Tag: AItech Article

Generative AI has swiftly become popular among marketers and has the potential to grow to a $1.3 trillion industry in the next 10 years. OpenAI’s ChatGPT is just one growth example—rocketing to over 100 million users in just two months of its release.

Many have hailed generative AI as a process-changing tool that can quickly produce swaths of content with minimal human intervention, drastically scaling content production. That’s the claim anyway. But as AI becomes more prevalent, its use in content production opens several questions — does generative AI actually produce quality content? Can it match what human marketers can produce?

With the digital landscape already saturated with content, marketers in the AI era need to fully understand the strengths and weaknesses of current generative tools so they can build (and protect) high-quality connections with their audiences.

Human-generated content beat out AI-generated content in every category.

Though the AI tools had strengths in some areas, no one tool mastered multiple criteria across our tests. When it comes to accuracy, readability, and brand style and tone, the AI tools could not reach the level of quality that professional content writers provided. It also lacked the authenticity of human-written content.

The lesson: Brands and marketers must keep humans at the center of content creation.

Unsurprisingly, AI is not the end-all-be-all solution for creating content that truly connects with human audiences.  

Yes, AI is an efficient and capable tool that marketers can leverage to supercharge specific content tasks. Using AI for tasks such as research, keyword analysis, brainstorming, and headline generation may save content creators money, time, and effort.

Even so, marketers should prioritize humanity in their writing. AI can only give us an aggregate of the staid writing available across the internet. But highly skilled human writers are masters of contextualization, tapping into the subtleties of word choice and tone to customize writing to specific audiences.

As some have pointed out, quantity can never win out over quality.

In the race to adopt AI tools, we must remember what makes content valuable and why it connects with human audiences. The online marketing landscape is becoming increasingly competitive, and brands can’t risk the ability to build trusting connections with consumers in their rush to streamline workflows. Ultimately, humans must remain the central focus as brands invest in unique and authentic content that connects.

To Know More, Read Full Article @ https://ai-techpark.com/ai-vs-human-content-quality/

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In this fast-paced digital world, organizations are relying on event-driven architecture (EDA) that facilitates real-time responses, flexibility, and scalability in their business systems.

To understand EDA is a software design practice that structures a system’s segments to respond to, produce, and process events. For instance, any event creates a significant change in state within a system that is further triggered by external characteristics, such as user activities, sensor inputs, and other systems.

The rise of microservices is one cause for the prompt adoption of event-driven API (EDAs) management. These EDAs are centralized to this architecture, allowing data exchange through different events that aid in optimizing performance, ensuring scalability, and maintaining seamless integration between various services and applications.

In this article, we will explore the top five EDAs that enable developers and businesses to stay ahead of the evolving landscape of real-time interactions.

Apache Kafka

The first event-driven API on our list is Apache Kafka, which is an open-source, distributed streaming solution that allows developers to publish, subscribe to, and process streams of events in real time. Kafka has excelled in handling large data sets in real-time, even in low latency, which makes it an ideal solution for messaging and event sourcing. This API is also known for its high fault tolerance via its distributed architecture, guaranteeing that even in the case of node failure, data is not lost. However, Kafka lacks built-in authorization for features such as message filtering or priority queues, which are essential in some event-driven use cases and can be a major drawback while setting up distributed systems. Even though Apache Kafka is open-source and free to use, it has a paid version, which is called Confluent Cloud, that offers a fully managed data transfer service with pricing starting at $0.10 per GB for storage.

Gravitee

Even though Gravitee is an open-source API management platform, it offers event-driven API capabilities that support synchronous and asynchronous API lifecycles and security. Gravitee is known for its user-friendly interface, which simplifies the API management process, allowing developers to deploy only the components they need and reducing unnecessary complexity. Apart from that, Gravitee reinforces event-driven protocols such as WebSockets and Server-Sent Events (SSE), making it an ideal choice for businesses transitioning into EDA. However, Gravitee struggles with performance issues, particularly with high-throughput events, which eventually lags in documentation. For additional enterprise editions, Gravitee charges $1,500 per month; however, the pricing may increase on add-on custom services and API traffic volume.

To Know More, Read Full Article @ https://ai-techpark.com/event-driven-api-management-in-2024/

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The growing importance of agility and operational efficiency has helped introduce serverless solutions as a revolutionary concept in today’s data processing field. This is not just a revolution, but an evolution that is changing the whole face of infrastructure development and its scale and cost factors on an organizational level. Overall, For companies that are trying to deal with the issues of big data, the serverless model represents an enhanced approach in terms of the modern requirements to the speed, flexibility, and leveraging of the latest trends.

Understanding Serverless Architecture

Working with serverless architecture, we can state that servers are not completely excluded in this case; instead, they are managed outside the developers’ and users’ scope. This architecture enables developers to be detached from the infrastructure requirements in order to write code. Cloud suppliers such as AWS, Azure, and Google Cloud perform the server allocation, sizing, and management.

The serverless model utilizes an operational model where the resources are paid for on consumption, thereby making it efficient in terms of resource usage where resources are dynamically provisioned and dynamically de-provisioned depending on the usage at any given time to ensure that the company pays only what they have consumed. This on-demand nature is particularly useful for data processing tasks, which may have highly varying resource demands.

Why serverless for data processing?

Cost Efficiency Through On-Demand Resources

Old school data processing systems commonly involve the provision of systems and networks before the processing occurs, thus creating a tendency to be underutilized and being resource intensive. Meanwhile, server-less compute architectures provision resources in response to demand, while IaaS can lock the organization in terms of the cost of idle resources. This flexibility is especially useful for organizations that have prevaricating data processing requirements.

In serverless environments, cost is proportional to use; this means that the costs will be less since one will only be charged for what they use, and this will benefit organizations that require a lot of resources some times and very few at other times or newly start-ups. This is a more pleasant concept than servers that are always on, with costs even when there is no processing that has to be done.

To Know More, Read Full Article @ https://ai-techpark.com/serverless-architectures-for-cost-effective-scalable-data-processing/

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Access to real-time data and insights has become critical to decision-making processes and for delivering customised user experiences. Industry newcomers typically go to market as ‘real-time’ natives, while more established organisations are mostly at some point on the journey toward full and immediate data capability. Adding extra horsepower to this evolution is the growth of ‘mobile-first’ implementations, whose influence over consumer expectations remains formidable.

Nonetheless, sole reliance on real-time data presents challenges, challenges that predominantly circle matters of interpretation and accuracy.

In this article, we explore why inaccurate real-time data and analytics transpire, explain the commonplace misinterpretation of both, and look at some of the tools that help businesses progress toward true real-time data competency.

The Risks of Using Imperfect, Legacy, and Unauthorised Real-Time Data and Analytics

Businesses risk misdirecting or misleading their customers when they inadvertently utilise imperfect or legacy data to create content. Despite real-time capability typically boosting the speed and accessibility of enterprise data, mistakes that deliver inappropriate services can undermine customer relationships.

Elsewhere, organisations invite substantial risk by using data without proper authorisation. Customers will often question how a company knows so much about them when they are presented with content that’s obviously been put together using personal details they didn’t knowingly share. When such questions turn to suspicion, the likelihood of nurturing positive customer relationships shrinks.

Misinterpreting Data and the AI ‘Hallucination’ Effect

Real-time data’s speed and accessibility are also impeded when full contexts are absent and can lead to organisations making hasty and incongruent decisions. Moreover, if the data is deficient from the start, misinterpretation of it becomes rife.

Today, the risks of flawed data and human oversight are exacerbated by a novel problem. Generative AI technology is known to ‘hallucinate’ when fed with incomplete datasets. At significant risk to the organisation, these large language models fill any gaps by inventing information.

To Know More, Read Full Article @ https://ai-techpark.com/real-time-data-and-analytics/

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