Uploaded May 2024 | Updated September 2026, 14 minutes ago
Join us this Tuesday, May 7th, 2024, at 5pm UTC for the first session of a special two-part SingularityNET's Technical Tuesdays mini-series dedicated to the latest advancements in the development of a unified experiential learning component for OpenCog Hyperon.
Session 1
- Demonstrating how NACE deals with uncertainty and revision of the rules in real-time by applying Non-Axiomatic Logic (NAL) aspects;
- Integrating the AIRIS (Autonomous Intelligent Reinforcement Interpreted Symbolism) causality-based learning AI into Hyperon.
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NACE: github.com/patham9/NACE
ONA-style NARS implementation in MeTTa: github.com/patham9/metta-nars
AIRIS: github.com/berickcook/AIRIS_Public
AIRIS 2023 Demo - youtube.com/watch?v=40W2OmV_rm0
AIRIS Experiential Minecraft Demo - youtube.com/watch?v=IzenbCwIlkg
---
These advancements are part of our ongoing initiative to consolidate the strengths of several systems —ROCCA, NARS, OpenPsi, and AIRIS— to create a unified experiential learning component for Hyperon. This approach will allow AI models to:
- Develop a goal-independent understanding of their environment through causal knowledge gained from planned and spontaneous interactions;
- Explore their environment with increased efficiency using a curiosity model that prioritizes situations with high uncertainty, challenging their existing causal knowledge.
Our preliminary findings indicate that this approach surpasses common Reinforcement Learning techniques in terms of data efficiency by orders of magnitude.
#AGI #OpenCogHyperon #AI
Join us this Tuesday, May 7th, 2024, at 5pm UTC for the first session of a special two-part SingularityNET's Technical Tuesdays mini-series dedicated to the latest advancements in the development of a unified experiential learning component for OpenCog Hyperon.
Session 1
- Demonstrating how NACE deals with uncertainty and revision of the rules in real-time by applying Non-Axiomatic Logic (NAL) aspects;
- Integrating the AIRIS (Autonomous Intelligent Reinforcement Interpreted Symbolism) causality-based learning AI into Hyperon.
---
NACE: github.com/patham9/NACE
ONA-style NARS implementation in MeTTa: github.com/patham9/metta-nars
AIRIS: github.com/berickcook/AIRIS_Public
AIRIS 2023 Demo - youtube.com/watch?v=40W2OmV_rm0
AIRIS Experiential Minecraft Demo - youtube.com/watch?v=IzenbCwIlkg
---
These advancements are part of our ongoing initiative to consolidate the strengths of several systems —ROCCA, NARS, OpenPsi, and AIRIS— to create a unified experiential learning component for Hyperon. This approach will allow AI models to:
- Develop a goal-independent understanding of their environment through causal knowledge gained from planned and spontaneous interactions;
- Explore their environment with increased efficiency using a curiosity model that prioritizes situations with high uncertainty, challenging their existing causal knowledge.
Our preliminary findings indicate that this approach surpasses common Reinforcement Learning techniques in terms of data efficiency by orders of magnitude.
#AGI #OpenCogHyperon #AI
