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.

With the rise of OpenAI’s GPT-3, ChatGPT and Codex products, as well as GitHub’s CoPilot and numerous competitors, today we’re seeing developers experimenting with AI to help augment their development workflows. While at first these efforts focused on more commonly used programming languages, such as Javascript and Python, the AI use cases are now expanding to Infrastructure-as-Code (IaC) configurations.

Artificial Intelligence (AI) is revolutionizing the financial services industry, known as fintech. From improving customer experience to reducing operational costs and mitigating risks, AI is having a significant impact on fintech. In this blog, we will explore the top 10 ways AI is being used in fintech.

With news headlines like “A massive ransomware attack hit hundreds of businesses” becoming common, concern about malware has never been higher. High-profile examples of malware like DarkSide, REvil have been profiled so many times that not only cybersecurity professionals are on edge — every organization that has on-premises or in-the-cloud workloads is concerned.