Harnessing the capabilities of AI to develop the world's most sophisticated ecological monitoring and modelling system which can lead deep inquiry into socio-ecological systems and support system-level innovation in natural resource management.
Germany, Sweden, Estonia
The environmental problems of the BSR, exacerbated by climate change, necessarily require multi-jurisdictional & cross-disciplinary initiatives to effectively manage. European policy supports this; IISAM utilises a unique technology & collaborative to realise it. IISAM will facilitate cohesion & inclusion in Europe via joint problem-solving by uncommon bedfellows (environmental science & cognitive computing experts), collective scientific advancement and democratic participation in science & resource management. The application can be trained & applied for management of other natural resources, in other regions. It can also be applied to different fields (e.g., epidemiology - studying environment-health connections). In fact, its performance improves with interdisciplinary training & use.
1- Stubborn analytical bottlenecks are traversed, producing new insights & hypotheses into co-evolutionary processes, structural qualities & stochastic behaviour of marine ecosystems.
2- Highly-complex data is reliably transformed into useful information & communicated in actionable ways for natural resource managers, bioeconomy entrepreneurs, policy-makers & NGOs – leading to improved decision-making, risk management, increased accountability.
3- BSR (includes The Ruhr) communities can access critical information about their environment, thus enabled to engage in grassroots management of marine resources.
4- Europe becomes a world leader in environmental management and transnational & interdisciplinary collaboration, inspiring similar locally-grown innovative initiatives.
Apr–Jun 2015: Establish IISAM (either as an NGO or a project hosted by existing NGO / research institute), consisting of experts in marine & data science, ecological informatics & cognitive computing, from each BSR country. Secure additional funding & project partners.
Jul–Dec 2015: Work with organisations (research institutes, academic journals, etc.) to access, collate & verify data & models for AI training. Establish contract with AI proprietor.
Jan-Oct 2016: Train & test AI using data & models. Engage user groups to develop interactive dashboards for their respective fields. Establish subscription services with AI proprietor for user group access to application.
Nov 2016-onwards: Develop application for other fields (e.g., forest management, epidemiology) & regions (Mediterranean)
1- Leads: relevant stakeholders & interested partners, datasets to be accessed; 2- Constructive Criticisms: potential pitfalls in concept, ethical issues, bureaucratic constraints; 3- Technical: technological & analytical limitations & possibilities.