The c-logo-s initiative writes a chronologically organized history of the programming language Logo and the broader theoretical and institutional currents that shaped it, tracing origins from 1930s formal logic and 1940s cybernetics through the language's 1960s inception at BBN to its influence on modern graphical and agent-based environments. The account frames Logo not as an isolated educational tool but as a convergence point for ideas from Turing, Church, Piaget, the Macy Conferences, and early artificial intelligence research, detailing how collaborative efforts by Wallace Feurzeig, Cynthia Solomon, Daniel Bobrow, and Seymour Papert transformed interactive computing into a medium for exploring mathematics and cognitive development. The text further outlines Logo's commercial and academic distribution across the microcomputer era, its integration with physical robotics through LEGO and the MIT Media Lab, and its conceptual legacy in contemporary systems like StarLogo, NetLogo, Scratch, and Snap!.
Telukunta et al. argue that enterprise governance of agentic AI fails because it applies a single deterministic toolkit across four distinct scales of stochastic agency. They propose the CASE framework, which maps specific disciplines to each scale: control theory for individual agents, complex adaptive systems for agent collectives, supervisory cybernetics for human-agent teams, and engineering operations for fleet-level automation. The authors derive cross-layer coupling conditions and introduce a "zero-touch deployment paradox," where improved deployment automation mechanically outpaces static oversight capacity. Validating the model through three studies of public data, they identify an "Emergence Gap," noting that while 82% of production failures involve multi-layer trajectories, no major tooling provides full coverage for the complex adaptive layer and no public enterprise deployment evidences any interaction-level governance.
- The framework formalizes oversight requirements as testable engineering inequalities, specifically converting the Law of Requisite Variety into a verifiable design condition for compliance with EU AI Act Article 14.
- A non-compensatory maturity index is used, where the weakest layer carries majority weight, effectively preventing high scores in operational layers from masking critical gaps in interaction-level governance.
- Autonomy is redefined as a controlled variable modulated by a "decision quality" error budget, allowing for the systematic expansion or contraction of agent scope based on live performance data.
- The framework formalizes oversight requirements as testable engineering inequalities, specifically converting the Law of Requisite Variety into a verifiable design condition for compliance with EU AI Act Article 14.
- A non-compensatory maturity index is used, where the weakest layer carries majority weight, effectively preventing high scores in operational layers from masking critical gaps in interaction-level governance.
- Autonomy is redefined as a controlled variable modulated by a "decision quality" error budget, allowing for the systematic expansion or contraction of agent scope based on live performance data.
The documents cover Gilbert Simondon’s genetic "mecanology," a philosophy of technical evolution independent of economic demand. It compares Simondon’s concepts, such as "concretization," with Genrich Altshuller’s TRIZ and modern log-periodic modeling. These sources explore how technical lineages progress through rhythmic phases of adaptation and saturation, offering theoretical insights into the nature of technical objects and their evolutionary laws.
Notes and reflections from the 2025 Protocol Symposium, covering topics like protocol entrepreneurship, digital institutions, protocol art, agentic systems, and practical applications of protocol thinking.
Italo Calvino's 'literature machine' is a prescient vision of the perils and promise of artificial intelligence. This article explores Calvino's thoughts on the future of literature in the age of computers, his embrace of fantasy as a way to represent the modern world, and why his work remains relevant today.
The Vivarium Project was an Apple-sponsored research program led by Alan Kay and Ann Marion, focused on modeling animal minds to create new programming languages and environments, especially for children. It explored agent-based systems and end-user programming.
This webpage discusses the electronic publication of W. Ross Ashby's seminal work 'Introduction to Cybernetics', originally published in 1956. Principia Cybernetica has made this classic text available online, thanks to permissions from the Ashby estate, allowing for wide access. The book is praised for its clear, simple, yet profound insights into cybernetics and systems theory, introducing fundamental concepts like the homeostat, the law of requisite variety, and the principle of self-organization. It's formatted as a PDF for easy reading and printing.
Brian Douglas, a control systems engineer based in Seattle, has created various resources over the years and invites visitors to explore them.
- Linear (PID, lead-lag, full-state feedback)
Nonlinear (gain scheduling, backstepping, sliding mode)
Optimal & Predictive Control
LQR, MPC, Hamilton-Jacobi-Bellman
- Intelligent & Multi-Agent Control
Reinforcement learning, genetic algorithms, swarm control
- Robust & Adaptive Control
- H-infinity, mu-synthesis, active disturbance rejection, model reference adaptive control
- Planning & Estimation
- Path planning (RRT, A*), Kalman filters, sensor fusion
- Modeling, Simulation & System Analysis
- State-space, transfer functions, stability analysis, Nyquist, Lyapunov
- Linear (PID, lead-lag, full-state feedback)
Nonlinear (gain scheduling, backstepping, sliding mode)
Optimal & Predictive Control
LQR, MPC, Hamilton-Jacobi-Bellman
- Intelligent & Multi-Agent Control
Reinforcement learning, genetic algorithms, swarm control
- Robust & Adaptive Control
- H-infinity, mu-synthesis, active disturbance rejection, model reference adaptive control
- Planning & Estimation
- Path planning (RRT, A*), Kalman filters, sensor fusion
- Modeling, Simulation & System Analysis
- State-space, transfer functions, stability analysis, Nyquist, Lyapunov
This article explores the application of reinforcement learning (RL) to Partial Differential Equations (PDEs), highlighting the complexity and challenges involved in controlling systems described by PDEs compared to Ordinary Differential Equations (ODEs). It discusses various approaches, including genetic programming and neural network-based methods, and presents experimental results on controlling PDE systems like the diffusion equation and Kuramoto–Sivashinsky equation. The author emphasizes the potential of machine learning to improve understanding and control of PDE systems, which have wide-ranging applications in fields like fluid dynamics, thermodynamics, and engineering.
Using Digital Twins to optimize data center operations and eliminate wasted IT infrastructure can save significant costs and improve sustainability.
