OpenAI’s Big Announcement: Why Enterprises Should Pay Attention

In this contributed article, Chandini Jain, founder and CEO of Auquan, discusses how RAG AI represents a major breakthrough for making generative AI viable for knowledge-intensive tasks in the enterprise. It combines the power of retrieval-based models (access to real-time data + domain-specific data) with generative models (natural language responses).

Microsoft’s Quest for the Next Killer App

In this contributed article, Gordon McKenna, VP of Cloud Evangelist & Alliances at Ensono, discusses the situation with Microsoft strongly backing OpenAI, what can we expect the future to look like? Microsoft’s investment into OpenAI was a clear move for the company to align itself with the next killer app that would drive engagement on Azure cloud. 

Introducing Kyligence Copilot: The AI Copilot for Data to Excel Your KPIs

Kyligence Copilot is an AI Copilot for data that is built on the foundation of the Kyligence Zen metrics platform. It leverages the power of a large language model powered by Azure OpenAI, offering users the ability to search for metrics, conduct in-depth analysis, gain valuable insights, and enable the automatic creation of dashboards using intuitive, natural language conversations centered around business metrics. This seamless integration significantly reduces business users’ barriers to leveraging data, boosting their overall work efficiency.

Generative AI Report: Pilot Taps OpenAI to launch Pilot GPT

Welcome to the Generative AI Report, a new feature here on insideBIGDATA with a special focus on all the new applications and integrations tied to generative AI technologies. We’ve been receiving so many cool news items relating to applications centered on large language models, we thought it would be a timely service for readers to start a new channel along these lines. The combination of a large language model, fine tuned on proprietary data equals an AI application, and this is what these innovative companies are creating. The field of AI is accelerating at such fast rate, we want to help our loyal global audience keep pace.

#insideBIGDATApodcast: Should We, and Can We, Put the Brakes on Artificial Intelligence?

Appearing on the New Yorker Radio Hour, Sam Altman, CEO of OpenAI, which created ChatGPT, says that AI is a powerful tool that will streamline human work and quicken the pace of scientific advancement. But ChatGPT has both enthralled and terrified us, and even some of AI’s pioneers are freaked out by it – by how quickly the technology has advanced. 

Infographic: Is AI the Next Gold Rush?

Our friends over at analyzed over 10,000 AI companies and their funding data between 2015 and 2023. The data was collected from CrunchBase, NetBase Quid, S&P Capital IQ, and NFX. Corporate AI investment has risen consistently to the tune of billions.

Gaining the Enterprise Edge in AI Products

In this contributed article, Taggart Bonham, Product Manager of Global AI at F5 Networks, discusses last June, OpenAI released GPT-3, their newest text-generating AI model. As seen in the deluge of Twitter demos, GPT-3 works so well that people have generated text-based DevOps pipelines, complex SQL queries, Figma designs, and even code. In the article, Taggart explains how enterprises need to prepare for the AI economy by standardizing their data collection processes across their organizations like GPT-3 so it can then be properly leveraged.

Have a Goal in Mind: GPT-3, PEGASUS, and New Frameworks for Text Summarization in Healthcare and BFSI

In this contributed article, Dattaraj Rao, Innovation and R&D Architect at Persistent Systems, discusses the rise in interest for neutral network language models, specifically the recent Google PEGASUS model. This model not only shows remarkable promise when it comes to text summarization and synthesis, but its non-generalized approach could push industries such as healthcare to embrace NLP much earlier than was once supposed.

Why Humans Still Need to be Involved in Language-Based AI

In this contributed article, Christine Maroti, AI Research Engineer at Unbabel, believes that humans still need to be in the loop in most practical AI applications, especially in nuanced areas such as language. Despite the hype, these algorithms still have major flaws. Machines still fall short of understanding the meaning and intent behind human conversation. Not to mention, ethical concerns such as bias in AI still are far from a solution.