Following our well-received presentations with Walgreens’ Andy Kettlewell at this year’s NRF and RILA LINK shows, we were fortunate to present another testimonial breakout session—this time from a CPG perspective—at Gartner’s annual Supply Chain Symposium/Xpo in Orlando. One of the things I’m especially proud of since the beginning of antuit.ai is the number of customers who’ve been eager to come forward and share their positive experiences with us.

In a previous blog post, we built a small Python application that queries Elasticsearch using a mix of vector search and BM25 to help find the most relevant results in a proprietary data set. The top hit is then passed to OpenAI, which answers the question for us. In this blog, we will instrument a Python application that uses OpenAI and analyze its performance, as well as the cost to run the application.

Generative Artificial Intelligence (AI) is commanding conversations these days, a never-before-seen system that’s captured many millions of users since its debut in November 2022. A machine learning innovation that creates content of all kinds (and that’s just the beginning), generative AI also comes up with new product designs and optimizes business processes. We have only begun to exploit and understand this disruption.

At Traceloop, we’re solving the single thing engineers hate most: writing tests for their code. More specifically, writing tests for complex systems with lots of side effects, such as this imaginary one, which is still a lot simpler than most architectures I’ve seen: As you can see, when an API call is made to a service, there are a lot of things happening asynchronously in the backend; some are even conditional.

Generative AI projects like ChatGPT have motivated enterprises to rethink their AI strategy and make it a priority. In a report published by PwC, 72% of respondents said they were confident in the ROI of artificial intelligence. More than half of respondents also state that their AI projects are compliant with applicable regulations (57%) and protect systems from cyber attacks, threats or manipulations (55%). Production-grade AI initiatives are not an easy task.

Earlier this month, we released the first version of our new natural language querying interface, Query Assistant. People are using it in all kinds of interesting ways! We’ll have a post that really dives into that soon. However, I want to talk about something else first. There’s a lot of hype around AI, and in particular, Large Language Models (LLMs).

Honeycomb recently released our Query Assistant, which uses ChatGPT behind the scenes to build queries based on your natural language question. It's pretty cool. While developing this feature, our team (including Tanya Romankova and Craig Atkinson) built tracing in from the start, and used it to get the feature working smoothly. Here's an example. This trace shows a Query Assistant call that took 14 seconds. Is ChatGPT that slow? Our traces can tell us!

Azure OpenAI is a service for deploying AI applications on Azure resources. With its easy-to-use REST APIs, you can leverage the service to access OpenAI’s powerful language models, such as ChatGPT, for your applications while taking advantage of the reliability and security of the Azure platform. Datadog already offers an out-of-the-box integration for OpenAI so you can monitor key performance trends, such as API usage patterns, token consumption, and more.

Five worthy reads is a regular column on five noteworthy items we have discovered while researching trending and timeless topics. This week, we explore the amalgamation of the rapidly evolving world of artificial intelligence (AI) in cryptocurrency. Designed by Dhanwant Kumar The world of cryptocurrency has come a long way since the introduction of Bitcoin in 2009. Today, there are thousands of cryptocurrencies available, each with unique features and scenarios.

In this blog post, we will explore the importance of scalability, automation, and AI in the evolving landscape of infrastructure monitoring. We will examine how Netdata's innovative solution aligns with these emerging trends, and how it can empower organizations to effectively manage their modern IT infrastructure.

People have been using ChatGPT for various reasons over the past few months. Though the results aren’t flawless, they are unquestionably impressive. Its usefulness has been particularly noticeable for repetitive and time-consuming tasks. However, people have become apprehensive about their job security, which has hindered their ability to fully utilize ChatGPT to their advantage.

The public sector is investing heavily on artificial intelligence and machine learning initiatives. Deloitte AI Institute reported that 60% of government AI and data analytics investments aim to directly impact real-time operational decisions and outcomes by 2024. From automating redundant tasks to increasing the quality of services offered to citizens, public sector institutions have a wide range of applications where they could implement AI.

While organizations are quickly adopting AI to automate tasks and improve operations, it’s important to consider the security risks associated with integrating AI into your company’s processes and software. AI not only brings an opportunity to increase efficiency but also introduces additional risks to your organization if not used responsibly — just like a recent example from Samsung has shown.

In today’s economic headwinds, organizations are looking for ways to make workers more productive and drive operational efficiency. Generative AI—where machines “understand” human language and respond to and act on it—can make that happen. That's why I’m excited to announce ServiceNow® Generative AI Controller and Now Assist for Search.

You probably have been hearing a lot about automation and artificial intelligence (AI) these days, with a vision of some kind of AI-driven world that will take all of our jobs away. The reality is that there’s always too much work to do. AI and automation are more likely to help people get their jobs done more efficiently rather than take them away. Basic automation can have large returns for the network – and improve the quality of work.

Network monitoring is a challenging job because networks continue to evolve to meet ever-changing client requirements. Businesses today heavily depend on their networks, and even a short outage can lead to penalties and lost profits. This is why your monitoring tool must also transform itself to not only scale as you grow but offer new features that address new challenges posed by the increasing usage demands placed on your network.

Customer expectations aren’t what they used to be. Emerging technology trends have elevated the customer experience by bringing personalization and automation to the forefront. With these strides, companies have increased their customers’ expectations ten-fold. To meet rising customer expectations, the only solution is AI. While we’re only scratching the surface, here are a few ways AI can help improve your customer engagement.

The use of statistics, advanced algorithms and AI/Ml is becoming omnipresent. The benefits are visible in every walk of life, from web searches, to movie and retail recommendations, to auto-completing our emails. Of course, not many anticipated the dramatic entrance of generative AI in the form of ChatGPT for writing college essays and poetry on arcane topics.

OpenAI is an AI research and development company whose products include the GPT family of large language models. Since the introduction of GPT-3 in 2020, these models’ fluent and adaptable processing of both natural language and code has propelled their rapid adoption across diverse fields. GPT-4, ChatGPT, and InstructGPT are now used extensively in software development, content creation, and more.

Digital code behind a digitized face Welcome back to our series with Nicholas Wegman, Ph.D., Senior Director of Artificial Intelligence and Alex Barnes, Senior Director of Product Management, as they continue to discuss the science behind Artificial Intelligence (AI) and reveal how it can specifically increase retail/CPG margin. In the second part of our three-part series, we’ll delve deeper into the data science of AI, and why it’s not working for every business.

Seems like everyone is building a ChatGPT thing right now, doesn’t it? Well we are too! Inspired by so many others, we decided to see what AI could do with our simplified analytics and observability data. Turns out, it can do quite a lot. I’m thrilled to share that we’ve shipped our first AI insights chatbot, Professor Sloth.

The release of ChatGPT in November of 2022 elicited excitement from all corners of the internet. It could write code, diagnose patients, ace exams, write books and more — all in a matter of seconds. Yet, many people were left underwhelmed by the results. Inputting “write a blog post about…” resulted in bland and formulaic articles no one wanted to read. The AI doomers could breathe a sigh of relief as it became apparent AI wasn’t coming for tech jobs any time soon.

ChatGPT is quickly being adopted for optimizing pretty much any vertical. You can write reports, come up with content, analyze data, and even get the tool to write code for you. Gartner predicts that by 2025, the AI market would be worth about $134 billion. Thankfully, many businesses are embracing this technology instead of acting hostile. This is a good thing because there is a multitude of ways in which enterprises can leverage ChatGPT.

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.