In 2023, generative AI took the spotlight, emerging as the most talked-about technology of the year. This content creating powerhouse can do everything from text, image, and video generation to answering questions through natural language queries. And its potential uses are vast. While many industries are still in the experimental phase, the insurance sector is poised to benefit significantly from the integration of artificial intelligence into its ecosystem.
This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging. This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit. To view our code example, visit our GitHub page.
Natural language processing and large language models to help BigQuery process dataframes.
Visionaries from Capgemini, Databricks and Fivetran lay out the data quality imperative for implementing enterprise AI applications.
The rise of generative AI and the massive popularity of OpenAI’s ChatGPT has led to widespread recognition that software applications are about to fundamentally change. Generative AI offers the potential to both deliver breakthrough new application capabilities and transform the way people interact with software.
In the ever-evolving landscape of software testing, the advent of Artificial Intelligence (AI) has not just been a game-changer; it’s been a paradigm shift. Test automation, once a static process, has metamorphosed into a dynamic and intelligent entity, reshaping how we approach quality assurance.
The current state of AI, despite the relevant infancy of the tools, showcases promising potential. While human assistance is still needed, the convergence of chat UI and large language models allows users to ask for what they want in a natural language, and the technology is growing intelligent enough to respond or even take action.
New, game-changing technologies have emerged in the ever-changing field of software engineering as a result of the relentless search for efficiency and creativity. Platform engineering, AI coding assistants, and AI-augmented software engineering (AIASE) are predicted to achieve widespread acceptance in the next 2-5 years, according to the Gartner, Inc. Hype Cycle for Software Engineering, 2023. When it comes to Quality Assurance, software testing is one area where Chat GPT is predicted to thrive.
