He was surrounded by seven staff from the world's leading artificial intelligence lab, which had launched ChatGPT a few months earlier. One of them was Wojciech Zaremba, an OpenAI co-founder. For over a decade, Megill had been toiling in relative obscurity as the co-founder of Polis, a nonprofit open-source tech platform for carrying out public deliberations. Democracy, in Megill's view, had barely evolved in hundreds of years even as the world around it had transformed unrecognizably. Each voter has a multitude of beliefs they must distill down into a single signal: one vote, every few years. The heterogeneity of every individual gets lost and distorted, with the result that democratic systems often barely reflect the will of the people and tend toward polarization.

How can many people (who may disagree) come together to answer a question or make a decision? "Collective response systems" are a type of generative collective intelligence (CI) facilitation process meant to address this challenge. They enable a form of "generative voting", where both the votes, and the choices of what to vote on, are provided by the group. Such systems overcome the traditional limitations of polling, town halls, standard voting, referendums, etc. The generative CI outputs of collective response systems can also be chained together into iterative "collective dialogues", analogously to some kinds of generative AI. Technical advances across domains including recommender systems, language models, and human-computer interaction have led to the development of innovative and scalable collective response systems. For example, Polis has been used around the world to support policy-making at different levels of government, and Remesh has been used by the UN to understand the challenges and needs of ordinary people across war-torn countries. This paper aims to develop a shared language by defining the structure, processes, properties, and principles of such systems. Collective response systems allow non-confrontational exploration of divisive issues, help identify common ground, and elicit insights from those closest to the issues. As a result, they can help overcome gridlock around conflict and governance challenges, increase trust, and develop mandates. Continued progress toward their development and adoption could help revitalize democracies, reimagine corporate governance, transform conflict, and govern powerful AI systems -- both as a complement to deeper deliberative democratic processes and as an option where deeper processes are not applicable or possible.

Take focus groups, for example. We have entered an era that is allowing brands to have meaningful engagements and conversations with consumers on a much larger scale than ever before. Online platforms that utilize AI to analyze and understand text in real-time and on a mass scale have emerged as a new methodology for understanding consumers. Remesh is an example of an AI-driven tool that can help liven up online focus groups by enabling participants to chat with a real-life moderator in real-time. Participants are also able to see what others are saying through a voting system, where they are asked to vote amongst different answers for the one they agree with most.

Newswire) Remesh, a software company that is reinventing market research through artificial intelligence (AI), today announced the closing of its $2.25 million seed investment round that brings its total funding to $3.85 million. The round is led by LionBird Ventures, a venture capital firm investing in early stage digital health and business services companies with offices in Tel Aviv and Chicago. The round also includes Reimagine Holdings Group, a holding company focused on growing consumer insights and marketing services companies, as well as individual investors, representing a mix of new and returning investors. "We believe that Remesh has shown real potential to change the way brands, consultants and agencies listen to feedback from their audiences," said Ed Michael, Managing Partner at LionBird Ventures. "Remesh has recognized a way to solve for a number of inefficiencies in market research using artificial intelligence. This new model not only replaces legacy systems, but establishes entirely new market research workflows."