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Computing hardware architecture has evolved from maximizing single-core performance to multiplying parallel processing capacity through more cores, more threads, and higher computational density. This transition demands software architectures specifically designed to exploit parallel processing, especially in advanced AI and blockchain-based systems. As Greg Meredith, CEO of our partner F1R3FLY. io, notes, "We need software architectures that can eat physical threads of computation and turn that into throughput." Traditional sequential programming models often struggle to effectively leverage the potential of these multi-core systems, creating a bottleneck in performance scaling. Rholang addresses this challenge through its foundation in the Rho-Calculus, a reflective higher-order process calculus specifically designed for inherent concurrency and reflection. Unlike computational models based on sequential execution or those that bolt on concurrency primitives, Rholang can leverage its process-oriented nature to detect and distribute non-interfering logical computation threads across available physical processing units, creating a more direct correlation between hardware resources and performance scaling. The Rho-Calculus was specifically designed to provide the minimal set of operations needed to model autonomous agents operating in parallel while maintaining the ability to represent their own reasoning processes. This structure mirrors the operational characteristics of intelligent systems, where autonomous processes run independently while communicating and coordinating. For our novel decentralized AI platform, MeTTaCycle, this future-proof architectural foundation enables more than just enhanced transactional throughput; it fosters an environment where independent computational processes can efficiently coexist and interact, forming a critical layer for the @ASI_Alliance's decentralized AGI infrastructure and products.

U.S. President Donald Trump announced a 500 billion investment in AI infrastructure. 'For AGI to get built here,’ said Sam Altman, CEO of OpenAI. Will these multi-billion data centers get us to AGI, or are we throwing money at the wrong problem? ↓

Join us this Friday, March 21, at 3:30 p.m. UTC, for a new MeTTa Coders session to explore and discuss the latest advancements in the MeTTa AGI language. Add our biweekly meetings to your calendar and stay updated on future sessions: bit.ly/SNET-Events

We are excited to announce that @MindChildren_AI has signed a commercial agreement with AIMindbot in preparation for the mass production of the Mindbot humanoid robot in South Korea in 2026. Learn more and read a statement from Mind Children CEO, Chris Kudla:

Our Annual Report for 2024 is now available, highlighting advancements in beneficial AGI R&D and advocacy, decentralized AI Platform development, SingularityNET Compute, and broader ecosystem developments: singularitynet.io/singularitynet…

We laid the foundation for a decentralized and scalable AGI supercomputing ecosystem. Key initiatives included developing strategic infrastructure, forging global partnerships, and optimizing AI workloads to support AGI applications. Learn more about SingulairtyNET Compute ↓

We formalize and quantify the concept of ”intensional inheritance” between two concepts to guide reasoning in AI and AGI systems, particularly in OpenCog Hyperon and its Probabilistic Logic Networks (PLN) reasoning system: arxiv.org/abs/2501.17393

MeTTa-Motto: Combining DeepSeek and Advanced LLMs with Symbolic Knowledge and Reasoning Our MeTTa-Motto library facilitates the integration of LLMs with MeTTa, allowing developers to leverage the capabilities of both technologies. This integration enables the development of AI applications that utilize natural language processing and symbolic reasoning. MeTTa-Motto recently expanded to include integrations with LangChain infrastructure and OpenRouter to increase developers' flexibility. This provides access to a wider range of LLMs, including Claude, ChatGPT, and DeepSeek if needed. The key technical distinction of MeTTa-Motto lies in its ability to bridge LLM interactions with MeTTa's logical processing. This enables: - Composing and chaining LLM calls using MeTTa scripts; - Processing LLM outputs as MeTTa expressions for further reasoning; - Using MeTTa's symbolic knowledge to construct targeted prompts; - Managing stateful dialogues through specialized agents; - Accessing external tools and data sources through LangChain integration. To explore the capabilities of MeTTa-Motto and learn how to leverage it in your projects, please explore the official documentation and tutorials at metta-lang.dev/docs/learn/tut…

What if we could make AI learn more like the human brain - with real-time adaptation and natural reasoning? Active Predictive Coding (ActPC) offers a biologically-inspired solution that could transform how neural networks learn and think. Discover how ↓

Join @nunet_global's technical roadmap discussion for 2025, including: - OpenCog Hyperon integration; - @ASI_Alliance node design and integration; - Support for Cardano node deployment; - Machine Learning, AI, and LLM platforms integration. Learn more: bit.ly/NuNet-R2025

We believe there are at least three concrete paths to true AGI, notably our hybrid neural-symbolic Hyperon framework. However, the path to beneficial AGI isn't through corporate control, but through open source development decentralized governance. The race is on @ASI_Alliance.

With the development of OpenCog Hyperon, we aim to take the world from today's small-scale, training data-reliant AI systems to tomorrow's autonomous, cognitively capable machines. We are proud of our R&D progress in 2024 and look forward to sharing updates with the community.

For those interested in the niggly little problem of how to encourage self-modifying AGI systems to maintain invariants related to their goal systems (e.g. goal stability or moderation of the pace of goal evolution) ... some new thoughts on this here ...arxiv.org/abs/2412.16559 ... No magical perfect solution is offered, but a concrete direction for both experimental and theoretical R&D, which can be pursued for example in conjunction with our work scaling up OpenCog Hyperon and using it to implement PRIMUS HLAGI architecture ... @OpenCog @ASI_Alliance

ASI CORTEX - TLDr ASI is dedicated to building advanced foundational models across various areas of AI, with a special emphasis on robotics. Our initial goal is to develop and train Cortex, a model designed to improve robotic capabilities significantly through cutting-edge AI. By drawing on recent progress in Large Language Models and multimodal systems, we aim to create robots that do more than follow pre-set scripts. They will understand their surroundings, adapt to changes, and learn over time. A key focus is on Vision-Language-Action (VLA) models, which guide robots using natural language instructions and visual feedback. By cleverly using tokens from large language models to represent robotic movements, these VLA systems can efficiently plan and execute tasks. Building upon open-source efforts like OpenVLA, we seek to push these models toward state-of-the-art performance. While VLA models form the core of our initial efforts, we remain open to integrating other advanced AI techniques. This may include brain-inspired structures, recurrent networks, and reinforcement learning modules. Such approaches will allow robots to refine their actions through trial and error, coordinate with other machines, and plan complex steps in dynamic environments. The potential applications are vast: from streamlining manufacturing and logistics to improving healthcare services and enhancing everyday tools. Estimates suggest that more capable, adaptive robots could yield significant financial benefits, ranging from hundreds of millions to billions of dollars annually. By focusing on delivering working AI models—like improved VLA systems—we aim to accelerate progress toward decentralized AGI and, ultimately, artificial superintelligence. Yet developing AI that understands and interacts naturally with the physical world remains challenging. Traditional robotics has depended on rigid, rule-based methods, but newer multimodal LLMs show promise in bridging this gap. Although practical real-world use is still limited, our project’s mission is to overcome these barriers. Through careful development, simulation-driven training, and continuous refinement, we hope to create robots that not only see and understand, but also think and do. Read the Full Whitepaper HERE x.com/ASI_Alliance/s… @Fetch_ai @SingularityNET @oceanprotocol @CUDOS_

ASI Stack Deep Dive Learn more about the initiatives to be revealed in the upcoming Artificial Superintelligence Alliance Roadmap

The Decentralized AI landscape is about to change forever. As a Founding Member of the @ASI_Alliance, we readily accept our leadership role in advancing AGI and, ultimately, ASI for the benefit of humanity and other sentient beings.

Imagine an AI that learns like a child: through curiosity, experience, and discovery. Introducing AIRIS, a groundbreaking AI system that writes its own expert rules by interacting with its environment—no step-by-step instructions required. Discover how ↓

The ASI AI DePIN stack is technology infrastructure that can be used to devise a number of decentralized AI solutions. ASI:Create uses ASI DePIN to deliver an ASI Core node that contains services for cross-chain agent registration, AI, knowledge, data, compute, hosting and payments. Self-governance authentications will be designed from the ground up to ensure capabilities for privacy preserving avatars for developers. This will enable the AI community to rapidly form their spaces with markets that are crowdfunded with commoditized crypto assets via liquid staking and more. @Fetch_ai @SingularityNET @oceanprotocol @CUDOS_ @HMsheikh4 @bengoertzel