Energy Problem: Powering Superintelligence Without Destroying the Climate
Superintelligence is an operational definition of a future system capable of recursive selfimprovement at humansurpassing levels across diverse domains, necessitating a...
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Computational intelligence, superintelligence, alignment, and agentic systems.
Explore long-form technical articles across machine learning, autonomous agents, AI safety, cognitive architecture, quantum systems, distributed infrastructure, education, and planetary-scale technology.
Causal Embeddings for Value-Stable Superintelligence
Causal embeddings represent a key departure from traditional statistical pattern recognition by explicitly modeling the underlying causeeffect relationships builtin in...
Metacognition: Thinking About Thinking in AI
Metacognition in artificial intelligence denotes the capacity of computational systems to monitor, evaluate, and adjust their own internal reasoning processes, a...
Cooperative Inverse Reinforcement Learning Path to Safe Superintelligence
The challenge of aligning artificial intelligence systems with human intentions constitutes a core engineering hurdle as these systems approach and eventually surpass...
Technical Approaches to Value Loading
Value alignment involves ensuring artificial superintelligence pursues objectives that faithfully reflect complex human values, including moral, cultural, and...
Successor Species Question: Are We Creating Our Replacements?
The progression of computational hardware has followed a distinct and accelerating path defined by the exponential growth of transistor density and the parallelization...
Human-In-The-Loop at Superintelligent Speed: Practical or Impossible?
Humanintheloop (HITL) systems traditionally required explicit verification or approval of artificial intelligence actions prior to execution, creating a synchronization...
AdS/CFT-Inspired AI
The AdS/CFT correspondence posits a key duality between a gravitational theory operating within a higherdimensional antide Sitter space and a conformal field theory...
Contextual Memory: Immersive Spaced Repetition 3.0
Hermann Ebbinghaus established the foundation of memory science in 1885 through his experiments on the forgetting curve, which demonstrated the exponential decline of...
Global Coordination on Superintelligence: Preventing Arms Races
Superintelligence denotes future systems that will reliably outperform humans across economically valuable tasks by connecting with cognitive abilities such as pattern...
Whole Brain Emulation Fidelity and Philosophical Identity
Mind uploading involves creating a functional digital replica of a human brain’s structure and activity through precise computational emulation requiring detailed...
Genealogy Detective
Genealogy detective systems represent a sophisticated class of software designed to automate the comprehensive construction of family histories by ingesting and...
Debate Mastery Institute: Persuasion as Cognitive Craft
Persuasion and debate training originate in classical rhetoric, with Aristotle and Cicero establishing the foundational triad of ethos, pathos, and logos, which served...
Topos-Theoretic Audit Trails for Superintelligence
Category theory originated in the 1940s through the work of Eilenberg and Mac Lane to unify mathematical concepts across algebra and topology, providing a highlevel...
Coherent Extrapolated Volition: What Humanity Would Want
Modeling human preferences under conditions of enhanced knowledge and extended reasoning allows inference of what humanity would collectively desire if it were more...
High Bandwidth Memory: Feeding Data to Hungry Accelerators
High Bandwidth Memory (HBM) addresses the growing disparity between compute throughput and memory bandwidth in accelerators such as GPUs and AI chips where performance...
Maintaining Social Fabric in Post-Labor Societies
Social cohesion relies on shared trust, common narratives, and mutually recognized norms to function as the bedrock of stable societies capable of sustaining complex...
Safe AI via Adversarial Preference Elicitation
Reinforcement learning from human feedback serves as the primary mechanism for aligning large language models with human intent, yet this methodology relies heavily on...