Peer-To-Peer Innovation Networks

🤔Weekend Reading 👉 A few years ago, I began exploring how AI and collective intelligence could converge to address complex challenges—from public health to democratic innovation. One suggestion of my paper at the time included augmented collective intelligence: the idea that technology could help groups think better together, not just individuals work faster alone. (see: https://lnkd.in/ezMRya9) 📢 A new paper by Thomas Kehler, Scott Page, Alex 'Sandy' Pentland, Martin Reeves, and John Seely Brown brings this vision into the generative AI era—introducing the concept of Generative Collective Intelligence (GCI). 👉 Instead of framing AI as a substitute for human cognition, GCI treats AI as a "cultural and social technology that allows humans to take advantage of the information other humans have accumulated." This represents a shift from personal productivity tools to collective reasoning infrastructures—where humans and AI collaborate to align on goals, explore alternative solutions, and overcome communication barriers. 🧭 As the authors put it: “The greatest potential of AI lies not in its capacity to act alone but in collaborations that combine human creativity and wisdom with AI's computational and organizational capabilities.” 👉 The paper offers mathematical foundations (comparative judgment, minimum regret), rich use cases (climate adaptation, healthcare, civic participation), and a vision where AI becomes a partner in structured deliberation, not just a source of generative outputs. (which Claudia Chwalisz is also examining). 🤔 Of particular interest to me involved their focus on “Amplifying Serendipitous Discovery” - which reminded me of my recent conversations with the great Dirk Helbing who is also looking into how to enable serendipitous encounters to solve societal problems. 🧭 Quote:  “Generative collective intelligence can amplify serendipitous discovery—unexpected connections and insights that emerge when diverse perspectives interact in structured ways...Traditional group decision-making often falls victim to communication complexity, as the increased number of participants creates exponentially more communication channels. Humans lack the capacity to determine which of the thousand plus combinations of three people that could be chosen to form a group of twenty offer the most promise for a breakthrough.” 🤔 Another area of interest explored in the paper involves “The Architecture of Epiphany” - which relates much with what we are working on re: the structuring the quest of questions or architecture of Inquiry (see: https://lnkd.in/etDgZSyN)  🧭 Quote: "What distinguishes GCI's approach to breakthrough thinking is its structured facilitation of what cognitive scientists call "conceptual blending"—the process of integrating elements from different mental spaces to create new conceptual structures." 📄 Full paper: https://lnkd.in/e_gMtpST

AI Swarm Intelligence: Lessons from Nature to Optimize Business Decisions Ever notice how birds flock in perfect sync or ants find food with uncanny efficiency? That same principle many simple units acting together drives AI swarm intelligence. Instead of a single, resource-heavy model, small AI agents locally interact, share findings, and converge on the best solution. Understanding Swarm Intelligence What is Swarm Intelligence? Swarm intelligence is a collective behavior exhibited by decentralized, self-organized systems. Think of it as many “small brains” working together to form a super-intelligent system without any centralized control. This principle is observed in nature, Ant Colonies & Bird Flocks. In AI Terms: Swarm intelligence leverages multiple simple & small AI agents that interact locally with one another, leading to a global problem-solving strategy. Instead of relying on one monolithic, resource-heavy model, these agents collectively explore and optimize solutions. Swarm Intelligence in Action Practical Example Logistics: Agents independently assess routes, share data, and collectively decide the most efficient path,adapting instantly to traffic or demand shifts. This decentralized approach can quickly adapt to traffic changes, accidents, or sudden demand spikes, much like a flock of birds adjusting its course on the fly. Business Optimization with Swarm Intelligence Supply Chain Management: Scenario: A global retailer manages inventory across multiple warehouses. Swarm Approach: Small AI agents monitor local inventory levels, predict demand fluctuations, and communicate with each other to optimize stock distribution. Result: A highly adaptive, efficient supply chain that minimizes stockouts and reduces excess inventory. Adaptive and Resilient: Unlike traditional AI models, a swarm-based approach is inherently flexible. If one agent fails or encounters an unexpected obstacle, others seamlessly fill the gap. It’s like having a team of friends where if one friend forgets the directions, the rest can still get you to the party on time. Scalability: Swarm intelligence scales naturally. Whether you have 10 or 10,000 agents, the system’s performance improves as more data points contribute to the collective decision. Example: In urban planning, a swarm of sensors and agents can collaboratively monitor traffic, pollution, and energy consumption, leading to smarter, more responsive cities. Cost Efficiency: Instead of investing in one supercomputer model, businesses can deploy numerous smaller, cost-effective agents that work together, often yielding faster and more robust results. As we look to the future, It’s not just about creating smarter algorithms, it’s about reimagining how multiple, simple agents can collectively tackle complex challenges, much like nature has perfected over millions of years. What do you think? How could swarm intelligence transform your industry or business model?

Breaking Silos, Building Innovation: How SEA Milano Airports 🇮🇹 Transformed with FORTH … When SEA Aeroporti di Milano set out to innovate, they weren’t just looking for new concepts—they wanted to embed innovation into their culture. With 35 million passengers a year and a complex, diverse workforce, SEA faced a challenge familiar to many large organizations: how to break down silos and make change truly happen. Guided by Evidentia , a human-centered innovation boutique in Italy, SEA applied the FORTH Innovation Method to tackle diversity and inclusion in a structured, results-driven way. Instead of vague discussions and stalled initiatives, they focused on measurable impact, rapid implementation, and broad internal involvement. The Power of FORTH in Action • Over 600 employees participated in research, interviews, and testing. • 2,233 ideas were generated, leading to 43 concepts. • Seven innovation projects were selected, three of which were launched immediately. What made this a success? Top-down sponsorship and bottom-up engagement. From shift workers to senior executives, everyone had a voice. The process not only delivered concrete innovation projects but also created a lasting mindset shift—embedding inclusion as a core value at SEA. It was lead by three great skilled facilitators Anna Forciniti, Letizia Migliola (she/her) and Maria Vittoria Colucci. Key Takeaways for Innovating Across Silos 1. Engage all levels—from frontline staff to top leadership. 2. Create psychological safety—so diverse voices are truly heard. 3. Ensure fast implementation—so innovation doesn’t stall. 4. Build strong sponsorship—leaders must actively support change. 5. Use a structured method—like FORTH, to align teams and drive action. By applying FORTH, SEA Milano Airports didn’t just generate new ideas; they built an innovation culture that lasts. Want to see similar results in your organization? Let’s talk about how structured innovation can work for you! #Innovation #FORTHMethod #DesignThinking #DiversityInclusion #SEAInnovation

Stanford University's genies STORM & CO-STORM are revolutionizing interdisciplinary teamwork by facilitating the creation of Wikipedia-style articles and Roundtable Discussion conversions. 📚 In a world where experts seamlessly unite across disciplines, Stanford's STORM and CO-STORM employ Autonomous AI agents to delve into a myriad of online documents and research papers, fostering real-time collaboration for transformative breakthroughs. 🔆 STORM, or Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking, pioneers an innovative framework enhancing interdisciplinary collaboration. By amalgamating diverse viewpoints and utilizing advanced retrieval techniques, STORM amplifies research exploration's clarity and depth. 💫 Building upon this foundation, CO-STORM introduces Collaborative STORMing sessions, fostering structured environments for brainstorming, solution refinement, and implementation to tackle contemporary challenges effectively into a conversational format of discussions amongst various experts. ✨ Insights gleaned from these genies highlight the enrichment of research depth and solution diversity through multi-perspective question asking, the productivity boost from enhanced retrieval systems, and the accelerated innovation driven by structured topic synthesis. 🌟 From revolutionizing healthcare to addressing global sustainability challenges, STORM and CO-STORM empower teams to unleash the collective information retrieval potential of the AI agents in research and development, shaping a brighter future. 💫 My experiments with these tools:- 🔆 I sought an article on one of my research topics "Collaboration amongst human experts, LLMs, and AI agents towards evaluations of AI systems" via STORM which appeared to be a good first draft. STORM used four different agents - A basic Fact Writer, a Software Engineer, a Data Ethicist, an AI Research Scientist to create an engaging and well-cited article. Check it out here - https://lnkd.in/d8_yi_rG 🔆 I also tried a conversation-style roundtable discussion on another topic of interest "Responsible Governance Framework for Generative AI Adoption for Small and Medium Businesses". Check it out here - https://lnkd.in/du8ap4dm ✨ Explore the research and platform:- 📜 Paper - https://lnkd.in/dDBWvqte 👩💻 Code - https://lnkd.in/dfq8HTxE 🌐 STORM/ CO-STORM - https://lnkd.in/dK7gj6SC 💫 How could these approaches redefine your field of interest? Please share your thoughts! #StanfordSTORM #CO-STORM #Collaboration #AIInnovation #AgenticAI #ResearchLeadership #InterdisciplinarySolutions #Innovation #Stanford

How often do we design with people, instead of for them? It’s easy to fall into the trap of thinking that creativity is something only designers hold the key to. But when we pause and engage with communities, we realize something powerful: Creativity thrives within the community itself—it just needs the right conditions to flourish. Take, for example, the Collective Action Toolkit (CAT) by Frog. It’s not just a tool; it’s a framework that empowers communities to solve problems by tapping into their collective strength. Through a series of activities—like clarifying goals and imagining new ideas—small groups around the world have used this toolkit to not only share their thoughts but to take decisive action that addresses their concerns. The beauty of this approach is in its adaptability. It’s not a one-size-fits-all model. Each group can mould it to fit their unique needs, ensuring that everyone’s voice is heard and valued. But collaboration, as we know, isn’t always easy. There’s often discomfort, sometimes even conflict, when differing ideas meet. Yet, as designers, navigating these challenges is where true progress happens. As Otto Scharmer and Peter Senge, leaders in organizational development, have shown, it's in this space of tension that new solutions are born. A recent contribution from @Design Impact offers a set of guiding principles for designers to keep in mind when working with communities. One of these, “Value me for who I am, not who I’m told to be,” resonates deeply. It’s a reminder that behind every design is a real person, with history, emotions, and passions. When we acknowledge that, we move beyond simply gathering feedback—we tap into real leadership within the community. At the end of the day, Social innovation isn’t just about creating a product or service. It’s about co-creating, about building alongside communities rather than handing down solutions. It’s about fostering a space where everyone’s creativity can shine, and where long-term, sustainable change is possible. Have you been part of a design process that values community leadership? What challenges—and opportunities—did you encounter along the way?

Last week 95 members of the Innovative Finance Initiative community came together to map how impact is being hardwired into the core mechanics of finance. During the session, we piloted a live community mapping exercise. Each group shared live examples of how they’re embedding (or seeing embedded) impact into 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘴, 𝘐𝘯𝘤𝘦𝘯𝘵𝘪𝘷𝘦𝘴, 𝘢𝘯𝘥 𝘗𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴. The result? A rich harvest of concrete practices, structural innovations, and collective questions for the field. Here are some of the insights that surfaced: 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 💸 Blended finance stacks de-risking regenerative agriculture and climate resilience 🌱 Evergreen funds & community ownership models to preserve long-term benefit 🛡️ Steward ownership & perpetual trusts to lock in mission across ownership transitions 📜 Legal clauses & fiduciary reforms enabling investors to prioritize impact alongside returns 𝐈𝐧𝐜𝐞𝐧𝐭𝐢𝐯𝐞𝐬 📊 GP carry and staff bonuses tied directly to impact KPIs 💵 Loan pricing that rewards verified community benefit 🌦️ Guarantees & parametric insurance unlocking risk-taking in underserved markets 🏅 Certifications & reputation-based systems rewarding transparency and collaboration 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 🤝 Participatory due diligence with community-designed scoring rubrics 📈 Living impact reports & dashboards updated in real time with stakeholder input 🗳️ Co-created governance, advisory panels & reviewer councils drawn from impacted communities 📖 Narrative-based reporting to capture unintended consequences & emergent outcomes 𝐂𝐫𝐨𝐬𝐬-𝐜𝐮𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞𝐦𝐞𝐬 📚 Appetite for shared infrastructure—repositories of tools, templates & examples 🗺️ Calls for mapping actors & use cases to reduce duplication and identify gaps ✊ Embedding justice, equity & systems thinking into design, not just outcomes The takeaway? Our community is not just experimenting—it is actively redesigning the architecture of finance to be more inclusive, accountable, and purpose-driven. IFI will continue expanding the Resource Hub with the examples and resources shared, and will integrate these insights into upcoming Missions and our Community Directory. Huge thanks to my incredible team Maegan Storm Lillis Tasneem Jhetam Peter Chakaniza Daniel Bannister for their hard work and our co-facilitators for leading the conversations Maegan Moorehart Mairi-Jane Fox, PhD Juan Jardon-Pina Karim Harji Esme Verity Macarena Machimbarrena & Elizabeth Blacklin. #InnovativeFinance #ImpactInvesting #AdventureFinance #RadicalCollaboration

My first deep dive into MCP and A2A taught me that most AI teams are optimizing for the wrong things. In today’s landscape, companies are obsessed with individual models: Fine-tuning. Benchmarks. Leaderboards. One LLM to rule them all. But in this new Stanford University & George Mason University paper, I watched something radical unfold: an entire framework designed for collective intelligence, not model isolation. ↳The problem:"...Current systems are still facing challenges of inter-agent communication, coordination, and interaction...and to the best of our knowledge, very few applications exist where both protocols (MCP and A2A) are employed within a single Multi-Agent System (MAS) framework." ↳The proposed solution: "We present a pilot study of AgentMaster, a novel modular multi-protocol MAS framework with self-implemented A2A and MCP, enabling dynamic coordination, flexible communication, and rapid development with faster iteration." While most of industry is pouring money into single-model optimization, AgentMaster asked: What if we designed the infrastructure for agents to collaborate instead? Right now, we’re living in the “personal optimization” phase of AI: ➤ Bigger LLMs (instead of better orchestration). ➤ Ad hoc plugins (instead of standards). ➤ One agent per workflow (instead of teams). Meanwhile, AgentMaster shows what’s possible when you optimize for the collective: ➤ Orchestrator agents that decompose complex queries into subtasks. ➤ Domain agents specialized in SQL, IR, image analysis-working together via A2A. ➤ MCP for memory + tool access that makes agents interoperable. ➤ Benchmarks: 96.3% BERTScore F1 and 87.1% G-Eval, validated by both LLM-as-a-Judge and human reviewers. ↳The irony hit me hard: ➤ We keep scaling models to trillions of parameters… ➤ Yet our workflows crumble the moment we ask them to coordinate. AgentMaster proved that you don’t need a bigger model, you need a system that knows how to collaborate. Maybe the most disruptive thing we can build isn’t another giant LLM. It’s infrastructure that makes multi-agent collaboration inevitable. The future isn’t single-model optimization. It’s collective orchestration. And it starts by asking: What if we stopped optimizing individual models and started building systems? ------ *Please note: AgentMaster is a pilot study / proof-of-concept framework, not a production-ready system

In pursuit of #CollectiveSuperintelligence the team at Unanimous AI had a new pre-print paper published in collaboration with CMU. It describes a forecasting study of a real-world task (predicting 60 baseball games against the spread) using Hyperchat AI technology. If you are not familiar with Hyperchat AI, it enables large groups (up to 250 people) to engage in real-time conversational deliberations by text, voice, or video, and converge quickly on optimized solutions to questions posed. The methods used were able to surface winners with surprising accuracy. Of course, the goal is not forecasting sports - but enabling large human groups to amplify their collective intelligence to super-intelligent levels. Here is a link to the study: https://lnkd.in/g6YgCPTj Keep humans in the loop! Unanimous AI David Baltaxe Gregg Willcox Chris Hornbostel Hans Schumann Ganesh Mani Joe Rosenbaum Alec Baum, CFA Joe King Don Montanaro Sean Hurtig #collectiveintelligence #hyperchatAI #swarmAI #thinkscape Joshua Sitzer

𝗦𝘄𝗮𝗿𝗺-𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗘𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗔𝗜: 𝗙𝗿𝗼𝗺 𝗦𝗺𝗮𝗿𝘁 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝘁𝗼 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝘃𝗲𝘀 Most factories today still operate with highly capable but largely independent systems i.e robots, machines, sensors, and planning engines working in silos. The next step in industrial intelligence is coordinated intelligence across many agents. Imagine a warehouse where AGVs negotiate routes in real time like ant colonies optimizing food paths, or assembly lines that reorganize themselves as soon as a machine fails — no emergency meetings required. Swarm-intelligent embodied Industrial AI applies the principles observed in nature i.e ants, bees, and bird flocks — where simple entities collaborate to solve complex problems through collective behavior. This enables: • Self-organizing production flows • Real-time adaptive logistics and scheduling • Distributed resource allocation without heavy central control • Resilient operations where performance continues even if individual    nodes fail 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗲𝘀𝘁 𝗰𝗮𝘀𝗲: Amazon Robotics fulfillment centers already operate thousands of mobile robots coordinating inventory movement, routing, and task allocation in real time. These systems combine shared robotic autonomy with centralized orchestration, demonstrating how collective robotic intelligence can complement central planning to achieve highly scalable automation. Similar swarm-based fleet orchestration pilots are now expanding into manufacturing logistics and intra-plant material movement. The enabling foundation is the Cloud–Edge–Terminal architecture, where machines, robots, and sensors act as embodied agents, edge systems coordinate real-time decisions, and cloud platforms provide learning and long-horizon optimization. The real shift underway is powerful: Factories are moving from automation of machines to orchestration of intelligent agent ecosystems. Organizations that design systems for collective intelligence, rather than isolated AI deployments, will unlock the next wave of operational performance. #IndustrialAI #SwarmIntelligence #SmartManufacturing #EdgeAI #DigitalFactories

I have met countless changemakers who pour their hearts into building solutions that truly work. You see it in their eyes, the hope, the persistence, and the deep belief that communities can change when given the right tools. Yet again and again, we see powerful ideas lose momentum when a project ends or leadership changes. The problem is rarely a lack of commitment. It is how our systems are built. Too many social innovations live inside single organizations. They rely on the energy of a few people or the life of one funding cycle. When those stop, the impact often stops too. That is why the real challenge in social innovation is not just proving that change is possible, but ensuring it lasts. That is what Pathways to Scaling with Governments explores. The new guide looks at how innovators can move from running programs to transforming systems, embedding proven solutions into public institutions like education, health, and social protection so that what works locally can grow and endure nationally. This approach has shaped much of my journey through One Family Foundation, Child and Youth Finance International, Catalyst 2030, and now through GCSI. I have seen how partnerships built on trust can turn bold ideas into lasting systems. Real impact happens when innovators and governments work together, not in isolation. When collaboration replaces parallel effort, solutions move from small pilots to national reach. Scaling responsibly is not about expanding for the sake of growth. It is about designing for permanence. It is about building systems that can hold, adapt, and sustain change long after individual projects or organizations step back. This guide offers a practical roadmap for anyone ready to make that shift, for founders, funders, and policymakers who understand that lasting change is built through shared ownership and long-term commitment. You can read and download Pathways to Scaling with Governments here: https://rb.gy/uzqyaz #ScalingImpact #SystemsChange #PublicSystems #SocialInnovation #GCSI #OneFamily