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We are currently witnessing a genuine economic shift rather than just predicting a speculative future, according to CEO Dr. @bengoertzel. He points out that artificial intelligence is evolving beyond elementary assignments to automate sophisticated cognitive endeavors that demand creative synthesis, analysis, and pattern recognition. Although high-level AI might eventually minimize the necessity for routine labor, Dr. @bengoertzel warns that this transition carries substantial risks if managed poorly. Without the implementation of decentralized systems, comprehensive social protections, and broad access to AI tools, there is a real danger of exacerbating economic inequality and displacing mid-career professionals.

We are eager to welcome you to the upcoming meeting of the BGI Nexus Working Groups, taking place at 5:00 PM UTC on Wednesday, January 28, 2026.

Following our earlier discussions regarding the strategic priorities for BGI Nexus in 2026, this gathering aims to turn those community insights into action. We are offering a platform for members to step forward and spearhead topics they are passionate about. Those interested in serving as leads will have the chance to give a short overview of the initiatives they plan to guide. Subsequently, all attendees will be free to team up and organize themselves based on the projects that best match their interests.

We would like to invite researchers and developers dedicated to AGI to attend our upcoming MeTTa Coders session this Friday, January 23, at 4:30 PM UTC.

This meeting unites the Hyperon community to investigate the latest advancements in the MeTTa language for cognitive computations, facilitate collaboration, and further practical use cases.

You can add our biweekly sessions to your calendar via:

To develop human-level AGI systems, we need a cognitive modeling approach that prioritizes functional similarities with human cognition over merely structural ones. We have realized this through our Economic Attention Networks (ECAN), which serve as the attention-allocation and resource-regulation subsystem of the Hyperon architecture, originally implemented and tested within the OpenCog AGI framework.

Inside ECAN, working memory—as implemented in the PRIMUS cognitive architecture—is stored in the form of Atoms that maintain sufficiently high Short-Term Importance (STI) values. A novel enhancement to ECAN within Hyperon reframes this attention allocation as incompressible fluid dynamics with optimal control semantics. Instead of treating attention as something that diffuses randomly through the knowledge graph, this approach models it as a conserved fluid, where the flow is optimally steered toward goal-relevant regions.

In the latest episode of The Ten Reckonings of AGI series, our CEO, Dr. @bengoertzel, sits down with technologist Jaron Lanier. Together, they explore the practical realities of building and governing advanced systems and debate how far our empathy should extend to AI and future AGI.

How can a human-level AGI system self-improve while staying aligned with human values and maintaining its core objectives?

Creating a capable AGI represents both a significant milestone in long-term AI research and a starting point for future development. A key aspect of this challenge is constructing a system that can improve itself without compromising its original purpose or values.

Although this idea may initially appear ambitious or speculative, it is a practical necessity. To be truly valuable, an AGI will need the ability to upgrade its capabilities, refine its algorithms, and scale its cognitive processing.

Unlike traditional software systems, which depend on external developers for updates, a genuine AGI system must execute self-modifications that protect its intended properties. Initially, such modifications should strictly maintain alignment with specified objectives, with any deviations occurring only after careful oversight and reflective evaluation.

This necessitates three interconnected capabilities:

1. Goal preservation: Mechanisms to represent and uphold core objectives so they remain intact throughout system upgrades.

2. Compositional safety: Shared principles that ensure modules continue to interact predictably and reliably as they evolve.

3. Controlled modification: A procedure for self-improvement that includes validation, testing, and rollback measures.

Joining the @ASI_Alliance decentralized AGI network offers you the opportunity to "participate in shaping the mind of the first superhuman thinking machine that humanity builds." $FET

Deploy on-demand or reserved NVIDIA GPUs—featuring L40S, H200, B200s, and more—on Bare Metal or VMs with @S_Compute.

Take advantage of preconfigured templates like vLLM, Triton, and JupyterLab to help accelerate your AI workflows.

Get started now: https://www.singularitycompute.com/

What do biological, computational, and human organizational systems all have in common?

Why is simplicity of form correlated with functional adaptability?

What constitutes the most adaptable system possible?

@MiTiBennett explores the answers to these questions through the lens of his proposed Stack Theory. This framework treats systems as infinite stacks of abstraction layers, where each layer represents the behavior of the layer beneath it.

While LLMs and other contemporary neural network architectures generally remain insufficient for achieving human-level AGI due to a lack of internal abstraction—which limits their ability to make creative leaps beyond their training data—they nevertheless demonstrate impressive practical capabilities across a wide range of applications.

Although these capabilities can be replicated via non-neural architectures, there is a strong pragmatic motivation to retain what currently works and integrate it with other components that operate differently. This reality militates toward hybrid AGI architectures, such as neural-symbolic and neural-symbolic-evolutionary systems.

Broadly speaking, current neural networks excel at pattern recognition, handling ambiguity, and learning from examples. Conversely, symbolic systems currently shine at explicit reasoning, structured manipulation, and explanatory transparency. Running these systems in isolation means constantly shuttling data back and forth, losing context in translation, and missing opportunities for mutual enhancement.

Hyperon eliminates these barriers by establishing neural and symbolic components as first-class citizens within the same computational space. Features, rules, proofs, options, and activations all become Atoms in shared memory, operating under a unified scheduling model.

This signifies that reasoning can directly guide where neural networks focus their attention, while neural networks can propose structured hypotheses back to the reasoning system—all without serialization boundaries or synchronization bottlenecks.

Join us on Tuesday, January 6, 2026, at 4:30 PM UTC for an engaging session of the BGI Nexus working group, where we will delve into:

What fundamental overarching themes are essential for defining and advancing Beneficial AGI?

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Will Artificial General Intelligence Result in Human Extinction or Unprecedented Prosperity?

This intriguing question is examined in a 2-hour discussion featuring Dr. @bengoertzel and Dr. Roman Yampolskiy @romanyam.

Tune in here: https://www.youtube.com/watch?v=NNsJMneeK80
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Seattle's @MindChildren_AI is currently working on Codey, a humanoid robot designed for educational and caregiving environments. This innovative solution emphasizes the importance of privacy protection, emotional well-being, and affordability in its implementation.

"Codey utilizes advanced AI elements from the SingularityNET ecosystem to enhance its reasoning and decision-making capabilities."

Discover more:

The idea of world modeling has gained significant traction recently, identified as a limitation in existing LLM systems and recognized as a vital element in achieving human-level and superhuman AGI.

Prominent figures in the field, including Yann LeCun, emphasize that AI must develop its own representation of reality. This can be achieved by processing information through its “senses,” much like how it learns the behavior of physical objects by observing videos.

However, it is important to note that world modeling is not an enigmatic concept that requires unexplored mechanisms. Within a well-rounded AGI framework that includes the essential features of a human-like artificial mind, the ability to model the world naturally arises from the dynamic interplay of these elements.

We present a world modeling strategy within Hyperon that is focused on AGI, aligned with the PRIMUS cognitive architecture, and tailored for robotics, as well as environments resembling Minecraft and other simulated or gaming settings.

Read here: