Hub AI research
Artificial Intelligence is already part of everyday life. Since the ImageNet moment in 2012, when Deep Learning proved its great utility in combination with a lot of computing power and gigantic amounts of data, only about ten years have passed – yet there has been more progress than in half a century before.
Still, many questions remain: Will Deep Learning remain the gold standard of AI development? What are the performance limits of large AI systems? Do we need symbolic systems? What technology will lead to artificial general intelligence? Finally, what actually is intelligence, anyway?
We explore these topics and more in our Artificial Intelligence Research Content Hub.
Programmers who rely on AI assistants tend to ask fewer questions and learn more superficially, according to new research from Saarland University. A team led by Sven Apel found that students were less critical of the code suggestions they received when working with tools like GitHub Copilot. In contrast, pairs of human programmers asked more questions, explored alternatives, and learned more from one another.
In the experiment, 19 students worked in pairs: six in human-only teams and seven in human-AI teams. According to Apel, many of the AI-assisted participants simply accepted code suggestions because they assumed the AI's output was already correct. He noted that this habit can introduce mistakes that later require significant effort to fix. Apel said AI tools can be helpful for straightforward tasks, but complex problems still benefit from real collaboration between humans.
