Artificial intelligence is certainly a hot topic right now, but what does it mean for the networking industry? In this post, Phil Gervasi looks at the role of AI and LLM in networking and separates the hype from the reality.

Large language models (LLMs) are the topic of the year. They are as complex as they are exciting, and everyone can agree they put artificial intelligence in the spotlight. Once LLms were released to the public, the hype around them grew and so did their potential use cases – LLM-based chatbots being one of them. While large language models have been available for some time, there are still a lot of challenges when it comes to building your own project.

The modern technologies we live with perform tasks that once seemed impossible and were meant only for science fiction movies. New inventions are constantly emerging, so with such an abundance of technologies, how will these affect our future online privacy? Today, we will discuss modern technologies that will seriously impact the shaping of our future. These technologies have the potential to revolutionize how our societies function.

Up until recently, machines mainly focused on analyzing large, existing amounts of data and finding patterns for a multitude of use cases. This is called “traditional AI.” But lately, machines have also started creating new content. And this is now known as “generative AI.” And given the rise of ChatGPT and its peers, generative artificial intelligence (AI) has quickly emerged as one of the most transformative technologies in recent years.

Artificial Intelligence (AI) is surely revolutionizing numerous industries. The AI market is projected to grow from $150 billion in 2023 to $1,345 billion in 2030, at a whopping 36.8% Compound Annual Growth Rate (CAGR). And at least 35% of companies are already using AI in their business, and an additional 42% are exploring it. However, the exhaustive list of AI business applications is still in the early stages.

As you’d imagine, generative AI has been a huge topic here at Grafana Labs. We’re excited about its potential role in bridging the gap between people and the beyond-human scale of observability data we work with every day. We’ve also been talking a lot about where open source fits in — especially if that Google researcher is right and OSS will outcompete OpenAI and friends. What role can we play to bring the community along?

In today's data-driven world, ensuring the stability and efficiency of software applications is not just a need but a requirement. Enter observability. But as with any evolving technology, there's always room for growth. That growth, as it stands today, is the convergence of artificial intelligence (AI) with observability pipelines. In this blog, we'll explore the idea behind this merge and its potential.

As the Australian workforce braces for increased AI and automation, individuals must adapt by acquiring new skills. In doing so, Australians can expand the impact of their work, embark on more fulfilling career paths, and increase their income potential. Recent ServiceNow-commissioned research from Pearson points to major changes in the workforce—in Australia and around the world—due to the implementation of AI and other technologies.