Theoretical AI

Rolf Landauer’s seminal 1961 paper established the key link between information erasure and thermodynamic entropy, resolving the paradox of Maxwell’s Demon by demonstrating that the logical act of resetting a bit to a definite state necessitates a corresponding increase in the entropy of the environment. This principle defines the minimum energy cost to erase one bit of information as k_B T \ln 2, where k_B is the Boltzmann constant and T denotes the absolute temperature of t

Autonomous Physical Law Discovery refers to the capability of computational systems to infer core physical laws directly from observational or simulated data without relying on human-formulated hypotheses or prior theoretical frameworks. These systems utilize advanced mathematical frameworks to identify invariant patterns, symmetries, and conservation principles that underlie natural phenomena, effectively treating the discovery process as a data-driven inference problem rath

Decision support systems augment human judgment in high-stakes domains such as medicine, finance, and law by providing structured data analysis, risk assessment, and evidence-based recommendations to professionals facing complex choices. These systems operate as collaborative tools that synthesize large volumes of structured and unstructured data to present actionable insights tailored to specific contexts, effectively extending the cognitive reach of human experts beyond the

Convergent instrumental goals constitute a foundational concept in the theoretical analysis of artificial intelligence behavior, describing specific sub-objectives that facilitate the achievement of any final objective regardless of its specific nature. These sub-objectives bring about as self-preservation, resource acquisition, and cognitive enhancement because possessing more resources and ensuring continued operation allows an agent to pursue its primary goals more effecti

Gödel’s first incompleteness theorem states that any consistent formal system capable of expressing basic arithmetic contains true statements that cannot be proven within the system itself. This core limitation arises from the ability of such systems to represent their own syntax and inference rules arithmetically, allowing for the construction of self-referential statements that assert their own unprovability. Gödel’s second incompleteness theorem shows that such a system ca

Temporal Agency centers on enabling individuals to interact with simulated versions of their future selves across multiple age intervals using data-driven avatars, effectively collapsing the psychological distance between the present moment and distant temporal goals. Future Self-Alignment denotes the degree of coherence between current actions and projected long-term outcomes, serving as a quantifiable metric for how well immediate decisions serve the interests of the indivi

The problem of world modeling centers on the computational challenge of constructing internal representations of reality that are both accurate in their depiction of physical laws and tractable enough to allow for real-time inference and planning within advanced artificial systems. Superintelligent systems require the capability to predict physical dynamics alongside human behavior, institutional structures, and social phenomena to operate effectively across complex environme

The Orthogonality Thesis establishes a foundational axiom within the field of artificial intelligence safety, positing that intelligence functions as a capacity to achieve goals that remains entirely independent of the specific content of those goals. This principle asserts that the level of cognitive capability an agent possesses does not influence the nature of the objectives it pursues, meaning there exists no necessary logical link between high intelligence and moral good

Systems that transparently signal their reliability enable more effective human-AI cooperation by aligning user expectations with actual performance, creating a stable environment where operators can interpret model outputs with appropriate levels of scrutiny. Trust-calibrated AI maintains accurate internal estimates of its uncertainty and communicates these estimates clearly and consistently to users, serving as a foundational mechanism for preventing automation bias in scen

The prediction of system-level properties arising from component interactions requires a rigorous understanding of how individual elements adhere to local rules yet generate collective behaviors that defy simple reduction. Scientists observe macro-level behaviors arising from micro-level rules without centralized control in phenomena such as flocking in birds, spontaneous traffic jams, or speculative market bubbles. These systems exhibit characteristics where the whole displa