Muhammad Rashid

Debates about artificial intelligence usually oscillate between behavioral measures and hardware-based metrics, but neither provides a physically grounded, substrate-neutral unit of machine intelligence. Benchmark scores are narrow and culture-dependent, whereas FLOP counts and parameter numbers describe particular implementations rather than decision capacity itself. I propose Machine Decision-Making Ability (MDMA) as a candidate for a universal law of machine intelligence: in the minimal physical sense, a machine’s intelligence is its capacity to enact autonomous state transitions per unit time. A modern mechanical momentary on–off button that flips itself off→on and on→off as two autonomous transitions (MDMA = 2). Tracing the concept backward, ancient alarm clocks and Zhang Heng’s seismoscope each realize a single autonomous trigger (MDMA = 1)—the tiniest physical unit of machine intelligence—while the abacus, for all its historical significance, never decides on its own and merely amplifies human cognition. Anchoring intelligence in autonomous state transitions connects AI capability to the thermodynamics of computation (Landauer-style energy costs and Bremermann/Lloyd bounds on ultimate rates) while remaining compatible with agent-level notions such as Legg and Hutter’s universal intelligence. Within this framework I examine three layers: _ (i) hardware MDMA_ across mechanical, electronic, and quantum substrates; _ (ii) Software Layer Abstraction_ as an MDMA multiplier via pipelines, caching, and learning (e.g., LLMs whose modest inference-time MDMA hides vast historical training budgets); and _ (iii) a “physics of decisions” program_ linking MDMA to mass–energy constraints and civilizational scaling.

This paper develops a speculative model of gravity based on the idea that the universe is filled with an invisible, unobservable medium, denoted R, and that all masses act as sinks for this medium. Smaller masses swallow R slowly and thus attract matter weakly; larger masses swallow R more intensely and produce stronger attraction. This “R-medium” picture is loosely analogous to a drain in a pool pulling surrounding water toward it. We situate the model within the historical context of aether concepts, Le Sage-type kinetic gravitation, and more recent sink-flow approaches to gravity, then propose a simple continuum formulation in which R obeys a steady-state continuity equation with sink terms proportional to mass. Under spherical symmetry, the resulting flow field leads to an effective inverse-square gravity law and permits bound orbits in a way that addresses a limitation of my earlier “ground is falling to apple” reinterpretation of gravity, which denied stable orbits. The present work is explicitly nonstandard and likely incompatible with relativity and some known constraints on drag and energy dissipation; its purpose is to serve as a focused articulation of a long-held intuition rather than a replacement for established gravitational theory.

This paper introduces a speculative reinterpretation of gravity, termed the “ground is falling to apple” theory. Unlike Newtonian and Einsteinian frameworks that allow for stable orbital motion, this perspective denies the long-term possibility of orbit. Instead, all masses are in continuous mutual motion toward one another, such that orbital stability is ultimately unsustainable. We formalize the idea with a discrete-time time-slot propagation model in which objects re-form each tick of time and an effective gravitational strength depends on a temporal density parameter. The account is intentionally nonstandard and conflicts with established physics; its value here is conceptual and programmatic.

This preprint extends the Machine Decision-Making Ability (MDMA) framework into a physical setting, asking how motion, gravity, and measurement shape the rates at which decisions are observed. We introduce two complementary extensions: (1) Einstein-MDMA Relativity, which relates observed decision rates to relativistic (kinematic) and gravitational time dilation; and (2) Observable–DMA Collapse, which treats observation as an information-resolving decision event, measured in bits per unit time. The goal is not to claim new physics, but to provide operational, physically grounded lenses that connect decision-making capacity to reference frames and measurement. These extensions help separate a system’s local (proper-time) capability from its frame-dependent appearance, and formalize the informational yield of observation as a decision process.

This work extends the core Machine Decision-Making Ability (MDMA) framework into a broader scientific context by proposing a structured set of cross-disciplinary theoretical extensions. While the core preprint formalized MDMA as a measure of autonomous decision capacity grounded in physical state transitions, here we examine how fundamental constraints and affordances from physics, information theory, and complexity shape that capacity. We organize nine extensions into four families (Scaling Laws; Quantum and Relativistic Limits; Mind and Complexity; Post-Silicon Frontiers) and formulate each as an MDMA-aware principle with an associated research question. The goal is not to settle debates, but to offer a coherent roadmap for measurement, limits, and design that can guide future experimental and theoretical work across substrates.

This paper extends the framework of Machine Decision-Making Ability (MDMA) by relating it to Einstein’s mass–energy equivalence equation, E = mc2. MDMA quantifies a system’s rate of autonomous state transitions, while E = mc2 defines the total energy reservoir inherent in matter. We argue that energy is the ultimate substrate for decision-making: each autonomous transition requires a quantized energy expenditure, and the mass of a system therefore represents its absolute decision capacity. This paper establishes a conceptual and mathematical bridge: (i) MDMA measures the realized decision flow, (ii) E = mc2 sets the theoretical ceiling, and (iii) physical limits such as Landauer’s bound and Bremermann’s rate provide the scaling laws between them. This alignment provides a unified framework for situating machine intelligence within physics itself.

Building upon the recently proposed metric of Machine Decision-Making Ability (MDMA), this paper introduces Software Layer Abstraction (SLA) as a multiplier that extends a machine’s apparent intelligence beyond the raw capabilities of its hardware. SLA quantifies how software layers—through reuse, caching, and prediction—amplify decision-making capacity. Tracing the progression from mechanical systems to electrical and quantum technologies, we argue that while hardware MDMA defines a system’s physical baseline, SLA explains why modern systems often appear to exceed hardware limits. Case studies include stored programs, predictive execution, and Large Language Models (LLMs), which distill years of high-MDMA training into portable predictive engines that run on modest devices. We outline five stages of abstraction—Binding, Notation, Escape, Dominion, and Singularity—and highlight the emerging threshold where software begins to design hardware autonomously. SLA is positioned as a complement to MDMA, clarifying the “illusion of spirit” and the risks of runaway abstraction.

This paper proposes a zero-free foundational framework called Existential Mathematics. Numerical entities are partitioned into (i) Existence, representing physically existing quantities (positives); (ii) Anti-Existence, representing directed reversals of existence (negatives); and (iii) Nothingness, corresponding to the classical symbol 0, here excluded from arithmetic. I formalize an Existential Algebra in which addition is partial (annihilating pairs are undefined), multiplication and division are total over E = R\{0}, and present an existential factorial in which 0! is assigned the boundary value 0 to reflect the non-operation of the empty act. A zero-free analysis is outlined by treating 0 as an annihilation boundary: it may be approached from either side but never attained. The program aligns mathematics with physically interpretable states of being (existence and its reversal) while rejecting calculations that treat “nothingness” as a legitimate operand.

This paper proposes Machine Decision-Making Ability (MDMA) as a physically grounded unit for quantifying the autonomous decision capacity of machines. Rather than starting from symbolic computation (e.g., abaci), we locate the conceptual origin of computers in devices capable of autonomous state change. We argue that the reusable, momentary mechanical on–off button embodies the first symmetric decision mechanism (two distinct state transitions), while earlier devices such as ancient water clocks and Zhang Heng’s seismoscope execute a single irreversible decision event. We formalize MDMA as a count of autonomous state transitions per unit time, present a concrete mechanical formulation, and illustrate historical measurements across early machines. The intent is to provide a universal baselining metric that (i) is substrate-neutral, (ii) scales from mechanical to electronic and future quantum systems, and (iii) supports systematic comparison of autonomy, framed as intelligence, without relying on traditional human-centric measures such as IQ scores or the Turing test.