Artificial intelligence (AI) and large language models (LLMs) have come a long way since their inception in the 1950s. From the pioneering research of English mathematician and logician Alan Turing to the recent breakthroughs achieved by models like GPT-3/GPT-4, AI has undeniably transformed industries and revolutionized human-computer interactions.

On October 30, 2023, President Joe Biden issued a landmark Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence (AI). The Executive Order represents a comprehensive statement of intent for AI regulation, mandating transparency from firms that use AI and establishing safety and security standards. Moreover, it compels AI developers to disclose the outcomes of safety evaluations to the U.S. government, especially if the results indicate a potential threat to national security.

The past year has seen an unprecedented AI hype wave triggered by the launch of OpenAI’s ChatGPT. Analysis abounds on whether the hype is real, where value will accrue and whether generative AI-first product builders have a real shot at category disruption or creation. As frenzied R&D and market activity continue unabated, market maps and take after take continue to drop hot. But what about revenue?

With its rise in popularity generative AI has emerged as a top CEO priority, and the importance of performant, seamless, and secure data management and analytics solutions to power those AI applications is essential. Cloudera Private Cloud Data Services is a comprehensive platform that empowers organizations to deliver trusted enterprise data at scale in order to deliver fast, actionable insights and trusted AI.

API pricing is important for developers and businesses alike, as it shapes strategic decisions and resource allocation. As APIs are integral to AI App developers’ frameworks , cost-value alignment in pricing ensures informed choices for organizations and customers alike, preventing unexpected financial hurdles. For AI-based API products like the ChatGPT API, pricing models must offer clarity and flexibility.

2024 is going to be an important transition year for artificial intelligence. 2023 was the public debut of generative AI and large language models (LLMs), a year of amazement, excitement, occasional panic and, yes, more than a little bit of hype. The year ahead is when businesses begin to make the promise of advanced artificial intelligence real, and we’ll begin seeing the effects on how we work and live.

In one of our recent blog posts, about six key predictions for Enterprise AI in 2024, we noted that while businesses will know which use cases they want to test, they likely won’t know which ones will deliver ROI against their AI and ML investments. That’s problematic, because in our first survey this year, we found that 57% of respondents’ boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year, while 37% expect a single-digit increase.

The greater tech community was front row for a high-stakes corporate saga this past weekend, complete with more plot twists than the Succession series finale.

Fivetran, LTIMindtree and Snowflake unveil DecisionsCX, empowering enterprises with Customer 360 for hyper-personalization.

Artificial intelligence holds the potential to enhance efficiency, streamline processes, and improve decision-making across various government sectors. But without robust safeguards, there is a heightened risk of biased decision-making, privacy breaches, and misuse of sensitive data. The Biden administration’s AI executive order establishes new standards for AI safety and security as well as for responsible use of AI at federal agencies and in state and local governments.

AI growing relevance in software testing has garnered substantial attention in today's software industry. While incorporating AI and ML approaches into software testing is not required, it is worthwhile to explore and study how these methods may deliver valuable benefits in specific elements of the testing process. With the incorporation of artificial intelligence (AI) techniques software testing will undergo a transformational transition.

It is unbelievable how AI is changing our lives by automating some of the most tedious tasks. Thanks to the fast development of AI technologies, the content marketing industry is changing on a daily basis. Here are 7 AI-driven tools that will help you improve your content quality.

Generative artificial intelligence represents a category of AI that utilizes generative models to produce text, images, or other forms of media. These models grasp the intricacies and structure of their input training data, enabling them to generate new data with similar characteristics. In insurance, generative AI plays a pivotal role in expediting digitization processes.

Large Language Models (LLMs) have now evolved to include capabilities that simplify and/or augment a wide range of jobs. As enterprises consider wide-scale adoption of LLMs for use cases across their workforce or within applications, it’s important to note that while foundation models provide logic and the ability to understand commands, they lack the core knowledge of the business. That’s where fine-tuning becomes a critical step.

The telecom industry is undergoing a monumental transformation. The rise of new technologies such as 5G, cloud computing, and the Internet of Things (IoT) is putting pressure on telecom operators to find new ways to improve the performance of their networks, reduce costs and provide better customer service. Cost pressures especially are incentivizing telecoms to find new ways to implement automation and more efficient processes to help optimize operations and employee productivity.

If you’re building an application today, one of your top product priorities for 2024 is almost certainly adding an AI copilot to your application. AI copilots – AI assistants powered by large language models (LLMs) and deeply embedded into applications – offer one of the most compelling opportunities to reimagine applications since the dawn of the internet.

We are thrilled to announce the latest addition to N|Solid Pro - the N|Solid Copilot, a groundbreaking AI-powered assistant designed to revolutionize your Node.js development experience. This innovative tool is a leap forward in Node.js application observability and security, it’s like having a Node expert on-call. View of N|Solid Pro Console with the Copilot drawer open allowing a user to interact with the AI Assistant.

Lately, AI and Machine Learning have been the most significant breakthroughs in the software testing space, and they will remain a hot topic for the foreseeable future.

Of the many things one might take for granted, access to banking and financial services may not immediately come to mind. But as a thought experiment, imagine trying to buy a home or a car without the ability to take out a loan. Try depending on cash payments from your employer, or relying on alternative banking solutions like short-term payday loans, check-cashing services, and prepaid debit cards.

Artificial Intelligence (AI) is quickly becoming the go-to for businesses looking to up their game. Its advances have led to a suite of AI-powered tools that make running a company smoother—from automating redundant tasks to whipping up stunning visuals to redefining customer service. We’ll introduce you to the best AI tools for productivity and provide strategies to integrate AI into your daily operations.

As we head into 2024, AI continues to evolve at breakneck speed. The adoption of AI in large organizations is no longer a matter of “if,” but “how fast.” Companies have realized that harnessing the power of AI is not only a competitive advantage but also a necessity for staying relevant in today’s dynamic market. In this blog post, we’ll look at AI within the enterprise and outline six key predictions for the coming year.

Generative AI is bringing a new era of “convenience” with ChatGPT, from OpenAI, taking center stage in our daily lives. From aiding in solving complex problems to generating content, this large language model has become a helpful companion for various testing-related tasks. As generative AI is becoming increasingly present in our daily lives, we should understand how to use it and account for its limitations.

OpenAI DevDay 2023 is upon us. We're excited by what some of the announcements mean for Continual and will be revealing more about Continual soon. In the meantime, you can watch the livestream and replay here: There's never been a better time to build an AI copilot for your application.

Exploratory testing is a dynamic, flexible methodology emphasizing simultaneous learning, testing strategy, and execution. Unlike traditional scripted testing, exploratory testing enables testers to actively explore software applications using their intuition, creativity, and experience. By assuming the end-user role, testers interact with the software in real-time, identifying potential issues and uncovering usability problems that scripted tests might overlook.

Generative AI is unlocking new ways to drive innovation, improve productivity and derive more value from data. For organizations to fully capitalize on this potential, it’s critical that everyone — not just those with AI expertise — is able to access and use generative AI.