Superintelligence

Causal invariance acts as a foundational constraint in superintelligence self-improvement by ensuring an agent’s causal role remains constant despite internal upgrades, establishing a rigorous mathematical boundary that separates the optimization of internal competence from the alteration of external influence. The principle dictates that the functional relationship between the agent and its environment must stay fixed throughout recursive improvement, meaning that while an a

The bandwidth constraint occurs when data transfer rates between system components fail to match computational processing speeds, creating a key disparity where high-performance processors remain idle while waiting for data to arrive from memory or storage subsystems. This mismatch creates idle time and limits overall performance because the central processing units or tensor cores cannot execute instructions without the necessary operands being fetched from external location

Liquid Neural Networks represent a class of adaptive, time-continuous neural models inspired by the active behavior of biological neurons found in the nematode C. elegans. These models differ fundamentally from discrete, feedforward artificial neural networks by processing information continuously rather than in fixed steps. Biological neurons operate in a regime where electrical potentials fluctuate in real-time, responding to stimuli with varying latencies and durations, a

Standard neural network architectures rely on gradient descent optimization techniques that adjust parameters to minimize a specific loss function, yet this process inherently suffers from a phenomenon known as catastrophic forgetting when applied to sequential data streams. When a model trained on an initial task encounters data from a subsequent task, the gradient updates computed for the new objective alter the weight values in directions that reduce performance on the pre

Red teaming originated within military strategy as a method to simulate adversarial attacks and identify vulnerabilities in plans or operational systems before they encountered real-world opposition. This practice migrated into cybersecurity as a formal discipline where dedicated teams emulate the tactics and techniques of malicious actors to test defensive postures prior to deployment. The artificial intelligence sector adapted this methodology to address safety concerns by

The universe expands toward a state of maximum entropy, known as heat death, where usable energy gradients vanish as the temperature approaches absolute zero and all physical processes will eventually cease without external energy input. Thermodynamic systems naturally evolve toward equilibrium, a state characterized by the uniform distribution of energy across all spatial coordinates, rendering the extraction of work impossible due to the lack of temperature differentials. S

Red teaming in artificial intelligence constitutes a specialized practice where dedicated groups or automated systems actively probe, challenge, and exploit weaknesses within machine learning models and their associated deployment environments to uncover vulnerabilities before malicious actors can exploit them. This discipline draws a direct lineage from cybersecurity red teaming, where offensive security experts simulate real-world threats to test defenses, yet it diverges b

Emotional memory is the capability to encode, store, and retrieve factual details alongside associated affective states such as joy, frustration, or anxiety, creating a holistic record of an event that exceeds mere data logging. Biological systems achieve this complex setup through limbic structures, including the amygdala and hippocampus, which function in concert to bind raw sensory data with emotional valence, thereby ensuring that survival-relevant experiences are retaine

Holographic memory systems store data as interference patterns within a three-dimensional medium, enabling data to be encoded throughout the volume rather than on a surface. This volumetric approach allows multiple data pages to be stored and retrieved simultaneously through angular, wavelength, or phase multiplexing. Data is written by intersecting two coherent laser beams consisting of a signal beam carrying information and a reference beam within a photosensitive storage m

The rise of compositional abstraction in neural architecture search has been driven by the urgent necessity for formal mathematical frameworks that can manage the escalating complexity of deep learning models without succumbing to the fragility of ad hoc design. As neural models have evolved from simple perceptrons into massive, multi-modal systems comprising billions of parameters, the methods for designing them have had to go beyond manual tuning and simplistic heuristic se