Discussion backed up by some concrete examples, sketching broad 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 leading to a reproducibility crisis in science and its technological applications. Let’s explore the fundamental issues contributing to this detrimental effect, which applies not only to AI in scientific research but also to AI development and utilization in general.
Artificial Intelligence, or AI, has become an integral part of society and of technology in general, finding every month several new applications in medicine, engineering, and the sciences. In particular, AI has become a very important tool in scientific research and in the development of new technology-based products. It enables researchers to identify patterns in data that may not be obvious to the human eye, and other kinds of computational data processing. All this certainly entails a revolution, one that in many cases materializes in the form of game-changing software solutions. Among tens of examples, some such as large language models that can be put to “think”, speech recognition models with superb capabilities, and programs like Deepmind’s AlphaFold 2 that revolutionized biology.
Despite AI’s growing stake in society, concerns are growing that the misuse of these powerful tools is worsening the already strong and dangerous crisis in reproducibility that threatens science and technology. Here, I will discuss the reasons behind this phenomenon, focusing mainly on the high-level factors that apply broadly to data science and AI development beyond strictly scientific applications. I believe the discussion presented here is valuable for all those involved in developing, researching, and teaching about AI models.
First, let’s see what reproducibility is, and what the issue with it is, especially in the context of science and technology.
