Discussion supported by some concrete examples, outlining general guidelines on how to develop better ai systems.
artificial intelligence has become an integral tool in scientific research, but concerns are growing that the misuse of these powerful tools is causing a reproducibility crisis in science and its technological applications. Let's explore the fundamental issues that contribute to this detrimental effect, which applies not only to ai in scientific research but also to the development and use of ai in general.
artificial intelligence, or ai, has become an integral part of society and technology in general, finding several new applications in medicine, engineering and science every month. In particular, ai has become a very important tool in scientific research and in the development of new technology-based products. It allows researchers to identify patterns in data that may not be obvious to the human eye and other types of computational data processing. All of this certainly implies a revolution, which in many cases materializes in the form of revolutionary software solutions. Among dozens of examples, some like Great language models that can be put to “think”., Voice recognition models with excellent capabilities.and programs like Deepmind AlphaFold 2 that revolutionized biology.
Despite the growing interest in ai in society, concerns are growing that the misuse of these powerful tools is worsening the already strong and dangerous reproducibility crisis that threatens science and technology. Here I will discuss the reasons behind this phenomenon, focusing mainly on high-level factors that apply broadly to data science and ai development beyond strictly scientific applications. I believe the discussion presented here is valuable to all those involved in developing, researching, and teaching about ai models.
First, let's look at what reproducibility is and what the problem is, especially in the context of science and technology.
