Programme

Download your copy of the AI Summit agenda here.


Please note times are subject to change
AI Summit Agenda


Expand All +
  • Aotearoa AI Summit - 12 May 2021


  • Enthusiasts see a world of opportunity for the use of AI across new domains, but somehow this hasn’t happened yet. This talk will discuss some of the issues that inhibit people from using AI to the full, and ways that these can be overcome over the next few years. The presentation will also propose ways to communicate the benefits and limitations of AI to people who have real problems to solve and limited time and budgets.

  • You will be able to book your roundtable the week before the Summit. The options include - Investment in the AI Economy / Human-centred and trusted AI / Enabling Foundations / Uniquely New Zealand / Preparing the Workforce

  • You will be able to book your roundtable the week before the Summit. The options include - What role for New Zealand in international AI ecosystem? / How to enable AI-driven use cases in organisations / How can NZ Government and industries create a data ecosystem that supports the future of AI? / Considerations for NZ tailored AI systems / Developing a dynamic AI research platform and partnerships in Aotearoa

  • In New Zealand, 40,000 – 50,000 hectares of forest is harvested each year with most areas replanted while some areas are converted to another land use. Ministry for the Environment conduct deforestation assessments every two years to monitor these changes and meet New Zealand’s international reporting obligations. In 2020 the Ministry turned to an AI based approach through Lynker Analytics to conduct an assessment of deforestation that had occurred during 2017 and 2018. Using an aerial survey, Lynker Analytics captured photography of almost 7,500 distinct areas of potential forest loss in all regions of the country. The imagery was then classified into land cover classes such as cutover, plantation seedlings, pasture, and mature native forest within each target using a Machine Learning (ML) approach. From this a multi-criteria analysis was used to assign each area of possible forest loss a dominant land cover and replant status. The model was used as the primary monitoring system to detect deforestation and re-planting and flag those targets to the Ministry. The automated monitoring system proved reliable in detecting deforestation, re-planting and other land cover changes exceeding one hectare. It also enabled more rapid assessment of replant status used by the Ministry for reporting.