their key offerings:

1. Image Recognition

Using deep learning algorithms, GempertsIndia excels in identifying objects, faces, and patterns in images, making it an invaluable asset for security, e-commerce, and marketing industries.

2. Real-Time Video Analysis

Their real-time video analysis solutions enable businesses to monitor and evaluate visual data instantaneously, ensuring quicker decision-making and enhanced security measures.

3. Medical Imaging Solutions

GempertsIndia's advanced imaging technology aids in medical diagnostics by providing precise analysis of X-rays, MRIs, and CT scans, revolutionizing the healthcare industry in Lucknow and beyond.

4. Smart Surveillance Systems

For industries focusing on safety and monitoring, GempertsIndia designs intelligent surveillance systems that integrate image visioning with predictive analytics.

5. Retail and E-Commerce Solutions

By integrating image visioning into inventory management, virtual try-ons, and customer behavior analysis, GempertsIndia enhances the shopping experience for consumers while boosting operational efficiency.

Industries Benefiting from GempertsIndia's Image Visioning Expertise

GempertsIndia has carved a niche for itself by serving a broad spectrum of industries with its innovative image visioning services:

Healthcare: Assisting in accurate diagnostics through medical imaging.

Retail: Revolutionizing customer experience with personalized recommendations.

Manufacturing: Improving quality control through automated defect detection.

Agriculture: Enhancing crop monitoring and yield prediction using satellite imagery.

Security: Elevating surveillance systems with real-time threat detection.

Why Choose GempertsIndia for Image Visioning in Lucknow?

When it comes to image visioning, GempertsIndia offers a host of benefits that set them apart from competitors. Here’s why businesses trust GempertsIndia.

1. Proven Track Record

With numerous successful projects under their belt, GempertsIndia has demonstrated its ability to deliver high-quality solutions consistently.

2. Competitive Pricing

Despite offering premium services, GempertsIndiaensures affordability, making cutting-edge technology accessible to businesses of all sizes.

3. Local Expertise with Global Standards

Based in Lucknow, GempertsIndia combines a deep understanding of local market dynamics with globally recognized technological standards.

4. Exceptional Customer Support

GempertsIndia prides itself on offering 24/7 customer support, ensuring seamless project execution and client satisfaction.

5. Sustainability Focus

By integrating eco-friendly practices into their operations, GempertsIndia contributes to a sustainable future while delivering innovative solutions.

Success Stories: Transforming Businesses with Image Visioning

Several businesses in Lucknow and across India have benefited from GempertsIndia’s image visioning expertise. A prominent example is a leading retail chain that partnered with GempertsIndia to implement AI-powered inventory management. This collaboration not only reduced wastage but also enhanced customer satisfaction by ensuring product availability at all times.

Another success story comes from the healthcare sector, where GempertsIndia’s medical imaging solutions helped a hospital achieve faster and more accurate diagnoses, significantly improving patient outcomes.

Future Prospects: GempertsIndia’s Vision for Tomorrow

As technology continues to advance, GempertsIndia aims to remain at the forefront of innovation. Their future plans include:

Expanding Service Offerings: Incorporating more AI-driven tools for augmented and virtual reality applications.

Global Reach: Extending their services to international markets while strengthening their presence in Lucknow.

Research and Development: Investing in R&D to explore new possibilities in image visioning and related technologies.

Conclusion: Your Go-To Image Visioning Partner in Lucknow

For businesses seeking reliable, innovative, and efficient image visioning solutions, GempertsIndia is undoubtedly the best choice. Their cutting-edge technology, skilled professionals, and customer-centric approach make them a trusted name in Lucknow and beyond.

Whether you are in healthcare, retail, or any other industry, GempertsIndia’s tailored solutions can transform the way you utilize visual data. Embrace the future of image visioning with GempertsIndia and take your business to new heights.

Contact GempertsIndia today to explore how their expertise can benefit your business!

FAQs

1. What industries does GempertsIndia serve?

GempertsIndia caters to various industries, including healthcare, retail, manufacturing, agriculture, and security.

2. How does GempertsIndia ensure the quality of its services?

GempertsIndia employs a team of experts and cutting-edge technology to deliver precise and reliable image visioning solutions.

3. Can GempertsIndia handle custom image visioning projects?

Yes, GempertsIndia specializes in offering customized solutions tailored to the specific needs of its clients.

4. Where is GempertsIndia located?

GempertsIndia is based in Lucknow but offers services to clients across India.

5. What sets GempertsIndia apart from its competitors?

Their focus on innovation, competitive pricing, and exceptional customer support makes GempertsIndia a leader in image visioning.

Source Link: https://davidjef.livepositively.com/gempertsindia-the-best-image-visioning-company-in-lucknow-for-cutting-edge-technology

[assertai]

Computer Vision for Retail: Unlocking the power of Inventory Visibility Software

Summary: Delve into the realm of computer vision for retail and discover how it intersects with inventory visibility solutions, revolutionizing the retail landscape by enhancing inventory management, optimizing operations, and delivering superior customer experiences.

Introduction:

Are you ready to witness the future of retail unfold before your eyes? Imagine a world where inventory management isn't just a mundane task but a seamless, efficient process powered by cutting-edge technology. Welcome to the era of computer vision for retail, where every item on the shelf is meticulously tracked and managed with precision.

Unveiling CCTV Based Inventory Management with Alpha:

Assert AI, a trailblazer in the realm of retail innovation. Their CCTV-based inventory visibility software, Alpha, is revolutionizing the way retailers handle their stock. Utilizing the latest advancements in computer vision and artificial intelligence, Alpha provides unparalleled accuracy and efficiency in inventory tracking, enabling retailers to stay ahead of the curve in today's competitive market.

Harnessing the Power of Computer Vision for Retail:

Asset AI's Alpha software goes beyond traditional inventory management systems by harnessing the power of AI. Through advanced image recognition algorithms, Alpha accurately tracks stock movements, identifies out-of-stock items, and detects theft or tampering in real-time. This proactive approach not only improves inventory accuracy but also enables retailers to make data-driven decisions to optimize their supply chain and enhance the shopping experience for customers.ne are the days of manual stock counting and human error. With computer vision, retailers can now automate the tedious task of inventory management with remarkable precision. By analysing live footage from CCTV cameras, Alpha identifies products on the shelves in real-time, ensuring that stock levels are always up-to-date and accurate.

Enhancing Inventory Visibility with Artificial Intelligence:

 Alpha doesn't stop at just tracking inventory. Powered by artificial intelligence, this innovative software goes beyond mere surveillance, offering comprehensive insights into product performance, consumer behaviour, and market trends. By harnessing the power of AI, retailers can optimize their inventory management strategies and make data-driven decisions to maximize profitability.

Trends and Stats:

According to a recent study by Grand View Research, the global market for computer vision in retail is projected to reach $17.4 billion by 2027, with a compound annual growth rate (CAGR) of 47.6%. This exponential growth is fuelled by the increasing demand for AI-driven solutions to address the challenges faced by retailers, such as inventory management, customer engagement, and loss prevention.

Are you ready to embrace the future of retail?

With computer vision and artificial intelligence leading the way, the possibilities are endless. Whether you're a small boutique or a multinational corporation, harnessing the power of technology like Alpha can propel your business to new heights. The question is: are you ready to take the leap?