top of page
Writer's pictureSteve Quenette

AI applied to research - what 1,600 researchers think

We are working with BioPlatforms Australia, an Australian enabler of research infrastructure for the molecular sciences, to understand #AI's impact on the #omics community (BPA's focus areas are #genomics, #proteomics, #metabolomics, and #syntheticbiology). We will soon host a series of events and messages to share what we have learned and to crystallise on near-term strategy. Watch this space. 


In the meantime, one of the core pieces of work was consulting the community, asking what is the local sentiment and capability to respond to an AI and increasingly generative AI ecosystem? 


Here's a precursor to our findings. Late last year, Nature published an article entitled AI and science: what 1,600 researchers think. It provides valuable insight from all walks of academia. Some key takeaways mirrored in our findings are:

  • The share of research papers with titles or abstracts that mention AI or machine-learning terms has risen to around 8%

  • Lack of skills is the dominant barrier to using AI

  • An anecdote from the drug discovery community - "Only a very small number of entities on the planet have the capabilities to train the very large models — which require large numbers of GPUs, the ability to run them for months, and to pay the electricity bill. That constraint is limiting science's ability to make these kinds of discoveries", Garrett Morris, University of Oxford

  • More than half of those surveyed felt it important that researchers using AI collaborate with the commercial firms dominating computing resources and tool development.


Our specialisation is developing a progressive and impactful evidence base, near-term and long-term AI infrastructure, and enablement strategies. We are uniquely qualified to consult deeply technical and academic stakeholders and to facilitate technology partnerships. Dare to Dream!



bottom of page