Artificial Intelligence and Research and Development

AI-supported research processes are highly efficient. They are also changing how scientists look at data. Instead of creating standalone models and checking them with data, they can now give algorithms the job of creating a model. Deep-learning algorithms that simulate the neural pathways of the human brain are extremely promising for this task. One benefit is that AI considers data without human biases based on researchers’ understanding of physics or their expectations. Thus, the researchers can stimulate new ways of thinking or research approaches that a human would be incapable of imagining.

Finding Explanations with “Explainable AI” 

AI is also transforming the way research is done – everything from insights, theories and explanations to the data and applications. Even if the result is progressively improved efficiency, AI raises a range of ethical challenges. One example is the loss of trust when an AI system becomes a black box and the results can no longer be explained. Or when correlations point to connections that don’t exist. “Explainable AI” is a way around this dilemma. Researchers use transparent models such as decision trees to document a pattern from AI. Another tactic is to use a simpler model to depict a complex AI model and generate explanations with its help. This makes it possible to explain how AI operates before or after the fact.

A Focus on Researchers? 

The use of AI involves exploiting the potential of intelligent technologies without making people or their creative output obsolete. The job of a physicist is considered one of the most secure when it comes to AI-related risks to employment. After all, much of the work already takes place on computers. But other fields of research may be affected. They include genetic and climate research, areas where artificial intelligence is already being used successfully. But even these fields require the capacity for causal thinking, in order to trace genetic patterns back from diseases or weather patterns to climate change, for example. The human being is still needed at the center of research to recognize the connections that really exist.