Adnan Mahmud

The 2025 UK Strategic Defence Review commits £4 billion to autonomy this Parliament and mandates 10% of the defence budget for artificial intelligence and autonomous systems from April 2025. Yet existing certification frameworks cannot adequately address machine learning systems. This analysis examines how this structural mismatch between surging investment and immature assurance frameworks creates significant commercial opportunities for specialists in probabilistic reasoning, epistemic and aleatoric uncertainty representation, and interpretable artificial intelligence. Drawing on policy documents from the UK Ministry of Defence, NATO, and the European Union, this blog maps specific funding programmes, capability gaps, and market entry points across the defence innovation ecosystem.

Autonomous systems present tort law with a structural epistemological barrier: the opacity of machine learning decision-making renders traditional fault and causation analysis intractable, leaving victims facing insurmountable information asymmetries whilst manufacturers struggle to explain algorithmic behaviour even with full system access. This paper proposes that accountability must be engineered into autonomous systems ex ante rather than reconstructed ex post, outlining a Bayesian accountability infrastructure comprising three integrated components: continuous evidential logging via sufficient statistics, causal graphical models enabling computable counterfactual queries, and uncertainty-aware behavioural envelopes that distinguish statistically anticipated failures from systematic miscalibration. The framework transforms liability adjudication from adversarial narrative construction to principled probabilistic evaluation, preserving tech-impartiality whilst addressing the evidentiary gaps identified in recent European legislative proposals.

Autonomous systems operating in contested environments require real-time uncertainty quantification under adversarial interference and sensor degradation, yet existing approaches struggle to balance computational tractability with probabilistic rigour. This review aims to sketch whether modular Bayesian frameworks can reconcile this tension by decomposing complex digital twins into linked components that enable efficient computation whilst maintaining principled uncertainty propagation. Through thematic synthesis of foundational works and recent contributions spanning probabilistic graphical models, multi-fidelity modelling, and Bayesian verification methods, the review identifies patterns across four dimensions: modular decomposition strategies, information-passing mechanisms, computational efficiency approaches, and uncertainty quantification methods.

JPMorgan's unprecedented analysis of the U.S. defence-industrial complex exposes a startling paradox: despite $148 billion in annual R&D investment, only 16% of SBIR-funded companies successfully transition to production, with less than 1% achieving Programme of Record status. This comprehensive examination reveals how resource abundance creates its own inefficiencies—the "capital intensity paradox"—while exploring alternative models from technologically advanced allies, partnership-dependent strategies, and emerging nuclear diplomacy frameworks. The findings challenge fundamental assumptions about defence innovation, suggesting that efficiency matters more than scale and that resource-constrained countries may actually achieve superior innovation outcomes.

The future of robotics demands a fundamental rethinking of autonomous systems operating in contested environments where multiple agents interact, compete, and coordinate. Traditional single-agent approaches prove inadequate for scenarios involving GPS-jammed drones, underwater vehicles avoiding obstacles, or ground robots in disaster zones. This post explores how next-generation digital twins must evolve beyond simple digital replicas to comprehensive multi-agent simulation environments that integrate neurosymbolic AI, Bayesian inference, and multi-agent reinforcement learning.

An exploration of one of mathematics' most influential failures: David Sprecher's 1996 attempt to make Kolmogorov's superposition theorem computationally practical. This venture consequently sparked a decade of rigorous investigation, revealing fatal issues with monotonicity and continuity, before ultimately leading to Köppen's recursive solution and the modern breakthroughs in Kolmogorov-Arnold Networks that power (at least has strong potential) today's machine learning applications.

This exploration examines the potential parallels between verification principles central to Logical Positivism and contemporary approaches to ensuring reliability in LLM outputs for scientific discovery. We identify four key verification traps that may constrain LLMs' scientific potential and propose an alternative approach inspired by Epicurus' principle of multiple explanations. The discussion culminates in a research proposal for developing a balanced framework that ensures reliability while preserving LLMs' capacity for creative scientific thinking.

In this post, we present a comprehensive taxonomy of logical reasoning, systematically charting the landscape from fundamental deductive and non-deductive frameworks to specialized logical systems and meta-logical properties. Building on this structured taxonomy, we then explore the implications of this mapping for understanding and evaluating reasoning processes in LLMs. The discussion is anchored in the goal of establishing clearer conceptual boundaries for assessing LLM reasoning performance.

Evaluating hallucinations in LLM outputs is anything but straightforward. Over the past few years, researchers have developed a wide array of metrics—from ROUGE and BLEU to embedding-based and graph-based techniques—each with its own strengths and blind spots. This post walks through a structured taxonomy of these metrics, classifying them by lexical, semantic, factual, logical, and pragmatic dimensions.

But we do not stop there. The latest addition to this taxonomy is a unified Meta-Metric Framework that integrates diverse metrics into a single, adaptive evaluation pipeline. Inspired by ensemble learning, this framework uses dynamic weighting, dimensional aggregation, and task-aware scoring to deliver a more robust, interpretable, and generalizable assessment of hallucination across domains. Whether you're working on summarization, open-domain QA, or factual dialogue systems, this meta-metric approach offers a practical path toward better model accountability and fidelity.