In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "reinforcement learning": I expect this definition would prompt many to ask, "What rewards can you give a machine learning agent?" A gold star? Praise? No, the short answer is: numerical values. In reinforcement learning, rewards are crucial for training agents to make decisions that maximize their performance in a given environment.

LogicMonitor’s Generative Intelligence Solution for IT Teams Cutting-edge generative technologies have revolutionized our industry, paving the way for fresh and innovative approaches to deliver interactive and actionable experiences. At LogicMonitor, we firmly believe in leveraging these generative techniques across our platform, offering a uniquely dynamic support system for various aspects of our end-user experience.

For the past few months, we’ve been working closely with the LangChain team as they made progress on launching LangServe and LangChain Templates! LangChain Templates is a set of reference architectures to build production-ready generative AI applications. You can read more about the launch here.

Generative AI has already shown its huge potential, but there are many applications that out-of-the-box large language model (LLM) solutions aren’t suitable for. These include enterprise-level applications like summarizing your own internal notes and answering questions about internal data and documents, as well as applications like running queries on your own data to equip the AI with known facts (reducing “hallucinations” and improving outcomes).

Organizations saw a 243% ROI and $1.2 million in savings over three years In today’s complex and distributed IT environments, traditional monitoring falls short. Legacy tools often provide limited visibility across an organization’s tech stack and often at a high cost, resulting in selective monitoring. Many companies are therefore realizing the need for true, affordable end-to-end observability, which eliminates blind spots and improves visibility across their ecosystem.

I previously posted a blog that was a glossary of terms related to artificial intelligence. It included this brief definition of "generative AI": I expect for someone learning about AI, it's frustrating to read definitions of terms that include other terms you may not understand. In this case, generative adversarial networks — GANs — is probably a new term for many. This post will explain what GANs are for that reason — and also because they’re super cool.

Top tips is a weekly column where we highlight what’s trending in the tech world and list ways to explore these trends. When you think about generative AI, what instinctively comes to your mind is content and image generation. But, in this week’s Top tips column, let’s look at a less-explored facet of generative AI: data analytics. There are a lot of conversations about data and its benefits.

In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "data preprocessing": It is common for people familiar with these matters to talk about not having clean data. When dealing with AI for whatever your needs are, clean data is crucial for the quality of results. Garbage in, garbage out, as they say. So, let’s dive into what it means to have clean data.