We are suddenly experiencing an unprecedented demand for actionable information on biodiversity from governments, NGOs, and corporations worldwide. These stakeholders require biodiversity information that is finer grained (in space and time), more accurate, and more comprehensive than anything available today. Obviously AI (by which we mean scaled up deep learning) has great potential to both provide and interpret this next generation of information – from gathering more ecological data; to integrating that data into more useful information; to helping groups of stakeholders to make informed decisions against that information. However, this kind of transformative progress in AI for biodiversity will require a new level of integration of data, organizations, people, and technology. After an expanded version of the above intro, touching on some specific opportunities and challenges, I’ll raise the (open!) question of how, and how deeply, we should try to build what we might call nature’s schemas (i.e. phylogeny, traits, interactions, geographic and environmental space, and ecological time) into the next generation of planetary computing platforms.

I am a systems-oriented research scientist and research strategist, currently co-leading on sustainability and biodiversity at Google DeepMind. I have 25+ years of experience in Ecology, Earth System Modelling, Environmental Data Science, Deep Learning, Reinforcement Learning, RL Environment Design, and AGI. From a foundation of academic research at Cambridge, York, and Princeton, in 2007 I moved into the world of tech research, leading a group at Microsoft Research Cambridge, before moving to DeepMind in 2015. This varied career path has given me a broad understanding of various global challenges, and the landscape of data science, machine learning, and AI. In addition, I have been lucky enough to have lots of chances to develop the various additional skills needed to help inspire and lead multi-discipline teams, develop long-term strategy, and communicate with diverse groups of stakeholders.

Selected achievements:

• Over 50 scientific articles in Science, Nature, PNAS, PLOS Biology, Global Change Biology etc.
• Fortune Magazine ‘Big Data All Star’ 2015; Wired Magazine ‘Smart List’ 2012; WEF ‘Young Scientist’ 2013; Keynote speaker at NeurIPS 2016
• Co-Creator of The Madingley Model (‘Top Ten Scientific Discovery of 2014’ according to La Recherche)
• Successfully supervised or co-supervised over 10 PhD students and postdocs, and mentored many other colleagues, many of whom went on to leading roles in biodiversity, sustainability or even more widely
• Work covered extensively by the media including Fortune, CNN, Financial Times, New Scientist
• Led the research strategy of DeepMind’s Worlds team for over 5 years