#AItechnologies

[govindhtech]

EPAM’s Retail Media Orchestration Toolkit and Google Cloud

EPAM’s Retail Media Orchestration Toolkit for Streamlined Ad Operations

Increase retail media success more quickly with Google Cloud and EPAM.

Retail media networks are not a novel form of advertising platform that enables merchants to sell advertising space to outside companies on their digital platforms. However, they will undergo a significant transformation in the upcoming year. As consumers’ concerns about privacy grow, they want more tailored advertising advice.

For many years, EPAM and Google Cloud have been developing retail media solutions, giving you the data and insights you need to enhance buyer experiences, improve metrics, and get more thorough and granular perspectives of your consumers.

Businesses who employ AI and gen AI and make the most use of first-party data will experience a return on investment in retail media. I am excited to inform the launch of EPAM’s Retail Media Orchestration Toolkit today, which will enable retailers of all sizes, regardless of how developed their retail media operations are, to take advantage of the opportunities that lie ahead in the upcoming year. The Retail Media Orchestration Toolkit gives you access to specialized, internal retail media operations that are coordinated with EPAM’s extensive retail knowledge and backed by Google and Google Cloud’s market-leading digital advertising capabilities.

You may significantly outperform your rivals and enhance your first-party data with previously unattainable information by utilizing Google Cloud’s AI and gen AI technologies and expertise.

Profits from retail media are still elusive

Even while many retailers have established retail media operations and are aware of the economic potential of first-party data, they still have difficulty seeing these initiatives through to maturity. Typical obstacles to optimizing retail media earnings consist of:

Marketers can choose from hundreds of retail media networks to host campaigns. This infrastructure cannot match the increased demand for data-driven insights. Companies want to spend their advertising resources on networks with comprehensive, data-driven insights, yet many retailers struggle to provide the in-depth information advertisers require. Few shops specialize in retail media; they sell products. The challenge is further compounded by the magnitude involved for many retailers.

Incapacity to deliver precise, quick measurements: Closed-loop campaign performance measurement, and in particular omnichannel measurement across various digital and physical consumer engagements, necessitates a degree of retail media technology, skill, and coordination that few merchants have.

The absence of resources and technology to enable data clean rooms Retail media are driven by consumer data. The success of advertising increases with the amount of detailed and thorough data. It’s critical to protect sensitive information, including comprehensive customer data, in order to uphold industry ethics, preserve consumer trust, and frequently comply with legal requirements. Data clean rooms offer a secure setting for several authorized individuals to use and exchange client data. However, many merchants lack the resources and knowledge necessary to maintain a data clean room, and the technological obstacles are substantial.

Data and workflow standardization challenges: Most retail media networks are made up of disparate independent software vendors (ISVs). They make use of their own protocols, guidelines, and reporting styles. As a result, incoming reporting data is constantly pouring in and needs to be converted to internal formats before being sent to advertisers. A lot of retailers try to deal with this by manually handling incoming data, which leads to more employees, worse performance when reporting to customers, and lower profitability for retail media.

Retail Media Orchestration Toolkit

Retailers can now deploy in-house, customized retail media solutions, just like Walmart, Tesco, Albertsons, and Kroger have done.

In collaboration with EPAM and Google Cloud, the Retail Media Orchestration Toolkit was created through Google Cloud’s Industry Value Network (IVN) project, utilizing ISV solutions like Moloco. With the help of the Toolkit, retailers may use their data to help their advertising clients and enhance their retail media operations.

EPAM’s extensive understanding of retail media operations stems from years of experience creating in-house, customized solutions powered by Google Cloud for some of the biggest retailers globally. Custom retail media solutions can be designed and implemented with Google Cloud’s comprehensive, end-to-end platform and solutions for audience capabilities, measurement, media execution, and innovation.

EPAM’s Toolkit, which is built on the cutting-edge Google Cloud Cortex Framework cloud-based data foundation, enables clients to make better use of their data, regardless of where it is stored. This includes first-party data from programs like Google Ad Manager, Google Search Ads 360, Display & Video 360, and others, making a true in-house, custom retail media solution a feasible option. The solution, which is a component of Google Cloud’s Industry Value Network, also makes use of ISV solutions to offer a complete and replicable solution via the pre-built connectors and accelerators.

Using the Retail Media Orchestration Toolkit, retailers benefit from:

An entirely owned, tailored, internal retail media system that is readily expandable when necessary

The capability of automating, standardizing, and streamlining retail media operations

Costly, prone to error human processes are replaced with fully automated ones.

Omnichannel measuring capabilities so they can show marketers the ROI and campaign effectiveness

Making better, data-driven decisions to optimize campaign performance across various, heterogeneous platforms

Advice on how to use cutting-edge technology like artificial intelligence (AI) and machine learning (ML) to create a solution that meets their present demands.

Significant, measurable advantages are already being felt by retailers who are utilizing the EPAM and Google Cloud retail media solution, such as:

Increases in retail media revenue and advertiser demand of 15% to 20%

Double the campaign’s performance

40% time savings

Costs of retail media activities are reduced by 12%.

Retail media success’s four stages

Four steps make up the design and implementation of an internal solution using the Retail Media Orchestration Toolkit:

Combine, standardize, automate, evaluate, and display transactional and multichannel campaign delivery data for multichannel measurement.

Superior omnichannel measurement Data from user-level interactions across platforms and channels should be tracked, reported, and examined.

Audiences: Using unique segmentation models that are built within your own cloud environment and syndicated to your retail media partners, create high-value predictive audiences based on your transactional data.

Utilize analytics to create and improve new revenue sources by drawing on brand and consumer data.

Regardless of their degree of retail media maturity, retailers were intended to profit from this staged approach. When you’re ready, you can take use of the insight-boosting potential of Google Cloud’s AI and ML capabilities. Your solution may be set up and installed to suit your unique requirements, yielding benefits practically instantly.

Prepare to expand your media efforts in retail

Due to its youth, very few, if any, organizations have fully matured their retail media activities. There is still room for improvement, even for large retailers who have created their own in-house retail media solutions. Some areas that usually lack technological maturity include optimizing workflows, creating complete automation of retail media operations, and making the most of emerging capabilities with AI and ML.

However, where is your organization now? How far along is your company in realizing the full potential of retail media? What actions are necessary to get to that point?

To address those questions, a maturity evaluation is used. It will only take two or three meetings with important members of your company to give us a broad overview of your retail media business. And it will use that information to create a customized action plan for you that includes:

A multi-year plan that addresses technology, procedures, and collaborations and is tailored to your company’s maturity level

A projection of retail media’s profit and loss that identifies important dependencies

A structure of investments and resources to help you expand more quickly

Simply put, your maturity assessment shows you how to get from where you are to where you want to be and shows you how to maximize the potential of retail media for your company.

Read more on Govindhtech.com

[ortmoragency]

Explore the evolution of AI to its next frontier and uncover the concept of Artificial Superintelligence and its implications for technology, society, and the future.

[softlabsgroup05]

Explore the cutting-edge AI technologies driving energy efficiency in factories with our insightful overview. Learn how machine learning algorithms optimize energy consumption by analyzing real-time data from sensors and smart devices. From predictive maintenance to energy usage forecasting, understand the key technologies revolutionizing energy management in manufacturing facilities. Whether you're a factory manager or an energy expert, our guide simplifies the complexities of AI integration for improved sustainability and cost savings. Stay updated with Softlabs Group for the latest advancements in AI-driven energy efficiency solutions tailored for factories.

[placement-india]

The Future of 🧑‍💼 Work: How AI is Shaping the Job Landscape

𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 is significantly transforming the Job Market. 👉While some Jobs may become obsolete, new roles will emerge that require skills in working with AI Systems, Data Analysis and Decision-Making.

AI Technologies are automating repetitive and mundane tasks, allowing employees to focus on more complex and creative work, which can lead to increased efficiency and productivity in many industries. AI is also changing how businesses operate and organizations function, impacting various industries, including coding, marketing, legal, healthcare administration and more.

Preparing for an AI-Integrated future is about a combination of upskilling, reskilling, staying informed and maintaining an adaptable mindset. While challenges are undeniable, those who are proactive in their approach to personal and professional development will be in the best position to capitalize on new opportunities.

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[otiskeene]

MyHeritage Launches OldNews.com: A New Website For Exploring Historical Newspapers

MyHeritage has recently launched OldNews.com, a brand new website that grants users access to an extensive collection of historical newspapers from all over the world. The primary objective of this site is to cater to genealogists, researchers, and history enthusiasts by providing them with the ability to search, save, and share articles pertaining to people and events throughout history. At its launch, OldNews.com boasts millions of historical newspaper pages, encompassing a diverse range of publications, including major international newspapers as well as smaller local journals and gazettes.

OldNews.com offers a user-friendly interface that facilitates easy navigation. Moreover, the content available on the site has been meticulously processed using optical character recognition (OCR) technology and enhanced with sophisticated algorithms developed by MyHeritage. This significant expansion of historical newspaper content on MyHeritage more than doubles the resources previously available, thereby offering an invaluable tool for individuals seeking to delve into captivating stories about their ancestors and gain deeper insights into historical events.

Historical newspapers are a treasure trove of detailed accounts about individuals and events, making them highly valuable for genealogists, historians, and educators alike. They provide a wealth of information, ranging from birth and marriage announcements to obituaries, sports, culture, lifestyle news, and advertisements, among other things. By granting access to a wide array of publications from the 1800s and 1900s, OldNews.com aims to assist users in uncovering unique stories and acquiring a more comprehensive understanding of historical contexts.

Read More - https://www.techdogs.com/tech-news/business-wire/myheritage-launches-oldnewscom-a-new-website-for-exploring-historical-newspapers

[cloudrevoluteus]

RPA and artificial intelligent process automation-Cloud Revolute

[webcodegenie]

You've probably heard of ChatGPT, but do you know which AI technologies are the greatest alternatives to ChatGPT? Learn more in this article.   

[ai-tech9]

Best AI Tools You Should Know💯

🚀 Ready to elevate your tech game? Discover the ultimate list of Best AI Tools You Should Know! 🤖✨

From cutting-edge innovations to game-changing applications, this article breaks down the top AI tools that can transform your productivity and spark new ideas. Whether you're a tech enthusiast, a business leader, or just curious about the future of AI, you won't want to miss this!

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Essential AI tools for various industries

How these tools can boost your efficiency

Real-world applications and success stories

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https://www.alkoptantech.com/2024/06/top-five-ai-tools-you-should-know-about.html

AITools #TechTrends #Innovation #Productivity #FutureOfAI

[rekrootinginc]

AI can aid in improving decision-making. By using machine learning algorithms to analyze thousands of data points about each prospect, such as their abilities, experience, education, and hobbies, recruiters may uncover the individuals who best fit the needs of your organization more quickly.

#airecruitment #recruitmenttrends #automatedhiring #futureofrecruitment #aitechnology #hiringprocess #recruitmentinnovation #AI

[connectinfosoftech]

Artificial Intelligence and Machine Learning Solutions by Connect Infosoft Technologies

We offer customizable AI and ML solutions tailored to meet the specific requirements of each client, ensuring maximum impact and ROI.

Let's make your business more efficient and successful with AI and ML solutions

[govindhtech]

How Open Source AI Works? Its Advantages And Drawbacks

What Is Open-source AI?

Open source AI refers to publicly available AI frameworks, methodologies, and technology. Everyone may view, modify, and share the source code, encouraging innovation and cooperation. Openness has sped AI progress by enabling academics, developers, and companies to build on each other’s work and create powerful AI tools and applications for everyone.

Open Source AI projects include:

Deep learning and neural network frameworks PyTorch and TensorFlow.

Hugging Face Transformers: Language translation and chatbot NLP libraries.

OpenCV: A computer vision toolbox for processing images and videos.

Through openness and community-driven standards, open-source AI increases accessibility to technology while promoting ethical development.

How Open Source AI Works

The way open-source AI operates is by giving anybody unrestricted access to the underlying code of AI tools and frameworks.

Community Contributions

Communities of engineers, academics, and fans create open-source AI projects like TensorFlow or PyTorch. They add functionality, find and solve errors, and contribute code. In order to enhance the program, many people labor individually, while others are from major IT corporations, academic institutions, and research centers.

Access to Source Code

Open Source AI technologies’ source code is made available on websites such as GitHub. All the instructions needed for others to replicate, alter, and comprehend the AI’s operation are included in this code. The code’s usage is governed by open-source licenses (such MIT, Apache, or GPL), which provide rights and restrictions to guarantee equitable and unrestricted distribution.

Building and Customizing AI Models

The code may be downloaded and used “as-is,” or users can alter it to suit their own requirements. Because developers may create bespoke AI models on top of pre-existing frameworks, this flexibility permits experimentation. For example, a researcher may tweak a computer vision model to increase accuracy for medical imaging, or a business could alter an open-source chatbot model to better suit its customer service requirements.

Auditing and Transparency

Because anybody may examine the code for open source AI, possible biases, flaws, and mistakes in AI algorithms can be found and fixed more rapidly. Because it enables peer review and community-driven changes, this openness is particularly crucial for guaranteeing ethical AI activities.

Deployment and Integration

Applications ranging from major business systems to mobile apps may be linked with open-source AI technologies. Many tools are accessible to a broad range of skill levels because they provide documentation and tutorials. Open-source AI frameworks are often supported by cloud services, allowing users to easily expand their models or incorporate them into intricate systems.

Continuous Improvement

Open-source AI technologies allow users to test, improve, update, and fix errors before sharing the findings with the community. Open Source AI democratizes cutting-edge AI technology via cross-sector research and collaboration.

Advantages Of Open-Source AI

Research and Cooperation: Open-source AI promotes international cooperation between organizations, developers, and academics. They lessen effort duplication and speed up AI development by sharing their work.

Transparency and Trust: Open source AI promotes better trust by enabling people to examine and comprehend how algorithms operate. Transparency ensures AI solutions are morally and fairly sound by assisting in the detection of biases or defects.

Startups: Smaller firms, and educational institutions that cannot afford proprietary solutions may employ open-source AI since it is typically free or cheap.

Developers: May customize open-source AI models to meet specific needs, improving flexibility in healthcare and finance. Open Source AI allows students, developers, and data scientists to explore, improve, and participate in projects.

Open-Source AI Security and Privacy issues: Unvetted open source projects may provide security issues. Attackers may take advantage of flaws in popular codebases, particularly if fixes or updates are sluggish.

Quality and Upkeep: Some open-source AI programs have out-of-date models or compatibility problems since they don’t get regular maintenance or upgrades. Projects often depend on unpaid volunteers, which may have an impact on the code’s upkeep and quality.

Complexity: Implementing Open Source AI may be challenging and may call for a high level of experience. Users could have trouble with initial setup or model tweaking in the absence of clear documentation or user assistance.

Ethics and Bias Issues: Training data may introduce biases into even open-source AI, which may have unforeseen repercussions. Users must follow ethical standards and do thorough testing since transparent code does not always translate into equitable results.

Commercial Competition: Open-source initiatives do not have the funds and resources that commercial AI tools possess, which might impede scaling or impede innovation.

Drawbacks

Open source AI is essential to democratizing technology.

Nevertheless, in order to realize its full potential and overcome its drawbacks, it needs constant maintenance, ethical supervision, and active community support.

Read more on Govindhtech.com

[algodocs]

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[motivaiart]

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[informatology]

"Exploring the Intersection of AI and Image Captioning: How Machines Generate Accurate and Meaningful Descriptions"

AI technology has come a long way in recent years, and one area where it has made significant progress is in image captioning. Image captioning refers to the process of generating a textual description of an image or video. In this article, we will explore how AI technology works with captioning and the different approaches used to generate captions.

Neural Networks

Neural networks are a key technology in image captioning. These networks are designed to mimic the human brain and can learn from examples and data. The networks consist of several layers of nodes, each of which performs a specific operation on the input data. For image captioning, the neural network is trained on a large dataset of images and their associated captions. The network then uses this training to generate captions for new images.

Natural Language Processing

Natural language processing (NLP) is another important technology used in image captioning. NLP is a subfield of AI that focuses on the interaction between computers and human language. It involves the analysis of language and the development of algorithms that can understand and generate natural language. In image captioning, NLP is used to generate captions that are grammatically correct and semantically meaningful.

Attention Mechanism

Attention mechanism is a technique used to improve the performance of neural networks in image captioning. It works by allowing the network to focus its attention on specific parts of the image when generating the caption. For example, if the image contains a person, the attention mechanism can direct the network to focus on the person's face when generating the caption. This helps to ensure that the generated caption is more accurate and relevant to the image.

Transfer Learning

Transfer learning is a technique that involves using a pre-trained neural network as a starting point for a new task. In image captioning, transfer learning can be used to improve the performance of the network by starting with a pre-trained network that has already been trained on a large dataset of images and captions. This allows the network to learn more quickly and accurately, reducing the amount of training time required.

In conclusion, AI technology has made significant strides in image captioning, thanks to the use of neural networks, natural language processing, attention mechanisms, and transfer learning. These technologies have enabled machines to generate captions that are accurate, meaningful, and grammatically correct. As AI technology continues to evolve, we can expect to see even more advanced image captioning systems that can understand and describe images and videos with greater accuracy and nuance.