SingularityNET

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Golang SDK Development for SingularityNET AI Marketplace

The SDK will enable seamless integration with key components like Multi-Party Escrow (MPE), the Registry, and IPFS, serving both AI developers and consumers. It must maintain backward compatibility with the existing Python SDK, offer intuitive interfaces for managing identities, organizations, and services, and include strong security and scalability features. Comprehensive documentation is required to ensure ease of use and long-term maintainability.

Project Catalyst

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Development of a Cardano-Based MultiParty Escrow (MPE) System

The MultiParty Escrow (MPE) smart contract is a critical component of the SingularityNET platform's integration with the Cardano blockchain. Designed to manage payment channels within SingularityNET’s Decentralized AI Platform and AI Marketplace, this contract enables secure, decentralized transactions between clients and AI service providers. By leveraging Cardano’s Extended UTxO (EUTxO) model, this solution facilitates efficient and trustworthy interactions, ensuring streamlined payments for AI services.

SingularityNET

rfp owner

Utilize LLMs for modeling within MOSES

This RFP invites proposals to explore the integration of LLMs into the MOSES evolutionary algorithm. Researchers can pursue one of several approaches, including generation modeling, fitness function learning, fitness estimation, investigation into domain-independent “cognitively motivated” fitness functions, or propose new innovative ways to leverage LLMs to enhance MOSES's capabilities within the OpenCog Hyperon framework.

SingularityNET

rfp owner

Framework for evaluating approaches to attention allocation

The goal of this project is to develop a framework to evaluate various approaches to Attention Allocation (AA) within the OpenCog Hyperon and PRIMUS architectures. The AA system dynamically allocates cognitive resources to Atoms in the Distributed Atomspace (DAS), and this framework will help assess AA methods based on desired cognitive dynamics. The framework will improve both Probabilistic Logic Networks (PLN) and evolutionary methods like Meta-Optimizing Semantic Evolutionary Search (MOSES), which are critical components of the PRIMUS architecture.

SingularityNET

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Experiment with concept blending in MeTTa

This RFP seeks proposals that experiment with concept blending techniques and formal concept analysis (including fuzzy and paraconsistent variations) using the MeTTa programming language within OpenCog Hyperon. The goal is to explore methods for generating new concepts from existing data and concepts, and evaluating these processes for creativity and efficiency.

SingularityNET

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AGI related hardware

This RFP seeks to explore and evaluate innovative hardware paradigms, ranging from small-scale IoT devices to large, decentralized systems, to optimize AGI workloads in the OpenCog Hyperon framework and handle advanced processes such as cognitive synergy and hyperdimensional computing. The focus is on assessing hardware paradigms and emerging architectures (e.g. neuromorphic processors, associative processors such as TensTorrent’s APU, etc). The goal is to enhance computational efficiency, scalability, and cognitive synergy in AGI systems. Part of this should involve interacting with the Hyperon team who've built the existing and in-development MeTTa interpreters.

SingularityNET

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Review of quantum computing technologies

This RFP seeks to critically evaluate the role of quantum computing in advancing Artificial General Intelligence (AGI). The goal is to distinguish between realistic capabilities and hype, providing clear insights into the practical benefits and limitations of quantum computing for AGI architectures, particularly within the OpenCog Hyperon framework. Part of this should involve interacting with the Hyperon team who've built the existing and in-development MeTTa interpreters.

SingularityNET

rfp owner

Neuro-symbolic DNN architectures

This RFP invites proposals to explore and demonstrate the use of neuro-symbolic deep neural networks (DNNs), such as PyNeuraLogic and Kolmogorov Arnold Networks (KANs), for experiential learning and/or higher-order reasoning. The goal is to investigate how these architectures can embed logic rules derived from experiential systems like AIRIS or user-supplied higher-order logic, and apply them to improve reasoning in graph neural networks (GNNs), LLMs, or other DNNs.

SingularityNET

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PLN guidance to LLMs

This RFP seeks proposals to explore how Probabilistic Logic Networks (PLN) can be used to provide guidance to LLMs. We are particularly interested in applying PLN to develop an alternative to graphRAG for augmenting LLM memory using Atomspace knowledge graphs.

SingularityNET

rfp owner

Causal learning guided PLN inference control

Implementing causal, versus strictly correlative, inference can lead to a deeper understanding of many complex problems. Probabilistic Logic Networks (PLN), with their robust mathematical foundations for handling uncertain and incomplete knowledge—such as induction, abduction, analogy, and reasoning about time—offer a strong framework for this task. This project has two main goals:

1. To guide PLN inference control using causal networks.
2. To explore how PLN rules can distinguish causal from correlational relationships.

Proposals can address either of these goals separately or explore both together and the feedback between them. Submissions should include testable examples to validate the approach and demonstrate how causal reasoning improves PLN’s overall performance.

SingularityNET

rfp owner

Create corpus for NL-to-MeTTa LLM

Develop a MeTTa language corpus to enable the training or fine-tuning of an LLM and/or LoRAs aimed at supporting developers by providing a natural language coding assistant for the MeTTa language.

SingularityNET

rfp owner

Develop a framework for AGI motivation systems

Develop a modular and extensible framework for integrating various motivational systems into AGI architectures, supporting both human-like and alien digital intelligences. This could be done as a highly detailed and precise specification, or as a relatively simple software prototype with suggestions for generalization and extension.

SingularityNET

rfp owner

Implement clustering heuristics in MeTTa

The goal is to implement clustering algorithms in MeTTa and demonstrate interesting functionality on simple but meaningful test problems. This serves as a working prototype providing guidance for development of scalable tooling providing similar functionality, suitable for serving as part of a Hyperon-based AGI system following the PRIMUS cognitive architecture.

SingularityNET

rfp owner

Evolutionary algorithms for training transformers and other DNNs

Explore and demonstrate the use of evolutionary methods (EMs) for training various DNNs including transformer networks. Such exploration could include using EMs to determine model node weights, and/or using EMs to evolve DNN/LLM architectures. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is an example of one very promising evolutionary method among others.

SingularityNET

rfp owner

Develop interesting demos in MeTTa

Create educational and/or useful demos using SingularityNET's own MeTTa programming language. This RFP aims at bringing more community adoption of MeTTa and engagement within our ecosystem, and to demonstrate and expand the utility of MeTTa. Researchers must maintain demos for a minimum of one year.