AI-guided co-creation

Automating the acquisition of collective knowledge

Creativity and imagination for novel and disruptive innovations remain in the realm of human co-creativity. For that reason, we set out to build a new approach to AI that closely resembles the MIT Center for Collective Intelligence’s mission to create technology that combines human and machine intelligence in a system that collectively acts more intelligently than any person, group, or computer. A hybrid form of AI and human intelligence promises a more predictive path in a business environment under pressure to become more decentralized and adaptive.

  1. Recent advances in collective intelligence show that collectively, we are more accurate in predictions and decision-making as a cognitively diverse collective.³ A cognitively diverse group always outperforms an individual expert.⁴ Collective intelligence is fundamental to human knowledge development, as evidenced by the scientific method. We build lasting knowledge through collaboration that centers on peer-reviewed and supporting evidence. Human collective intelligence is believed to underlie the remarkable success of human society Scalable knowledge acquisition requires instrumenting the process of diverse groups of humans working together to co-create solutions.⁵ For collective intelligence to work, we must pay attention to the rule: cognitive diversity in the participants increases the predictive accuracy of the results.
  2. Cognitive science, linguistics, and representations of human knowledge provide the interface between humans and machines. For humans to understand the recommendations and actions of machine intelligence, we need explanatory models in natural language. Deep learning transformer models popularized as “large language models” provide a present and practical bridge between humans and machines. We use these technologies to capture the reasoning and results of people working together to solve a problem. From their discussions and deliberations, we create models of their collective knowledge. Deep learning transformer language models provide the means to extract knowledge from collaborative work.
  3. Complex adaptive systems are extremely hard to predict. An AI that learns patterns of knowledge from human interactions is grounded in the mathematics of detecting emergent patterns. The new tools require technologies that enable navigation in a complex adaptive environment. Google search is an example of applying the mathematics of complex adaptive systems to a practical problem. Early attempts to organize web searches by creating directories (Yahoo) gave way to PageRank, based on a complex adaptive system model used in discovering emergent patterns (Markov modeling). To the user, it simply means speed and accuracy when you type into the box. Harnessing the power of a complex adaptive algorithm led to a simple interface with a complex dynamic system that is massive and forever changing. Co-creation is a complex adaptive emergent process that yields answers to questions we did not think to ask. To the users, it simply means making decisions faster with less endless arguing and greater confidence.
  1. Decide the key questions that guide the discussion and deliberation process (solution template)
  2. Invite as many participants as needed to tap into their knowledge
  3. Participate in an asynchronous, single-blind engagement
  4. Get real-time results
  1. Quality of the business opportunity (Is it a compelling opportunity)
  2. Fitness of the team to the business needs
  3. The networking power of initial investors and advisors
  4. Likely to invest, which covers conviction about the investment opportunity (deal terms etc.)

Why is this relevant to a new process of guided problem-solving and co-creation?

All innovations face three questions similar to the startup case: 1. Will it work, 2. Is it worthy of funding and resourcing 3. Can we measure its impact? These types of questions apply to all innovative projects.

Getting started

One simple way to get started with AI co-creation is in your next Zoom video conference call. For groups larger than 10, it becomes increasingly difficult to give everyone a voice and to drive the meeting to a set of priorities for a conclusive outcome. CrowdSmart Lite is an app in the Zoom app store. You might ask, “What can we do to make everyone in our video conference calls feel more engaged?”



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Tom Kehler

Tom Kehler


I love pioneering transformative technologies based on solid science. Co-founder and Chief Scientist at CrowdSmart.