Artificial intelligence (AI) and machine learning (ML) are two cutting-edge technologies that are revolutionizing the field of website development. AI refers to the ability of computers to perform tasks that typically require human intelligence, such as recognizing speech, understanding natural language, and making decisions based on data. On the other hand, ML is a subset of AI that involves training algorithms to learn from data and make predictions or decisions based on that learning.

There is rapid adoption of artificial intelligence (AI) and machine learning (ML) in the finance sector. AI in banking is reshaping client experiences, including communication with financial service providers (for example, chat bots). Banks are exploring ways to use AI/ML to handle the high volume of loan applications and to improve their underwriting process.

We know that for many retailers and CPG companies, AI/ML solutions represent a game-changing technology. Yet, this journey seldom comes without a few expectable “growing pains”—from adoption and scale through a fully-fledged data-driven transformation. For multiple internal stakeholders across an organization, the end-to-end process can seem quite daunting—especially without a well-defined plan.

Many partners I’ve spoken with are experimenting with ChatGPT to automate script generation, write proposals etc—you name it, it’s being tried. But even in a budget-conscious economy, it’s still not worth sacrificing the human element. This is especially true if that move results in script errors, unreliable information, introducing cybersecurity risks, or compromising your company’s intellectual property. You still need a human involved in the coding process today.

ChatGPT has been the talk of the town for more than four months now. As the first ever artificial intelligence (AI) -powered chatbot, it has quickly gained immense popularity, helping students, engineers and even executives generate content, write and debug code and run market analyses. But could ChatGPT be used for anything other than natural language processing (NPL)? Could it, for example, assist businesses with strategic decision-making? I decided to try it out.

In the next two posts (maybe more) I'll share how we have developed elmah.io's email templates currently sent out using Amazon Web Services (AWS). This first post will introduce template development using MJML and Handlebars.js. In the next post, I'll explain the process of building them on Azure DevOps and deploying them to AWS.

Recent advances in AI-powered chatbot technology will change the way humans interact with applications and machines. AI will likely boost current IT Service Management (ITSM) chatbot capabilities and directly affect how knowledge management will be provided in the future. Let’s look at how AI in knowledge management is affecting content creation, management, and access.

Introduction to Netdata's new visualisation providing AI Insights, supporting Rapid Diagnostics.