Discussion backed up by some concrete examples, sketching broad guidelines on learn how to develop higher AI systems

Artificial Intelligence has grow to be an integral tool in scientific research, but concerns are growing that the misuse of those powerful tools is resulting in a reproducibility crisis in science and its technological applications. Let’s explore the basic issues contributing to this detrimental effect, which applies not only to AI in scientific research but additionally to AI development and utilization usually.

Artificial Intelligence, or AI, has grow to be an integral a part of society and of technology usually, finding every month several latest applications in medicine, engineering, and the sciences. Particularly, AI has grow to be a vital tool in scientific research and in the event of latest technology-based products. It enables researchers to discover patterns in data that is probably not obvious to the human eye, and different kinds of computational data processing. All this actually entails a revolution, one which in lots of cases materializes in the shape of game-changing software solutions. Amongst tens of examples, some reminiscent of large language models that could 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 those powerful tools is worsening the already strong and dangerous crisis in reproducibility that threatens science and technology. Here, I’ll discuss the explanations behind this phenomenon, focusing mainly on the high-level aspects that apply broadly to data science and AI development beyond strictly scientific applications. I consider the discussion presented here is worthwhile for all those involved in developing, researching, and teaching about AI models.

First, let’s see what reproducibility is, and what the problem with it’s, especially within the context of science and technology.