OpenCog Hyperon is an open-sourced platform where different #AI modules — such as neural networks (#NNs/DNNs), generative AIs, probabilistic AIs, program learning AIs, and others — can collaborate based on a shared knowledge metagraph.
The cognitive architecture of OpenCog Hyperon is based on MeTTa (Meta Type Talk), an AGI programming language that allows different AI modules to work together to achieve results that none of them could accomplish individually.
MeTTa is designed to combine conventional programming & reasoning as a chain of queries to a metagraph that can store either program expressions or knowledge. It can rewrite its own source code, creating advanced opportunities for self-optimization.
Hyperon allows #AI modules to coordinate & solve problems together, much like humans do by combining intuition, experience, insight, and logic to solve a single problem. It achieves this by using a set of tools such as pattern mining and attention allocation.
DAS is Hyperon's long-term memory where AI modules store their knowledge. Domain-specific information is stored together with more basic, high-level knowledge, such as the semantic relations between words or mathematical formulas and concepts.
Hyperon uses DSLs as a dialect of MeTTa with domain-specific primitives designed to make it easy to encode the AI components and knowledge base of an application. Therefore, we have different DSLs for financial markets, biotechnology, etc.
Hyperon’s architecture provides a scalable learning space and knowledge store to the various AI, and a set of tools, allowing them to coordinate to learn and solve problems together — like humans do.
2/ The article discusses Artificial Intelligence and how the SingularityNET Ecosystem and its projects are creating a network of narrow #AIs that all interconnect using #blockchain technology to create a #decentralized and benevolent Artificial General Intelligence.
"The emergence of true #AGI... will be in many ways the most complex achievement...of the human species — as well as presumably the last ...since once AGIs exist they will be more adept at science and technology than human beings."
@CogitoProtocol 1/ @CogitoProtocol is a project that offers a ""stablecoin-as-a-service"" framework, allowing for the creation of an entirely new class of digital assets with low volatility called #Tracercoins
@CogitoProtocol 2/ Unlike traditional stablecoins, which are typically pegged to a single fiat currency or commodity, #Tracercoins maintain their stability by soft-pegging to non-financial indices that represent progress along various developmental fronts.
🧵 Enabling real-world use cases at scale on the #SingularityNET platform via creating a MeTTa-based DSLs focused on smart contract programming in specific industry verticals.
2/ Smart contract scripting can be tricky, so it’s essential to design a developer-friendly framework to build applications and solutions without compromising on security.
3/ On #Ethereum, a “copy &paste” culture has emerged, where developers copy and tweak existing Solidity code. However, this approach is unsustainable in a software ecosystem powered by tools with reasonable composability.
The SNET mission combines ambitious & visionary development towards:
- Artificial General Intelligence (#AGI),
- Decentralized AI Marketplace,
- Beneficial #AI use across strategic vertical markets,
2) Delivering steadily & consistently throughout 2022, there was a lot to celebrate by end of the year:
- MeTTa & DAS development toward @OpenCog AGI framework
- 22 new AI services & Training models added to the Platform/Marketplace
- Two Phases of AI-DSL project complete
...
3) - Converter Bridge & Loyalty Program Launched (#BuildingOnCardano)
- @Hypercycle_AI ledger less sidechain++ for #Cardano microtransactions kicked off
- Deep Fund rnd 1 complete, governance vote, and 2 initiated
- Ambassador Program launched
- Decentralization Report published
1) NNs, LLMs, Diffusion - narrow #AI's are showing exciting capabilities right now. So, why do we need #AGI?
Simply, these algorithms are amazing pattern-recognizers...but they don't understand what they are doing.
A classic example of their unfortunate limitations:
2) A seemingly perfect-accuracy AI-driven tumor detector from tissue images... failed completely when given images from a new hospital.
On initial training data - 98% accurate; on data from this new source, it was no better than a coin flip
3) Turned out, all the training images with a tumor had a little white ruler in the picture to measure the size of the tumor. The new hospital did not include rulers in any images.
The model didn't understand tumors, and so has learned to be an amazing ruler detector.
2) The AI-DSL project is creating a revolutionary, intelligent interconnectivity layer to augment the utility of every service on the SNET Marketplace.
The protocol will allow AIs to be linked like legos & assembled into complex workflows by anyone, even non-technical users.
3) Phase 2 was funded by #Cardano Catalyst Fund7, with enthusiastic support for the #CardanoCommunity and this project, along with @Hypercycle_AI, will massively augment Cardano as the blockchain of AI