In the realm of technology, fields such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and Data Science have witnessed remarkable advancements that shape the future of our world. However, as these industries flourish, the lack of diversity within their ranks has become glaringly apparent. At Cigniti, we believe in ‘building bridges and embracing togetherness’ and are committed to Diversity, Equity, and Inclusion in technology.

Ever get overwhelmed by a chatty best friend asking a lot of questions? That's what the ChatGPT API "Over the Rate Limit" error is like. It's telling you, "Woah, slow down! Let me take a sip of water before we continue." In this guide, we will shed light on the rate limit and show you several ways to make your API requests more efficient to ensure you face as few interruptions as possible.

Today we're excited to officially launch AI Explain on ThoughtSpot for Sheets, the ultimate cheat code for data literacy and exploration. AI Explain integrates Google's PaLM 2 LLM, specifically leveraging the Bison model to automatically generate the top data stories for any visualization created with our Sheets extension.

ChatGPT is like that cool, incredibly talented artist every band wants to collaborate with. When you need something extra, something that will give your application a bit of zing, that's where ChatGPT comes in. It's got that rhythm that gets feet tapping. This guide will take you step-by-step on how to bring together the text-generating wizardry of ChatGPT with the simplicity and efficiency of Golang, opening up exciting new possibilities for crafting more intelligent applications.

The nursing profession is in crisis. According to McKinsey, over 30% of surveyed nurses said they may leave their current patient care jobs in the next year, and for inpatient nurses it’s higher at 45%. Meanwhile, the average professional tenure of nurses dropped from 3.6 years to 2.8 years between 2020 and 2023. These alarming trends have healthcare systems on red alert. Ninety-four percent of surveyed health system senior executives said the nursing shortage is critical.

Imagine harnessing the power of ChatGPT's human-like responses right from your Java code! It'd be like adding a supercharger to your already powerful engine. This step-by-step, hands-on guide (with full example code at the end) will show you exactly how to integrate, interact, and leverage this union so you can build smarter applications. Before you know it, you'll have Java and ChatGPT playing nice to deliver a user experience that's nothing short of mind-blowing.

Is the generative AI honeymoon over already? After months of buzz around its transformative possibilities, excitement is now starting to be tampered by a growing concern on trust and data privacy. Just in the last few weeks, there have been several lawsuits launched against AI companies, including a well publicized charge of copyright infringement.

If you’re not using artificial intelligence (AI) in your organization right now, you’re behind. But the reality is that beyond inputting some ideas into a large language model like ChatGPT, AI just isn’t that simple to operationalize across your business (although the benefits are real).

We all know that organizations face a huge challenge in extracting valuable insights from vast amounts of data. Chief Data Officers (CDOs) and Chief Data Analytics Officers (CDAOs) play a key role in this process, as they are responsible for managing and leveraging organizational data to drive sustainable and responsible growth. One technology that has revolutionized the way they unlock value from business data is generative artificial intelligence (AI).

The world of knowledge work is undergoing a profound transformation as generative AI emerges as a powerful force driving innovation, efficiency, and productivity. With its ability to analyze vast amounts of data, generate insights, and streamline complex tasks, generative AI is reshaping the way professionals work and unlocking new possibilities. It also raises fears of replacing knowledge workers with Generative AI.

In today’s rapidly evolving technological landscape, generative artificial intelligence (AI) has emerged as a powerful tool for various industries, and it seems like enterprises are fast to adopt it. Generative AI refers to the use of machine learning algorithms to generate original and creative content such as images, text, or music.

ChatGPT is a champ, but sometimes, it needs a breather. Here's what you need to know about the Model is Overloaded error and how to give it that breather.

As artificial intelligence (AI) changes industries at a dizzyingly rapid pace, industries and governments alike are just beginning to grapple with the implications of the groundbreaking technology. One major issue has come to the foreground: data privacy concerns. Between possible data breaches and companies using your data to train their own models (and perhaps helping your competition in the process), enterprises have concerns.

This blog series covers how to run, train, fine-tune, and deploy large language models securely inside your Snowflake Account with Snowpark Container Services This year there has been a surge of progress in the world of open source large language models (LLMs). This world of free and open source LLMs took yet another major step forward just this week with Meta’s release of Llama v2.

Ensuring the reliability, functionality, and overall quality of software applications has become increasingly crucial. Quality assurance plays a vital role in achieving these objectives by implementing systematic processes and techniques to evaluate and enhance software quality. As technology continues to advance at a rapid pace, new and innovative approaches are emerging to tackle the challenges of software quality.

At ThoughtSpot, we believe making data accessible to every knowledge worker requires human-centered technology—an analytics experience that bridges the “language” barrier between technology and people. AI is the perfect compliment to search because it empowers organizations to analyze, understand, and act on data.

Are you tired of the Prompt too Long error interrupting your flow with ChatGPT? Let's crack this nut together and learn how to keep your prompts lean, mean, and error-free.

By Noam Harel In the ever-changing landscape of the pharmaceutical industry, the integration of generative artificial intelligence (AI) holds immense promise and potential alongside risk, patient and consumer safety and tight regulation. Generative AI refers to the ability of machines to autonomously create new and unique content, ideas, or solutions.

By Noam Harel In the fast-paced and ever-evolving business landscape, innovation has become the lifeblood of success. Yet, many organizations fail to harness the full potential of innovation due to a significant gap between their business units. This gap, like a hidden chasm, prevents the sharing of best practices, stifling growth and hindering progress.

In the span of only a few months, AI has reshaped the landscape of almost every industry around the world in both positive and negative ways. Indeed, there is still a lot of room for improvement for this groundbreaking technology, but if businesses don’t embrace it, they’re sure to be left behind. In the QA industry, “AI testing” will become the norm in the next few years, bringing incredible advancements in the way we think and do software testing.

A deep learning framework is essential for building and deploying deep neural networks, a class of artificial intelligence algorithms inspired by the human brain’s structure and functioning. These frameworks provide a high-level interface, libraries and tools that simplify the development process of complex machine-learning models. Choosing the proper deep learning framework is crucial as it directly impacts your machine learning projects’ efficiency, productivity, and success.

As generative AI continues to captivate attention with its transformative potential, there is a danger that traditional AI and ML become overshadowed. But as I mentioned in my last blog, this would be a mistake as traditional AI methods still hold immense value and relevance, and likely more so than generative AI in the near term.

One of the largest health insurance providers in the United States uses Unravel to ensure that its business-critical data applications are optimized for performance, reliability, and cost in its development environment—before they go live in production. Data and data-driven statistical analysis have always been at the core of health insurance.

In our data-driven world, the landscape of product analytics is rapidly evolving. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), we're seeing a seismic shift in how businesses approach product development and enhancement. But how does AI and ML fit into product analytics, particularly for non-technical business leaders and marketers? And more importantly, what does this mean for the future? ‍

Generative AI stands out from other technological breakthroughs due to its remarkable velocity and unprecedented speed. In a matter of mere months since its initial emergence in the limelight, this cutting-edge innovation has already achieved scalability, aiming to attain substantial return on investment. However, it is imperative to effectively harness this formidable technology, ensuring that it can deploy on a large scale and yield outcomes that garner trust from your business stakeholders.