For a long time, AI was almost exclusively the plaything of science fiction writers, where humans push technology too far, to the point it comes alive and — as Hollywood would have us believe — starts to wreak havoc. Cheery stuff! However, in recent years, we’ve seen an explosion of AI and machine learning technology that, so far, has shown us a fun side with people using AI for creating, planning, and ideating in a big way.

In recent years, artificial intelligence (AI) has emerged as a pivotal technology to advance cloud computing, driving innovation and greater efficiency. Among the myriad of AI services available, the Azure OpenAI Service, born from the collaboration between Microsoft and OpenAI, stands out for its robust capabilities and seamless integration with cloud environments.

Every day the world is becoming increasingly powered by artificial intelligence. In fact, you’d struggle to find tech companies that have not announced AI integrations into their tech stack in one way or another. Cynics might say this is a passing phase, but the reason AI is so popular is that it’s a versatile set of capabilities that can help solve a lot of problems.

If you’re working with AI, you’re working with data. From numerical data to videos or images, regardless of your industry or use case, every AI project depends on data in some form. The question is: how can you efficiently store that data and use it when building your models? One answer is PostgreSQL, a proven and well-loved database that, thanks to recent developments, has become a strong choice to support AI.

In our ongoing series of blogs “Unravelling the AI mystery” Digitate continues to explore advances in AI and our experiences in turning AI and GenAI theory into practice. The blogs are intended to enlighten you as well as provide perspective into how Digitate solutions are built.

In the digital age, uptime monitoring has become a cornerstone of business operations, ensuring websites and servers are always accessible to users. It's not just about keeping the lights on; it's about preserving reputation, ensuring customer satisfaction, and minimizing revenue loss. Enter Artificial Intelligence (AI), a game-changer in the way we approach uptime monitoring.

I wrote a previous blog post, "AI Explainer: What's Our Vector, Victor?," to scratch the surface on vector databases, which play a crucial role in supporting applications in machine learning, information retrieval and similarity search across diverse domains. From that blog arose the topic of embeddings, which I addressed in a subsequent post, "AI Explainer: Demystifying Embeddings." In explaining embeddings, the notion of continuous space was presented, which is the topic of this blog.

Let's cut through the AI fantasy. You've read the headlines: artificial intelligence is the new electricity, the magic wand transforming businesses overnight. But in my experience working with many clients with InvGate, I've gained some valuable insights. Turns out it’s not all smoke and mirrors. Let's dive into the real story of AI in business, far from the echo chamber of Silicon Valley buzzwords and closer to the tangible reality of everyday corporate life.

In the log aggregation game, the biggest difficulty you face can be setting up parsing rules for your logs. To qualify this statement: simply getting log files into Graylog is easy. Graylog also has out-of-the-box parsing of a wide variety of common log sources, so if your logs fall into one of the many categories of log for which there is either a dedicated Input; a dedicated Illuminate component; or that uses a defined Syslog format; then yes, parsing logs is also easy.