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Interactional Motivation (IM)
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Type SingularityNET RFP
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Funding Awarded $20,000 USD
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RFP Guidelines Develop a framework for AGI motivation systems
olivier Georgeon
May 23, 2025Preview
olivier Georgeon
project ownerInteractional Motivation (IM)
Interactional Motivation (IM) implements self-motivation in artificial agents by assigning value to agent-environment interactions themselves, rather than valuing the outcome or states of the environment. Unlike traditional methods that rely on predefined goals or external rewards, IM drives agents to autonomously organize behavior and construct knowledge from their experience of interaction. This aligns with genetic epistemology and theory of enaction. Applying IM within AGI frameworks offers a pathway to study autonomous cognitive development, constitutive autonomy, and autonomous goal construction, potentially moving beyond traditional reinforcement learning and problem-solving AI.
GlucoseDAO
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Project for BGI Nexus 1
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Funding Awarded $50,000 USD
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Funding Pool Beneficial AI Solutions
- Milestones 0 / 5 Completed
Livia_Zaharia
Feb. 24, 2025Preview
Livia_Zaharia
Project ownerGlucoseDAO
GlucoseDAO is a decentralized organization that enables diabetics and others with continuous glucose monitors (CGMs) to get accurate glucose predictions at least one hour in advance. For millions of diabetic people, such predictions are crucial to planning their everyday lives (when to inject, act, or do sports). It is also helpful for healthy people as it allows them to optimize their diet and exercise routines based on glucose patterns. What we are developing: -Extension of GlucoBench benchmark that will also measure human performance -Open-source ML model to predict glucose and related health outcomes -ML service and app that everybody can use easily
NEURAL SEARCH
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Project for Round 4
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Funding Awarded $150,000 USD
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Funding Pool New projects
- Milestones 2 / 4 Completed
Luke Mahoney (MLabs)
May 20, 2024Preview
Luke Mahoney (MLabs)
Project ownerNEURAL SEARCH
Neural Search is a new way to incorporate Monte Carlo Tree Search into neural networks. Unlike similar techniques, Neural Search builds the exploration and optimization of the search into the layers of the network. The search itself is learnable, and provides us with a means to develop guided search algorithms trained on data. We have demonstrated that the algorithm can learn robust solutions to search problems. We propose further development of the approach to large scale problems, and to demonstrate the effectiveness of the algorithm by training it to play Go, a standard achieved AlphaGo. Total for the project is $150K over 11 months, and it will be resourced by 3 senior MLabs members.
Unique Knowledge Graph for Source and Content Reliability
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Project for Round 3
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Funding Awarded $40,000 USD
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Funding Pool SingularityNET RFPs
- Milestones 6 / 6 Completed
Dominik Tilman
Sep. 14, 2023Preview
Dominik Tilman
Project ownerUnique Knowledge Graph for Source and Content Reliability
Project Tags:
TrustLevel proposes a comprehensive solution to assess the reliability and objectivity of textual information across various domains. Their approach involves integrating diverse data sources, structured knowledge graph development, and multifaceted analysis. By doing so, they aim to provide users with a powerful tool for evaluating text objectivity.
The project consists of several key components, including sentiment analysis, named entity recognition, clickbait detection, source reputation analysis, and the creation of a structured knowledge graph. Users will have access to a query interface that allows them to interact with the knowledge graph and perform complex analyses.
TrustLevel outlines a detailed budget breakdown for the project, divided into five milestones, spanning 14 weeks. Each milestone focuses on specific aspects of the project, such as system architecture design, knowledge graph development, text analysis integration, query interface development, and final documentation.
To mitigate potential risks, TrustLevel emphasizes proactive communication, compatibility analysis, and alternative APIs/sources in case of technical challenges or data quality issues. They also provide optional tasks to ensure project completion even if the scope or timing becomes a concern.
Overall, TrustLevel's proposal demonstrates a well-structured plan to address the RFP's goals by leveraging a knowledge graph and various text analysis tools to assess text objectivity comprehensively.
Socious: AI-Based Job Matching for Social Good
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Project for Round 3
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Funding Awarded $50,000 USD
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Funding Pool New projects
- Milestones 5 / 5 Completed
seirayun
Sep. 3, 2023Preview
seirayun
Project ownerSocious: AI-Based Job Matching for Social Good
Project Tags:
Socious is addressing the gap between young people's desire to use their skills for social and environmental good and their ability to find such jobs. Their innovative AI solution curates purpose-driven jobs and matches them with individuals based on their passion and skills. By considering social causes, they aim to create a more meaningful match between talent and impact jobs. Socious operates as a Decentralized Autonomous Organization (DAO) and aligns with SingularityNET's vision of decentralized AI for a beneficial Singularity. They have achieved significant traction, listing over 70,000 impact jobs, registering 22,000 impact organizations, and attracting over 5,000 purpose-driven professionals. Their project milestones include solution architecture, completing the Impact Job Detector and Matching Engine, SingularityNET platform onboarding, and marketing efforts. Socious plans to launch a utility token, THANK, and allocate 10% of the pre-seed round allocation to SNET/Deep Funding to support their mission further.
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