OpenAI's ChatGPT system has sent the topic of artificial intelligence through the roof. But so many professionals across industries, including healthcare, do not truly understand how AI works – especially how the different forms of AI work. Further, there are a variety of acronyms floating around out there in the tech space: AI (artificial intelligence), ML (machine learning) and now FL (federated learning). But what's the difference between them, and how does each relate to healthcare? To get a primer on this important subject, Healthcare IT News talked with Ittai Dayan, CEO and cofounder of Rhino Health. Rhino Health is a vendor of a platform designed to enable developers and researchers to analyze data, create AI models and deploy them.

Humans are living in a truly global revolution of technology. The first two decades of the 21st century have witnessed dramatic advancements in artificial intelligence (AI) research. Machine learning has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. Machine learning is a subset of artificial intelligence that involves the study and use of algorithms and statistical models for computer systems to perform specific tasks without human interaction. Machine learning utilisation opens door to futuristic technologies that people use in their daily life.

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/).