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Not All Natural Language Query (NLQ) Models Are Created Equal: Part 3 - Power BI Q&A

In part one of this series, we discussed the evolution of Yellowfin’s Guided NLQ solution and focused on aspects of Guided NLQ that stand apart from the competition. In part two, we then compared Guided NLQ to Sisense's equivalent NLQ solution, Sisense Simply Ask. In part three, we will look deeper at another competitor’s NLQ offering, Microsoft Power BI and its Q&A feature.

Red Hat + Cloudera | A Hybrid Data Platform for Generative AI for FSI

Red Hat and Cloudera have joined forces to enable customers to take advantage of the cloud with full confidence, especially in the financial services industry, where data protection is critical. Red Hat Payment Industry Lead, Ramon Villarreal describes how collaborating with Cloudera provides leading financial services organizations with data resiliency, performance and expedited time to market as they leverage the cloud to move and manipulate massive amounts of data.

Generative AI vs. Large Language Models: What's the Difference?

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.

Game Developers Weigh In on Talent Shortages and Generative AI in Perforce Annual State of Game Development Report

Perforce Software announces release of the 2023 State of Game Development & Design Report. The annual industry report, conducted together with Incredibuild, the leading software development acceleration platform, gathers insights across AAA and Indie game development professionals to provide perspective on the current state of the gaming industry, emerging tools and platforms, along with opportunities that lie ahead.

Securing the Future: Navigating Risks with Large Language Models (LLMs)

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.

Securely Connect to LLMs and Other External Services from Snowpark

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.

A Complete Guide To AI/ML Software Testing

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.

15+ Best ChatGPT Prompts for Software Testing

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.

Snowpark ML: The 'Easy Button' for Open Source LLM Deployment in Snowflake

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.