Generative artificial intelligence (AI) is a game-changer, bringing with it the promise of unparalleled efficiency and the potential for entrée into new markets. As generative AI continues to soar in popularity, organizations are eager to tap into its transformative power. However, this enthusiasm should come with a side of caution. It’s critical that organizations develop a strong generative AI policy so the allure of new technology doesn’t lead to devastating mishaps.

Have you heard the phrase “AI won't replace humans - but humans with AI will replace humans without AI”? I personally love this quote because it perfectly encapsulates the nature of the anticipated workforce shift from the rise of generative AI. As I wrote back in 2017, the power of AI is not about machines supplanting human abilities, but rather about a symbiotic relationship between humans and AI. I think the Star Trek analogy I used then is standing the test of time…

While AI and machine learning have been industry buzzwords for a while, they are now becoming fundamental to software testing. Machine learning algorithms are making it easier to sift through logs, identify patterns, and even predict where bugs are most likely to occur. As these technologies mature, the role of AI in software testing will undoubtedly expand.

Setting up and running a successful ecommerce store has never been easier. Tools like Shopify have always kept things simple, but the rise of AI has made it extremely straightforward for anyone to create various assets required to run a successful ecommerce store. These include landing pages, images, ads, and so many other things. The rise in AI has led to the introduction of hundreds and thousands of new SaaS tools.

Artificial intelligence (AI) has taken the world by storm. ChatGPT was the ultimate proof of concept, demonstrating the power of large language models and AI in easy-to-understand terms. So naturally, business leaders are eager to unlock the productivity benefits that come from integrating AI into business operations. But despite their eagerness, organizations still need to do some work to prepare for AI integration.

First published on the BSA site along with DXC Technology Data analytics and generative AI are revolutionizing the approach organizations take to software development, testing, and delivery, enabling standardization and scalability across the board, including test automation and management. Like other sectors, financial institutions struggle to match the rate of transformation necessary to maintain a competitive edge.

Although it might seem a little early, I was just thinking: what will 2023 be remembered for? For many it will be the year that Beyonce and Taylor Swift took to stages around the world and pushed the boundaries of live music (I’m a confirmed Swiftie if you didn’t know). It is also the year of AI. When I speak with customers, they all talk about how they are steering towards AI adoption.

We're delighted to unveil Choreo TestGPT, Choreo's innovative approach to API testing powered by Generative Pre-trained Transformer (GPT) 3.5 large language model (LLM) from Azure OpenAI. This allows users to test APIs running or proxied via Choreo using natural language, serving as a complementary tool to traditional testing methods. It offers a convenient way to quickly test endpoints without delving deeply into API specifications or crafting intricate JSON payloads.

Generative AI seems like it's shaking things up for process automation, like other industries. But in reality, artificial intelligence is less of a shake-up and more of a natural complement to the capabilities that support a process automation initiative. Imagine a world where AI can turn a PDF into a digital interface, or sort all the emails in an inbox and generate responses for an employee to review.

Generative AI is a powerful tool for accelerating the branding process for new products or compounds.

Artificial intelligence (AI) has led to a seismic shift in the business landscape, largely due to the surge in popularity of large language models like ChatGPT. From predictive models that foster better decision-making to generative AI code tools that enable teams to build applications faster, AI offers incredible benefits to organizations. Businesses need to embrace this technology or risk falling behind their competitors.

Data is essential to marketing. It’s how we know our audience and measure campaign outcomes. It shows us where to adjust a campaign on the fly, for even better results. But working with data is increasingly complex, and having the right stack of technologies is invaluable.

Artificial intelligence has the potential to make work incredibly efficient—which means it’s the perfect complement to process automation technology. Process automation, and related approaches like business process management, already aim to improve productivity by automating what can and should be automated.

What are the differences between generative AI vs. large language models? How are these two buzzworthy technologies related? In this article, we’ll explore their connection. To help explain the concept, I asked ChatGPT to give me some analogies comparing generative AI to large language models (LLMs), and as the stand-in for generative AI, ChatGPT tried to take all the personality for itself.

Large language models (LLMs) have recently garnered immense popularity and global attention due to their versatile applications across various industries. The advent of ChatGPT in late 2022, particularly resonating with Gen Z, exemplifies their impressive capabilities. Nowadays, the cumbersome process of navigating automated phone menus (pressing 1 or 2) for customer support is becoming less desirable, with chatbots like Siri and Alexa offering a more user-friendly alternative.

Snowpark is the set of libraries and runtimes that enables data engineers, data scientists and developers to build data engineering pipelines, ML workflows, and data applications in Python, Java, and Scala. Functions or procedures written by users in these languages are executed inside of Snowpark’s secure sandbox environment, which runs on the warehouse.

There is no doubt about it: Artificial Intelligence (AI) and Machine Learning (ML) has changed the way we think about software testing. Ever since the introduction of the disruptive AI-powered language model ChatGPT, a wide range of AI-augmented technologies have also emerged, and the benefits they brought surely can’t be ignored. In this article, we will guide you to leverage AI/ML in software testing to bring your QA game to the next level.

We’ve got something truly special in store for you. We reached out to our expansive testing community, consisting of 40,000 testers, and posed a question about leveraging GPT prompts for various software testing scenarios and tips for effective prompting. The response was nothing short of astounding, and today, we’re thrilled to bring you the incredible insights we gathered. Prepare to be amazed as we unveil 15+ best ChatGPT prompts for software testing enthusiasts like you.

Companies want to train and use large language models (LLMs) with their own proprietary data. Open source generative models such as Meta’s Llama 2 are pivotal in making that possible. The next hurdle is finding a platform to harness the power of LLMs. Snowflake lets you apply near-magical generative AI transformations to your data all in Python, with the protection of its out-of-the-box governance and security features.