Since 2014, the Allen Institute for Artificial Intelligence (AI2) has been using artificial intelligence (AI) to build tools and programs to improve the everyday work of researchers. Michael Schmitz, Director of Engineering, explains that their mission is "to contribute to humanity through high-impact AI research and engineering. We have a multiple strategies to achieve this, such as producing impactful research discoveries, publishing in peer-reviewed journals, and building tools to help accelerate the pace of research and enable research that was previously prohibitive.” Their programs have included Semantic Scholar, a natural language processing tool that makes it easy to mine vast amounts of scientific literature for insights, and Aristo, a multidisciplinary project that draws on AI to reason about science.
In 2017 Marc Millstone, Manager and founder of the Beaker team, partnered closely with a group of researchers at AI2 to observe and understand how they work. It soon became clear, he says, that “the process of trying out new ideas was held back, not by the new ideas, but due to the cognitive overhead in infrastructure and collaboration—running experiments, tracking their results and then sharing them.” Reproducibility in the sciences is crucial for validating results and ensuring that they can be duplicated in another lab and built upon, but modern deep learning systems are complicated with large-numbers of parameters to track and complex dependencies to maintain.