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Since ChatGPT was released by OpenAI last year, large language models like it have gone viral. Cheerleaders extol these AI models as the future of work, maybe the best thing to happen since the invention of the internet, or as the invention that changes everything. Detractors point to their gaffs, failures, and “hallucinations.” Both Google and Microsoft have been embarrassed in the last several days by the outputs of their respective chatbots.

Can OpenAI ChatGPT become a contributor for an open source project like Falco? Is this the end of GitHub Copilot? ChatGPT is SO trendy that I overheard my grandma talking about it to her friends the other day. We’re seeing more and more uses of this AI for real world applications. That made us think… Falco, the first runtime security project to join CNCF as an incubation-level project, needs contributors.

OpenAI's ChatGPT chatbot has been a phenomenon, taking the internet by storm. Whether it is composing poetry, writing essays for college students, or finding bugs in computer code, it has impressed millions of people and proven itself to be the most accessible form of artificial intelligence ever seen.

In Calligo’s latest Beyond Data podcast, co-hosts Sophie Chase Borthwick and Tessa Jones are joined by Alexander Visheratin, Artificial Intelligence Engineer at Beehive AI. Here we explore some of the episode’s highlights; the importance of Natural Learning Processing (NLP) and the pros and cons of output produced by examples like OpenAI’s ChatGPT-3.

We’ve now seen a number of different use cases for ChatGPT from marketing, sales, software development and others including from the security field. This platform continues to dominate most of the headlines and impress based on how it’s able to handle questions and topics from various backgrounds.

In recent years, the world of artificial intelligence (AI) has seen a significant increase in the use of language models. ChatGPT, a language generation model developed by OpenAI, has been making waves in the news with its ability to process large amounts of data, which can be used to train machine learning models and to test them. One feature that’s grabbed headlines is its ability to write code and provide feedback on the accuracy and efficiency of code.