E-Books →AI for Scientific Discovery

They support literature management and synthesis, allowing the wealth of what has already been discovered and reported on to be integrated and easily accessed. They play a central role in data analysis and interpretation in the context of what is called 'data science'. AI is also helping to combat the reproducibility crisis in scientific research by underpinning the discovery process with AI-enabled standards and pipelines and supporting the management of large-scale data and knowledge resources so that they can be shared and integrated and serve as a background 'knowledge ecosystem' into which new discoveries can be embedded.
However, there are limitations to what AI can achieve and its outputs can be biased and confounded and thus should not be blindly trusted. The latest generation of hybrid and 'human-in-the-loop' AI technologies have as their objective a balance between human inputs and insights and the power of number-crunching and statistical inference at a massive scale that AI technologies are best at.