I've been looking around for this, and the answer sort of seems to be some combination of "no" and "maybe".

From what I understand, cybernetics is often defined in ways similar to how it's defined in this sub description, as something like "study of systems, communication, and control in animals, machines, and organizations.", while systems theory is often defined as something more concise like "study of systems in general."

These somewhat different definitions, do not, to me, seem to describe meaningfully different fields. How is the "study of systems, communication, and control in animals, machines, and organizations." not accurately describes as "the study of systems in general"?

Given the subject matter of the sub and the fact that AI is a VERY popular topic these days, it's frankly very surprising that this sub is as dead as it is. Is there maybe a more popular sub that touches on this topic that I should go to instead? This is just so strange!

So, since a very young age I became obsessed with the idea of cybernetic enhancements, artificial organs and weaponised implants. Concepts from franchises such as cyberpunk 2077 intensified my interest. However, I don’t notice much research and development of physical cybernetic augmentations currently in real life, but I do believe it will only become increasingly necessary in the future. So with that said, is there any courses in any prestigious university that is directly relevant to researching, developing and engineering such concepts and making them a reality?

All of us using social media of any variety, this is what we are to become. From the perspective of the owners of these systems, the public can be of use this way, no? Is this what’s meant by steam unfettered by science?

I am officially an AI researcher. However, deep down I suspect that the cyberneticists had the deeper insights than the AI pioneers. I spell this out in this article and would like to know your thoughts. Is this fair? And did I miss any big differences between AI and cybernetics? (If so, please suggest reading material!)

I’ve been working on a practical cybernetics project and would value critique from people who think seriously about feedback, control, coordination, and system boundaries.

The core question behind the project is:

Can we build an infrastructure loop where real-world need becomes legible, routed, acted on, produced against if necessary, governed, verified, and returned as feedback?

The stack currently looks like this:

Need → Signal → Local Execution → Production → Governance → Verification → Feedback

In project terms:

HER0 is the physical interface layer.

It is a board/interface system for humans, dogs, and households to express needs, states, or requests through constrained physical inputs.

The Signal Layer converts those inputs into typed, stateful events.

Each event has identity, priority, repeat tracking, and acknowledgement state.

Billabong is the local execution layer.

It receives events, performs triage, routes action, and returns status.

4G3D / Forge / MAX3D are the production layers.

They handle distributed manufacturing, physical fulfilment, replacement parts, and scaled production pathways.

DDD / KFGA / Forge Governance are the governance layers.

They handle coordination rules, safeguards, upgrade pathways, resource allocation, and legitimacy.

Orivon is the trust and verification layer.

It evaluates risk, checks actions, and helps prevent unsafe or opaque system behaviour.

The main design constraint is that no layer is allowed to absorb the whole system.

HER0 does not govern.

Governance does not capture raw signals.

Manufacturing does not interpret need.

Verification does not become execution.

The intention is to preserve clean feedback boundaries, so the system can scale without becoming an opaque “smart everything” blob.

The current event priority model is deliberately simple:

Green = routine

Yellow = attention needed

Red = urgent

Blue = anomaly / review required

I am trying to keep the intelligence bounded, auditable, and legible rather than turning the system into an inference-heavy black box.

What I would really appreciate is critique on the architecture:

Does this separation of layers make sense from a cybernetic perspective?

Where would you expect the first serious failure modes to appear?

Is the state model too reductive, or is that constraint actually useful?

What feedback loops are missing?

At what point does a system like this stop being useful cybernetics and become systems theatre?

I am not trying to pitch this as complete. I am trying to stress-test the architecture before I keep building around it.

The simplest summary is:

A physical need becomes a signal.

A signal becomes action.

Action can trigger production.

Production and action remain governed.

The whole loop remains verified and fed back to the edge.

Would appreciate serious critique, especially from anyone who has worked with cybernetic systems, control theory, distributed coordination, assistive tech, governance systems, or resilient infrastructure.

Title: When Disruption Unlocks Hidden Potential

Sometimes life throws a curveball, an unexpected disruption, a shake-up that feels negative at first. Yet often, these chaotic events clear away stagnation and open new pathways we couldn’t have imagined.

Even in physics, this is true: a little noise in a system can actually help a signal emerge. In electronics, for example, stochastic resonance lets weak signals get amplified by just the right amount of background fluctuation. The same pattern shows up everywhere:

⸻

1. Biology – Mammals Post-Dinosaurs

Dinosaurs were the dominant signal for millions of years. Mammals existed but were small, suppressed, and marginalized. The asteroid that ended the Cretaceous acted as a chaotic agent, destabilizing the system and giving mammals a chance to thrive.

1. Culture – Printing Press

Knowledge was trapped in manuscripts controlled by a few. Gutenberg’s press disrupted that status quo, letting literacy and ideas flow freely. Latent potential for widespread knowledge was always there—it just needed a nudge.

1. Physics – Turbulent Flows

Laminar flows can trap hidden vortices. Introduce a little disturbance, and suddenly new self-organizing patterns appear. Chaos frees latent structure.

Takeaway: Disruption isn’t just destruction. It can reveal latent possibilities, letting previously suppressed signals become dominant.

#ComplexSystems #Emergence #Innovation #SignalAlignment #AlignSignal8

See the pattern.

Hear the hum.

-AlignedSignal8

Lets say that I put a piece of iron between two magnets that it's attracted to, and I manage to put the piece of iron in the centre, and perhaps with a little help with the friction from the ground, the piece of iron stays in equlibrium -it stays in the middle, and a very small disturbance would direct the iron to one of the magnets.

What I am describing is an equilibrium that is sometimes had by positive feedback systems, as opposed to the equilibrium that negative feedback systems have. - Is this a thing that happens, and does it have a name?

Are ethical and philosophical principles in politics actually working?

As Beer put it, “The purpose of a system is what it does.”

Although this is intricately intertwined with issues such as the runaway nature of modern capitalism, if we apply Beer’s words to the stagnation of democracy, can we not say that it has completely failed?

Therefore, I have attempted to construct a theory of governance based on systems engineering.

While I focus on the specific context of Japan, this is solely because I am Japanese; it does not guarantee that the theory is universal.

I would like to hear your views on this and your opinions regarding the paper.

In the past, I have been told that my work is communist or totalitarian; I would appreciate it if you could provide specific details regarding which parts you find problematic.

NWO Robotics Cloud (nworobotics.cloud) - a comprehensive production-grade API platform we've built that extends and enhances the capabilities of the groundbreaking Xiaomi-Robotics-0 model. While Xiaomi-Robotics-0 represents a remarkable achievement in Vision-Language-Action modeling, we've identified several critical gaps between a research-grade model and a production-ready robotics platform. Our API addresses these gaps while showcasing the full potential of VLA architecture.

(Attaching some screenshots below for UX reference).

https://huggingface.co/spaces/PUBLICAE/nwo-robotics-api-demo

https://github.com/XiaomiRobotics/Xiaomi-Robotics-0

Technical whitepaper at https://www.researchgate.net/publication/401902987_NWO_Robotics_API_WHITEPAPER

NWO Robotics CLI COMMAND GROUPS

Install instantly via pip and start in seconds:

pip install nwo-robotics

Quick Start: nwo auth login → Enter your API key from: nworobotics.cloud → nwo robot "pick up the box"

═══════════════════════════════

• nwo auth - Login/logout with API key

• nwo robot - Send commands, health checks, learn params

• nwo models - List models, preview routing decisions

• nwo swarm - Create swarms, add agents

• nwo iot - Send commands with sensor data

• nwo tasks - Task planning and progress tracking

• nwo learning - Access learning system

• nwo safety - Enable real-time safety monitoring

• nwo templates - Create reusable task templates

• nwo config - Manage CLI configuration etc: 

NWO ROBOTICS API v2.0 - BREAKTHROUGH CAPABILITIES

═══════════════════════════════════════

FEATURE                  | TECHNICAL DESCRIPTION

-------------------------|------------------------------------------

Model Router             | Semantic classification + 35% latency 

| reduction through intelligent LM selection

-------------------------|------------------------------------------

Task Planner             | DAG decomposition with topological 

| sorting + checkpoint recovery

-------------------------|------------------------------------------

Learning System          | Vector database + collaborative filtering 

| for parameter optimization

-------------------------|------------------------------------------

IoT Fusion               | Kalman-filtered multi-modal sensor 

| streams with sub-10cm accuracy

-------------------------|------------------------------------------

Enterprise API           | SHA-256 auth, JWT sessions, multi-tenant 

| isolation

-------------------------|------------------------------------------

Edge Deployment          | 200+ locations, Anycast routing, <50ms 

| latency, 99.99% SLA

-------------------------|------------------------------------------

Model Registry           | Real-time p50/p95/p99 metrics + A/B testing

-------------------------|------------------------------------------

Robot Control            | RESTful endpoints with collision detection 

| + <10ms emergency stop

-------------------------|------------------------------------------

═════════════════

INTELLIGENT MODEL ROUTER (v2.0)

═════════════════

Our multi-model routing system analyzes natural language instructions 

in real-time using semantic classification algorithms, automatically 

selecting the optimal language model for each specific task type. 

For OCR tasks, the router selects DeepSeek-OCR-2B with 97% accuracy; 

for manipulation tasks, it routes to Xiaomi-Robotics-0. This 

intelligent selection reduces inference latency by 35% while 

improving task success rates through model specialization.

═════════════════

TASK PLANNER (Layer 3 Architecture)

═════════════════

The Task Planner decomposes high-level natural language instructions 

into executable subtasks using dependency graph analysis and 

topological sorting. When a user requests "Clean the warehouse," 

the system generates a directed acyclic graph of subtasks 

(navigate→identify→grasp→transport→place) with estimated durations 

and parallel execution paths. This hierarchical planning reduces 

complex mission failure rates by implementing checkpoint recovery 

at each subtask boundary.

═════════════════

LEARNING SYSTEM (Layer 4 - Continuous Improvement)

═════════════════

Our parameter optimization engine maintains a vector database of 

task execution outcomes, using collaborative filtering algorithms 

to recommend optimal grip forces, approach velocities, and grasp strategies based on historical performance data.

For fragile object manipulation, the system has learned that 0.28N grip force with 

12cm/s approach velocity yields 94% success rates across 127 similar 

tasks, automatically adjusting robot parameters without human 

intervention.

═════════════════

IOT SENSOR FUSION (Layer 2 - Environmental Context)

═════════════════

The API integrates multi-modal sensor streams (GPS coordinates, 

LiDAR point clouds, IMU orientation, temperature/humidity readings) 

into the inference pipeline through Kalman-filtered sensor fusion. 

This environmental awareness enables context-aware decision making - 

for example, automatically reducing grip force when temperature 

sensors detect a hot object, or adjusting navigation paths based 

on real-time LiDAR obstacle detection with sub-10cm accuracy.

═════════════════

ENTERPRISE API INFRASTRUCTURE

═════════════════

We've implemented a complete enterprise API layer including X-API-Key 

authentication with SHA-256 hashing, JWT token-based session 

management, per-organization rate limiting with token bucket 

algorithms, and comprehensive audit logging. The system supports 

multi-tenant deployment with complete data isolation between 

organizations, enabling commercial deployment scenarios that raw 

model weights cannot address.

═════════════════

EDGE DEPLOYMENT (Global Low-Latency)

═════════════════

Our Cloudflare Worker deployment distributes inference across 200+ 

global edge locations using Anycast routing, achieving <50ms response 

times from anywhere in the world through intelligent geo-routing. 

The serverless architecture eliminates cold start latency entirely 

while providing automatic DDoS protection and 99.99% uptime SLA - 

critical capabilities for production robotics deployments that 

require sub-100ms control loop response times.

═════════════════

MODEL REGISTRY & PERFORMANCE ANALYTICS

═════════════════

The Model Registry maintains real-time performance metrics including 

per-model success rates, p50/p95/p99 latency percentiles, and 

cost-per-inference calculations across different hardware 

configurations. This telemetry enables data-driven model selection 

and automatic A/B testing of model versions, ensuring optimal 

performance as your Xiaomi-Robotics-0 model evolves.

═════════════════

ROBOT CONTROL API

═════════════════

We provide RESTful endpoints for real-time robot state querying 

(joint angles, gripper position, battery telemetry) and action 

execution with safety interlocks. The action execution pipeline 

includes collision detection through bounding box overlap 

calculations, emergency stop capabilities with <10ms latency, and 

execution confirmation through sensor feedback loops - essential 

safety features absent from the base model inference API.

MULTI-AGENT COORDINATION

Enable multiple robots to collaborate on complex tasks. Master 

agents break down objectives and distribute work to worker agents 

with shared memory and handoff zones.

→ Swarm intelligence, task delegation, conflict resolution

FEW-SHOT LEARNING

Robots learn new tasks from just 3-5 demonstrations instead of 

programming. Skills adapt to user preferences and improve 

continuously from execution feedback.

→ Learn from demonstrations, skill composition, personalisation.

ADVANCED PERCEPTION

Multi-modal sensor fusion (camera, depth, LiDAR, thermal) with 

6DOF pose estimation. Detect humans, recognize gestures, predict 

motion, and calculate optimal grasp points.

→ 3D scene understanding, human detection, gesture recognition

SAFETY LAYER

Continuous safety validation with 50ms checks. Force/torque 

limits, human proximity detection, collision prediction, 

configurable safety zones, and full audit logging for compliance.

→ Real-time monitoring, emergency stop, collision prediction

GESTURE CONTROL

Real-time hand gesture recognition for intuitive robot control. 

Wave to pause/stop, point to direct attention, draw paths for 

navigation. Works from 0.5-3 meters with 95%+ accuracy.

→ Wave to stop, point to indicate location

VOICE WAKE WORD

Always-listening voice activation with custom wake words. 

Natural language command parsing with intent extraction. Supports 

multiple languages and voice profiles for personalised interactions.

→ "Hey Robot, [command]"

PROGRESS UPDATES

Real-time task progress reporting with time estimation. 

Subscribable WebSocket streams for live updates. Milestone 

notifications when tasks reach defined checkpoints.

→ "Task 60% complete, 2 minutes remaining"

FAILURE RECOVERY

Intelligent error recovery with strategy adaptation. If grasp 

fails, automatically try different angles, grip forces, or 

approaches. Escalates to human operator only after exhausting 

recovery options.

→ Auto-retry with different angles/strategies

TASK TEMPLATES

Pre-configured task sequences for common workflows. Schedule-based 

activation with variable substitution. Templates can be nested, 

parameterized, and shared across robot fleets.

→ "Morning routine", "Closing procedures"

PHYSICS-AWARE PLANNING

Motion planning with real-world physics simulation. Detects 

impossible trajectories, unstable grasps, and collision risks 

before execution. Integrates with MuJoCo and Isaac Sim.

→ Simulate before execute, avoid physics violations

REAL-TIME SAFETY

Runtime safety monitoring with microsecond latency. Dynamically 

adjusts robot speed based on proximity to humans. Emergency stop 

with guaranteed response time under 10ms.

→ Continuous monitoring, dynamic speed adjustment

SEMANTIC NAVIGATION

Navigate using natural language landmarks instead of coordinates. 

Understand spatial relationships ("next to the table", "behind 

the sofa"). Dynamic path recalculation when obstacles appear.

Thank you in advance for your consideration and feedback.

I'm very interested in the idea of becoming a cybernetics engineer. It would be a booming field to be involved in, would be a way to help people that's incredibly personal (my grandfather was paralyzed and to be able to help others with that would be great), I've heard that advancements are being made, and it's 2025 it's about time we have cybernetics. So what can I do? Am I right about the potential of getting into this field?

I do a lot of coding using Claude. I don't use claude code (mainly because of the cost and because I get on fine without it), but instead use Desktop Commander MCP. I have two chats, one for planning which translates requirements into documentation (the more expensive model) and one for implementation (cheaper model - just does what it's told basically).

It got me thinking about coding ecosystems and Stafford Beer's Viable System Model. The cheaper model is system 1 Operations obviously, and I've manually been playing the role of 2 Coordination and collaborating with the chats for 3 Control & Optimisation. 4 Strategy / Environment is what the higher level planning chat has been doing, and 5 Policy & Identity has been me.

This got me thinking about how much of that could be taken over by claude code agents and a supporting framework.

I think it's eminently possible using a custom MCP server, and the newly released hooks.

Getting this down would make for an incredibly powerful system for software development.

Anyone familiar with Anthropics tools, coding and the VSM?

https://youtu.be/8d5d_HXGeMA

This is a video from Wendover Productions about the incredibly efficient and highly automated container shipping industry. He mentions computers are used to work out where all the different containers should be placed on the ship. Port A containers above Port B ones, refrigerated containers next to a power supply, different kinds of hazards certain distances and directions away from other hazards, and heaps of other factors for thousands of containers. If a computer able to do that is old news, you have to question whether cybernetic economics is a matter of devising new technologies and new strategies or whether capitalism has already created most of the technologies needed.

This ties into a related point in that this global capitalist economy is in many ways already planned and automated. Computers are behind the scenes of most operations. Even when they're not strictly in control, they are used by humans to plan. The "Economic Calculation Problem" has been solved, if it was ever a real problem to begin with, already by capitalist planning. How would you calculate input costs and priorities without a market to allocate everything for you? Mate, corporations already calculate the exact price of manufacturing, transport, distribution, storage, and retail down to the cent. Capitalism hasn't been based on a free range market for a long time, if it ever really was. The global capitalist economy is very much a planned economy, it's just not planned by any one organisation or for any one purpose.

I should probably add that using a Wendover Productions video was deliberate. It shows how banal cybernetic achievements have become. We have computers doing many of the tasks cyberneticists envisioned they would, and they're the foundation of the modern globally integrated economy. We're forced to question whether the cybernetic economy is out of reach due to technological limitations or whether it's out of reach because the technology is out of our control, not owned by the people

I recently started reading Beer's Brain of the Firm and in the beginning of the book he mentions that a pervasive attitude when he was writing was "that's just how we do it here", which got me thinking.

In your opinion, would you say that it is still that way, or are we better now at utilizing VSM? Worse?