Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insights, and solutions, and amplifying collective intelligence. A collaboration server running a collaboration application is provided, wherein the collaboration server is in communication with the plurality of the networked computing devices and each computing device is associated with one user of the population of human participants. In some cases, the collaboration server defines a plurality of sub-groups of human participants. A local video conferencing application on each networked computing device enables real-time communication with other users of a sub-group assigned by the collaboration server. According to some embodiments, an AI-mediated process enables communication to propagate across a plurality of parallel subgroups using simulated participants within each subgroup.

Abstract: The present disclosure describes systems and methods for enabling human groups to collaboratively generate accurate estimate values through conversational deliberation mediated by one or more artificial agents. In some embodiments, a conversational agent is provided on a networked computing device used by each of a plurality of human users. The system presents an estimation query to each user, captures that user's conversational response, and processes with a large language model to identify an estimation value, magnitude of support, and at least one supporting reason. An aggregated estimation value is computed and conversationally expressed to the group of users along with aggregated reasoning. The conversational agent requests a subjective assessment from the human users, for example, asking whether the aggregated value is too high or too low. Each user's conversational response is processed to determine an updated estimation, for example including a direction, magnitude, and reasoning.

Abstract: Methods and systems for AI-mediated groupwise conversation are described. For example, a collaboration server is in networked communication with a plurality of computing devices, each running a local application that enables a real-time conversation among at least one human member and at least one AI agent. This facilitates a plurality of parallel real-time conversations among human members and AI agents. In some cases, a repeatedly executed data sharing process shares conversational content among parallel real-time local conversations, thereby connecting conversations into a single larger-scale discussion. In some cases, a content extraction process extracts ideas and reasons from the conversational data. Each participant's exposure to extracted ideas or reasons may be tracked to coordinate the optimized sharing of ideas and reasons aimed at increasing each participant's exposure to ideas and reasons that have not yet been discussed their local real-time conversation.

Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insight extraction, and solutions, and amplifying collective intelligence. A plurality of computing devices associated with members of a population of participants is provided, wherein the plurality of computing devices is networked via a computer network and in communication with a central server. In some cases, the central server divides the population into a plurality of subgroups and enables an LLM-powered conversational surrogate agent associated with each subgroup. The conversational surrogate agent is configured to repeatedly observe a conversation among members of its assigned subgroup and store content of the conversation to extract insights from the conversation at appropriate intervals.

Abstract: The present disclosure describes systems and methods for amplified collective intelligence across large networked human populations by using deadbands and interconnected sub-groups. Disclosed are methods and systems for computer moderated collaboration among the population of human participants using a plurality of networked computing devices. A prompt may be presented to participants for collecting collaborative responses. Individuals within the population may each provide an initial response to the prompt during an initial time period. During a second time period, participants are enabled to update their responses while content is shared across participants. A forbidden deadband region defines an area around each participant's initial responses that updated responses must fall outside of, Sub-populations are defined for sharing initial and/or updated responses, enabling coherent interactions among manageable sized groups while leveraging the full benefits of the whole population.

Abstract: The present disclosure describes systems and methods for enabling conversational collaboration among a group of distributed human users over a computer network while facilitating convergence on groupwise decisions, insights, prioritizations, solutions, ideas, and forecasts. Each individual user is provided with a chat environment through which they can communicate via real-time dialog with at least one AI-powered conversational agent. Conversational dialog from each user is processed by a large language model to identify and extract key points which are then shared over a network with other chat environments associated with other users. Shared key points are expressed as conversational dialog by conversational agents in those other chat environments. In this way, a distributed group of users can communication, collaborate, and efficiently converge on solutions with the support of intervening conversational agents over a period of time.

Abstract: Methods and systems are disclosed for enabling AI-facilitated groupwise conversation among very large, networked human teams. For example, a very large human team is divided into a plurality of small parallel real-time conversations, each of said small conversations including at least one conversational AI agent. A hyperchat process connects the plurality of small conversations in real-time, thereby enabling the large, networked human team to hold a productive real-time conversation in which they collaboratively discuss issues, brainstorm ideas, debate options, rank alternatives, and converge on solutions with amplified collective intelligence. In this way, the methods and systems enable thoughtful and productive real-time conversations among networked human groups of unprecedented scale.

Abstract: Methods and systems for real-time conversational interaction with an embodied large-scale personified collective intelligence are described. For example, one or more users may converse in real-time with a personified collective intelligence (e.g., an AI-powered conversational agent that represents the collective ideas, perspectives, reasoning, knowledge and/or wisdom of a networked human group). In some aspects, users may hold a real-time dialog with a personified collective intelligence agent based on the real-time conversational interactions of plurality of networked human participants. For instance, networked participants may respond to inquiries in real-time, and a large language model may process the responses to determine a real-time collective intelligence response that is expressed by the personified collective intelligence agent (e.g., as first-person dialog voiced by an animated avatar).

Abstract: The present disclosure describes systems and methods for asynchronous conversational deliberation among a population of human participants. In some aspects, a collaboration server is connected to a memory containing a representation of conversational content that was previously captured from prior human participants, the representation associated with each prior participant including at least one stored idea, insight, view, or assertion expressed by that prior participant and at least one associated numerical measure of sentiment, conviction, confidence, or support. According to some embodiments, an asynchronous conversational process is enabled in which each of a plurality of new human participants engages in real-time dialog with a Conversational Surrogate Agent displayed by their associated computing device, each Conversational Surrogate Agent configured to express to at least one new participant, at least one stored idea, insight, view, or assertion associated with at least one prior participant.

Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insight extraction, and solutions, and amplifying collective intelligence. A plurality of computing devices associated with members of a population of participants is provided, wherein the plurality of computing devices is networked via a computer network and in communication with a central server. In some cases, the central server divides the population into a plurality of subgroups and enables an LLM-powered conversational surrogate agent associated with each subgroup. The conversational surrogate agent is configured to repeatedly observe a conversation among members of its assigned subgroup and store content of the conversation to extract insights from the conversation at appropriate intervals.

Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insights, and solutions, and amplifying collective intelligence. A collaboration server running a collaboration application is provided, wherein the collaboration server is in communication with the plurality of the networked computing devices and each computing device is associated with one user of the population of human participants. In some cases, the collaboration server defines a plurality of sub-groups of human participants. A local video conferencing application on each networked computing device enables real-time communication with other users of a sub-group assigned by the collaboration server. According to some embodiments, an AI-mediated process enables communication to propagate across a plurality of parallel subgroups using simulated participants within each subgroup.

Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insight extraction, and solutions, and amplifying collective intelligence. A plurality of computing devices associated with members of a population of participants is provided, wherein the plurality of computing devices is networked via a computer network and in communication with a central server. In some cases, the central server divides the population into a plurality of subgroups and enables an LLM-powered conversational surrogate agent associated with each subgroup. The conversational surrogate agent is configured to repeatedly observe a conversation among members of its assigned subgroup and store content of the conversation to extract insights from the conversation at appropriate intervals.

Abstract: The present disclosure describes systems and methods for large-scale conversational deliberation and conversational collective intelligence with selectable levels of synchrony. In some aspects, a collaboration server defines a plurality of sub-groups of networked computing devices (e.g., human participants). Each subgroup may be associated with a conversational surrogate agent configured to observe real-time conversational deliberation among participants of its subgroup, pass insights to and receive insights from other conversational surrogate agents associated with other subgroups, and conversationally express insights received from other subgroups. Additionally, each subgroup may be associated with a conversational contributor agent configured to participate in local conversation of that subgroup by offering answers, insights, opinions, or factual information that is independently AI generated rather than being derived based on conversational content of participants in other subgroups.

Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insights, and solutions, and amplifying collective intelligence. A collaboration server running a collaboration application is provided, wherein the collaboration server is in communication with the plurality of the networked computing devices and each computing device is associated with one user of the population of human participants. In some cases, the collaboration server defines a plurality of sub-groups of human participants. A local chat application configured for displaying a conversational prompt received from the collaboration server is provided on each networked computing device. The local chat application enables real-time communication with other users of a sub-group assigned by the collaboration server.

Abstract: Methods and systems for real-time conversational interaction with an embodied large-scale personified collective intelligence are described. For example, a user may communicate with a personified collective intelligence (e.g., an artificial intelligence (AI) powered conversational agent based on aggregated input collected from a large number of human participants). In some aspects, the user may ask questions and/or hold a conversation with to a real-time personified collective intelligence agent, which may respond, to inquiries received from the user, based on real-time responses of a plurality of human participants. For instance, a plurality of human participants may respond to the inquiries, and a large language model may process (e.g., receive, analyze, and aggregate) the plurality of inquiry responses to determine a collective intelligence response that is expressed by the personified collective intelligence agent (e.g., in a first-person conversational form to the user).

Abstract: System and method for amplifying the accuracy of forecasts generated by software systems that harness the collective intelligence of human populations by curating optimized sub-populations through an intelligent selection process. Participants predict event outcomes and/or provide evaluations of their confidence in their predictions. The system determines a score wherein the alignment score indicates how well that participant's prediction aligns with the predictions given by the baseline population. Participants can then be selected from the population based on the participant alignment scores.

Abstract: Systems and methods are disclosed for amplifying the collective intelligence of networked human groups engaged in collaborative decision-making. Specifically, systems and methods are disclosed to enable networked human groups to collaboratively rank a set of answer options in response to a displayed question prompt. In some embodiments a distribution of unique aggregated rankings is computed based on the received ranking responses, and one of the unique aggregated rankings communicated to networked computing devices. In some embodiments an updating ranking response is received from the networked computing devices. In some embodiments a final collaborative ranking is computed based on the received updated ranking responses from the plurality of networked computing devices.

Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insights, and solutions, and amplifying collective intelligence. A collaboration server running a collaboration application is provided, wherein the collaboration server is in communication with the plurality of the networked computing devices and each computing device is associated with one user of the population of human participants. In some cases, the collaboration server defines a plurality of sub-groups of human participants. A local chat application configured for displaying a conversational prompt received from the collaboration server is provided on each networked computing device. The local chat application enables real-time communication with other users of a sub-group assigned by the collaboration server.

Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insight extraction, and solutions, and amplifying collective intelligence. A plurality of computing devices associated with members of a population of participants is provided, wherein the plurality of computing devices is networked via a computer network and in communication with a central server. In some cases, the central server divides the population into a plurality of subgroups and enables an LLM-powered conversational surrogate agent associated with each subgroup. The conversational surrogate agent is configured to repeatedly observe a conversation among members of its assigned subgroup and store content of the conversation to extract insights from the conversation at appropriate intervals.

Abstract: Systems and methods for amplifying the intelligence of networked human populations, maximizing the accuracy of collaborative forecasts and group insights. This includes systems and methods for sending and presenting a forecasting query for a future event to a plurality of participants, each using a networked computing device, the forecasting query describing a future event to be collaboratively predicted by the population of human participants. An initial forecast response is collected from each participant and analyzed by a central server. A plurality of unique overlapping subsets of responses are determined by the server. Unique overlapping subsets of unique initial forecast responses are then displayed, at substantially the same time, on each computing device. A second forecast response is collected from each participant and a final forecast is determined.