Frank Kumli’s Post

Untapped Collective Intelligence for Climate Action! I. Collective Intelligence Collective intelligence can be understood as the enhanced capacity that is created when people work together, often with the help of technology, to mobilize a wider range of information, ideas and insights II. Use Cases in Climate Action 1. New forms of accountability and governance 2. Anticipating, monitoring and adapting to systemic risks 3. Real-time monitoring of the environment 4. Understanding and working with complex systems 5. Inclusive development and technologies 6. Distributed problem solving III. Closing Important Gaps in Climate Action 1. The Data Gap Collective intelligence approaches are mobilizing citizens to generate real-time localized data, and bringing together data sets to uncover new insights. 2. The Doing Gap Collective intelligence approaches are getting more people involved in taking climate action, and helping people monitor the follow-through of institutions 3. The Diversity Gap Collective intelligence initiatives are bringing a wider range of people and perspectives into climate processes and data collection, including Indigenous communities 4. The Distance Gap Collective intelligence initiatives are fostering a two-way exchange between scientists and local communities — enhancing scientific understanding and public knowledge, as well as creating mutual trust. 5. The Decision-making Gap Collective intelligence initiatives are soliciting contributions from a diverse range of people, creating collective understanding of a problem, and supporting decision-making processes through structured deliberation IV. Key R&D opportunities A. Increase the utility of citizen data for climate issues 1. Apply methods from citizen-led experiments in agriculture to other climate issues 2. Enhance the evidentiary value of crowdsourced data in climate adaptation. 3. Develop new approaches to compensate for sparse data in disaster risk and biodiversity management. B. Invest in collective intelligence for climate decisions and action 1. Develop accessible, creative tools and methods for collective decision-making 2. Involve more diverse groups of people in oversight of government climate commitments 3. Create tools that help people take collective action to improve resilience C. Design multi.functional and scalable collective intelligence tools 1. Invest in crisis intelligence tools that track multiple hazards 2. Develop data standards for qualitative and citizen-generated data 3. Connect hyperlocal knowledge into global models and efforts Check out the report by Nesta and UNDP Accelerator Labs here: https://lnkd.in/du-qifuQ #innovation #climate #collectiveintelligence #data #citizenscience #sustainanility UNDP

What Makes a Business Model Sustainable: Activities, Design Themes, and Value Functions! I. Question What makes a business model sustainable? Or, in other words, what distinguishes a business model for sustainability from a conventional business model? II. Business Models for Sustainability A business model for sustainability refers to how an organization proposes, delivers, captures, maintains, unlocks, and shares value with and for its stakeholders III. Value Functions and Design Themes A. Maintaining value Ensuring the integrity and functionality of the natural environment and man-made artifacts 1. Avoid harmful substances 2. Avoid waste 3. Extend life cycles 4. Reduce resource consumption B. Unlocking value Utilizing untapped potential for sustainable value creation beyond an organization's boundaries, specifically at the interfaces with customers and markets 1. Facilitate informed decision-making 2. Influence purchasing behavior 3. Influence user behavior 4. Make sustainable offerings accessible 5. Stimulate demand for sustainable offerings C. Sharing value Involving, engaging, and supporting stakeholders to ensure that the benefits from value-creation processes are shared equitably and responsibly 1. Improve stakeholders' socioeconomic conditions 2. Offer access/opportunity 3. Support environmental/social causes IV. Conclusion 1. Understanding Gaining an understanding of what makes a business model sustainable necessitates a certain level of disaggregation of activities, coupled with explicit efforts to comprehend their specific purposes. 2. Design Themes The design themes contribute to a broader array of value functions, empowering organizations to actively contribute to sustainable value creation and clearly setting Business Models for Sustainability apart from conventional business models 3. Dynamic interplay It is the dynamic interplay among activities, design themes, and value functions that defines a Business Models for Sustainability and differentiates it from conventional business models Check out this insightful paper by Florian Lüdeke-Freund, Tobias Froese, Krzysztof Dembek, Francesco Rosati (🙏 for the share!) and Lorenzo Massa here: https://lnkd.in/eSWm4JJu Free pre-print: https://lnkd.in/et9wE8r2 #innovation #sustainability #businessmodelinnivation #sustainablebusinessmodel #strategy ESCP Business School DTU Entrepreneurship Climate Foundation Aalborg University

Theranostics and Artificial Intelligence: New Frontiers in Personalized Medicine! I. Theranostics Theranostics combines advanced imaging techniques with targeted therapy, offering insights into critical areas of precision oncology, such as optimized treatment regimens and efficient monitoring. II. Key Areas of AI Application 1. Patient Selection and Risk Stratification Given the unique nature of each patient, aligning patients with the optimal treatment options necessitates a thorough analysis taking into account both the patient's individual characteristics and the tumor's specifics. Artificial intelligence holds strong promise for this process by integrating diverse types of patient- and tumor-specific data, including multi-omics (such as genomics and proteomics) 2. Tumor Dosimetry By predicting and identifying the absorbed radiation doses by the target, organ-at-risk, and healthy tissues, dosimetry studies provide essential insights into the objective assessment of a treatment's safety and efficacy for each patient. The utilization of AI-based tools in dosimetry studies for theranostics holds the promise of significantly enhancing study accuracy and efficiency 3. Disease Monitoring By harnessing adaptive learning algorithms, AI holds the revolutionary potential to leverage traditional parameters to advance therapy monitoring. Various AI-based platforms offer e-consults to clinicians and patients across numerous specialties, enriching and refining the clinical experience 4. Drug Discovery Radiopharmaceutical development is a challenging endeavor, encompassing a myriad of facets including target identification, lead compound identification, radionuclide selection, vector molecule formulation, synthesis, evaluations, and drug approvals. These processes are tedious and costly, but AI-powered theranostics drug discovery studies promise optimization, paving the way for quicker development and approval of innovative radiopharmaceuticals. III. Outlook Despite these advances holding significant promise, adopting AI algorithms to the routine clinical practice raises several concerns, including security of data, accountability of outcome, and comprehensiveness and diversity of outcomes. Therefore further research and guidelines outlining the responsible use of AI are needed to harness the full potential of AI, while considering its limitations and ensuring its successful integration into clinical practice Check out the insightful article by the team at Mayo Clinic here: https://lnkd.in/dwPgc3mD #innovation #healthcare #ai #ml #theranostics #radiopharmaceuticals #digitalhealth #healthtech #oncology #genomics #proteomics

GenAI: The Open Medical-LLM Leaderboard: Benchmarking Large Language Models in Healthcare! I. Potential Over the years, Large Language Models (LLMs) have emerged as a groundbreaking technology with immense potential to revolutionize various aspects of healthcare II. Challenge When models are used for recreational conversational aspects, errors have little repercussions; this is not the case for uses in the medical domain however, where wrong explanation and answers can have severe consequences for patient care and outcomes III. Benchmark To fully utilize the power of LLMs in healthcare, it is crucial to develop and benchmark models using a setup specifically designed for the medical domain IV. Open Medical-LLM Leaderboard The Open Medical LLM Leaderboard aims to track, rank and evaluate the performance of large language models (LLMs) on medical question answering tasks V. Assessment It evaluates LLMs across a diverse array of medical datasets, including MedQA (USMLE), PubMedQA, MedMCQA, and subsets of MMLU related to medicine and biology VI. Key Findings 1. Commercial models like GPT-4-base and Med-PaLM-2 consistently achieve high accuracy scores across various medical datasets, demonstrating strong performance in different medical domains 2. Open-source models, such as Starling-LM-7B, gemma-7b, Mistral-7B-v0.1, and Hermes-2-Pro-Mistral-7B, show competitive performance on certain datasets and tasks, despite having smaller sizes of around 7 billion parameters 3. Both commercial and open-source models perform well on tasks like comprehension and reasoning over scientific biomedical literature and applying clinical knowledge and decision-making skills 4. Google's model, Gemini Pro demonstrates strong performance in various medical domains, particularly excelling in data-intensive and procedural tasks like Biostatistics, Cell Biology, and Obstetrics & Gynecology. However, it shows moderate to low performance in critical areas such as Anatomy, Cardiology, and Dermatology, revealing gaps that require further refinement for comprehensive medical application Check out this important initiative by Ankit Pal at Saama, Pasquale Minervini, PhD at School of Informatics, University of Edinburgh and Clémentine Fourrier at Hugging Face here: https://lnkd.in/gf637xED #innovation #healthcare #ai #genai #llm #healthtech #digitalhealth

Metroverse: An Urban Economy Navigator! Metroverse is an urban economy navigator built at the Growth Lab at Harvard University. It is based on over a decade of research on how economies grow and diversify and offers a detailed look into the specialization patterns of cities. As a dynamic resource, the tool is continually evolving with new data and features to help answer questions such as: * What is the economic composition of my city? * How does my city compare to cities around the globe? * Which cities look most like mine? * What are the technological capabilities that underpin my city’s current economy? * Which growth and diversification paths does that suggest for the future? As city leaders, job seekers, investors and researchers grapple with 21st century urbanization challenges, the answer to these questions are fundamental to understanding the potential of a city Make sure to check out the navigator by Harvard's Growth Lab Lab here: https://lnkd.in/dW3-dpcq Harvard University #innovation #strategy #cities #ecosystem #clusters #policy

Foresight for Foresighters! I. Uncertainty Until 2035 the uncertainty and complexity will only be increasing, giving rise to a Multi Crisis Environment II. Foresight Strategic Foresight is key for navigating complex and uncertain futures. III. Foresight Expertise Consulting and Strategic Foresight expertise will transition from being exclusive to a select class of professionals to becoming universally accessible through natural language IV. Recommendations for Foresight Professionals 1. Leverage fine-tuned AI models specific to the field of Foresight and Consulting and establish inhouse data science expertise 2. Become a lighthouse Foresight Institution in order to front run the future in a credible way 3. Focus on the value of original thought as the key area for human labor in successfully applying AI tools and making knowledge scale 4. Be part of the next generation of platforms in order to leverage open source and on-chain practitioners for your business model 5. Benefit from the rise in demand for Strategic Foresight driven by the rise of uncertainty by offering orientation and deep insights 6. Transform Marketing and Sales activities to fit a new era of AI gatekeeping by actually adding value for the customer 7. Become cutting edge by applying Foresight in the context of AI Alignment 8. Offer faster Time to Value products that utilize knowledge-automation fully 9. Adapt to the democratization of knowledge work and the deflationary force of automation by scalable products and new business models 10. Shift your attention towards implementation & actionable insights 11. Position yourself in the renaissance of Forecasting 12. Leverage open source for its competitive advantage 13. Participate in the shift towards inhouse expertise 14. Offer Immersive experiences, digital twins of the future and persistent images of the future in order to move beyond PDF and PPT when communicating results 15. Expand your areas of expertise beyond technology focusing on solutions for the next generation Check out the report by Julia Lampert and Patrick Duffner at 2b AHEAD ThinkTank GmbH here: https://lnkd.in/d2kHPkh3 #innovation #strategy #foresight #futurethinking #scenarioplanning #uncertainty #ai #genai #dao #digitaltwins #complexity

The People-Centric Lab of the Future: AI-supported Innovation to Meet Tomorrow’s Needs! I. Roles of AI in Innovation Teams 1. Scribe: generating content across all media, based on specific prompts 2. Librarian: answering questions, lookup, and search 3. Analyst: summarizing data series and underpinning pattern recognition and extrapolation 4. Engineer: aiding task definition, analysis, and optimization for problem solving 5. Scientist: causal inference of general laws based on empirical observation and counterfactual reasoning 6. Craftsman: directing and manipulating physical tools and objects in uncontrolled environments 7. Artist: aiding ideation and design, bringing together diverse influences and perspectives to help innovators create original ideas and concepts II. Concepts of the Lab of the Future A. Democratization: making AI available, customized for everyone and adopted as a natural way of working, closing the gap between AI users and AI providers B. Collaboration: using AI and other digital tools to help supercharge the sharing of data and knowledge, forming a virtuous circle unconstrained by the limitations of current data management C. Ambidexterity: setting the organizational context to facilitate and encourage AI-supported breakthrough innovation and exploration, at the same time as maintaining R&D efficiency and productivity III. How to get started A. Democratization 1. Identify and prioritize AI use cases and adoption, integrated across the R&D and innovation cycle, with defined AI skill-level targets and competences 2. Focus on end-to-end processes so technical experts can seamlessly adopt AI into their working routines 3. Ensure no separation between “innovating with AI” and “innovations with AI.” B. Collaboration 1. Develop and build out future requirements in terms of data architecture and engineering, as well as user experience 2. Strengthen and diversify the wider innovation ecosystem and increase permeability between roles, teams, and the company at large 3. Redefine the skills and expertise that are needed and use this to drive hiring, training, and team composition C. Ambidexterity 1. Strengthen the role and caliber of project leadership and solution engineering and increase the iteration steps in end-to-end innovation with more frequent optimization. 2. Focus on optimized decision making — reduce hierarchies and redesign governance to differentiate between types of decisions 3. Foster an innovation culture that is familiar and comfortable with uncertainty and ambiguity — essential for being able to experiment and learn along the AI adoption journey Check out the report by Michaël Kolk, Marten Zieris and Michael Eiden at Arthur D. Little here: https://lnkd.in/dgx2X4-K #innovation #ai #genai #cocreation #strategy #learning #skills #ambidextry #democratization #collaboration

World Creativity and Innovation Day: What is the World without Innovation! Innovation is the lifeblood of progress and the driving force behind the advancement of sustainable development goals In a business context, it’s the ability to conceive, develop, deliver, and scale new products, services, processes, and business models. Innovation can create value, enhance productivity, and foster competitiveness The potential of innovation fuels hope. But all too often, that hope is dashed by ineffective execution. What does it take to really spur innovation? On this World Creativity and Innovation Day, check out the insights by McKinsey & Company to learn how top innovators outperform peers, drive innovation with generative AI, and create a culture that accounts for the human side of innovation https://lnkd.in/dKcgy2h2 #innovation #strategy #creativity #ai #culture #genai

Implementing the European Health Data Space Across Europe! Findings: I. Governance 1. The conditions and processes to access data for secondary use vary significantly between countries 2. There is variation in countries' approaches in the run-up to adoption of the EHDS 3. A single model of centralised national EHDS governance will not fit all countries II. Capacity and Skills 1. Digitalisation in national health systems varies significantly between countries and areas of healthcare provision 2. Data systems in healthcare remain highly fragmented and infrastructures to connect them are scarce 3. Data-sharing initiatives exist at EU, national, regional levels 4. Industry will be a driving force for implementation III. Resources and Funding 1. The total budget and the upfront costs of implementation could not be precisely estimated 2. Available funding and resources for the EHDS were considered insufficient 3. Countries with limited national resources are also the ones where the investments required will be greatest 4. Existing and future European projects of common interest could facilitate implementation IV. Data Quality 1. Vast disparities exist in the implementation of standards for health data 2. Data quality is inconsistent and interoperability remains a universal challenge 3. Capacities and budgets for quality improvement measures are low in healthcare 4. Patients and citizens could have a role to play in controlling and enriching their own data 5. Extensive work is happening at EU level to help develop a common approach to quality V. Primary and Secondary Use 1. Current workflows and data management structures are not geared towards the need for collection of datasets for secondary use. 2. The reliance on paper records keeps entire territories and categories of health data inaccessible for research 3. New technologies could facilitate data collection and motivate healthcare professionals to record high-quality data 4. Paths for the introduction of data-driven innovation into clinical pathways exist 5. Public-private and multistakeholder collaborations are accelerating the development of data-driven solutions p VI. Awareness, Education, Communication 1. Awareness of the upcoming EHDS regulation is low among stakeholder groups 2. Citizens' acceptance of health data sharing for secondary use is variable 3. Patients are generally more conscious of the importance of data-sharing p 4. Data-sharing initiatives and projects completed in the course of the COVID-19 pandemic can serve to demonstrate tangible results and benefits Check out the report by EIT Health and European Union here (thanks Danny Van Roijen for the share!): https://lnkd.in/d9m9HTqh #innovation #healthcare #healthdata #digitalheath #healthtech #ehds