Example Impact Area: AI in Sciences
It goes without saying that there are a plethora of areas in which the application of AI can have a significant impact. In order to more clearly articulate the kinds of papers we are encouraging, we will take a deep dive on one such area - the application of AI to advance and accelerate scientific discovery.
Historically, the terms “discovery” and “serendipity” have been closely linked with many discoveries of important materials, chemical products or medicines. In many domains, the scientific discoveries are still associated with laborious and time-consuming “probe and error” experiments or so-called the Edisonian approach to innovation. The combination of big data and AI is often referred to as the fourth industrial revolution.\cite{httpwwweconomistcomnewsspecial-report21700761-after-many-false-starts-artificial-intelligence-has-taken-will-it-cause-mass} AI is revolutionizing medicine, diagnostics, computer vision, language processing as they accumulate more and more data.
What are the general ways in which scientists apply AI methods in scientific settings?
- Prediction. Arguably, the simplest most straightforward way to apply AI is to use it to tackle prediction problems: mapping data to predicted numerical outputs. This is where machine learning is typically used to extract complex patterns and correlations from these data.
- Understanding. Here the emphasis of AI on understanding the underlying problem. In many cases an accurate prediction is not enough. Instead, we want to gain interpretable insights into what properties of the data or the process led to the observed outcome.
- Explainability. Explainable AI (XAI) will step even further, complementing predictive models with logical reasoning and explanations of their actions, to ensure that researchers are getting the right answer for the right reasons
- Discovery. A cutting-edge frontier of research. Can AI find new laws and phenomena?