DISA

When? Friday April 4th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/aht4pqfi4XWv76PK6 by April 2nd (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Data-driven Community-based Business Models for Forestry: Friends and Foes – Samin Ghalandarzadeha, Södra
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Generalisation of GenAI and Verification pipelines – Nemi Pelgrom Fortnox
15.50 -16.00 Sum up and plan for our seminars in May

Abstracts

Data-driven Community-based Business Models for Forestry: Friends and Foes – Samin Ghalandarzadeha, Södra

Building on our recent systematic literature review of the challenges and opportunities in data-driven and community-based business models for agriculture and forestry , this study will explore key findings and will seek to bridge the gap between theory and practice by engaging experts from a major Swedish community-based forestry association.

Through interviews with industry specialists, this study will uncover new opportunities and challenges for implementation of the abovementioned business models, as well as test these evidence-based findings. Ultimately, this research will assess the feasibility of the proposed business models, identify context-specific challenges and benefits, and strengthen the theoretical framework with real-world insights.

Generalisation of GenAI and Verification pipelines – Nemi Pelgrom Fortnox

Based on the publication of AlphaGeometry a little over a year ago, a new development in the strive towards trustworthy AI is gaining popularity; to combine generative models with automatic verification tools, as separate parts of frameworks or information pipelines. Many formats of information pipelines have been well researched before Generative AI joined the picture, but the difficulty in interpreting GenAI models into the languages (terminologies) used by those fields, makes it hard for researchers to interpret what previous results are relevant in these new contexts. In this presentation I will propose a formal terminology for describing this kind of pipeline, which may be used as guidance for how to interpret the validity, or trustworthiness, of any pipeline produced that fulfils the relevant criteria.