The AI evaluation ecosystem is in crisis. Frontier models now exceed 90% accuracy on MMLU, 95% on HumanEval, and 93% on HellaSwag — scores that were considered unattainable three years ago. This saturation is not evidence of intelligence; it is evidence that our instruments have failed. We argue that three convergent forces have rendered current AI leaderboards meaningless: (1) benchmark satura...
The classic "build vs buy" dilemma in enterprise software has been resolved for most AI deployments in 2026 — not by a clear winner, but by a third option that renders the original question obsolete. As Gartner projects worldwide AI spending at $2.5 trillion in 2026, enterprises are abandoning bespoke AI moonshots in favour of orchestrated integration across incumbent vendor ecosystems. This ar...
We present a meta-meta-analysis of 217 benchmark evaluation studies published between 2020 and 2026, examining not the benchmarks themselves but the systematic reviews that assess them. Our coverage matrix reveals a profound structural bias: 78.3% of surveyed studies evaluate text-based capabilities, while causal reasoning (4.1%), embodied intelligence (1.8%), and social cognition (0.9%) remain...
Fifteen articles in, we promised a systematic map of gaps in anticipatory intelligence. Now the field has had a year to respond — or not. Foundation models claim temporal reasoning (GPT-5.4, Gemini via Groundsource). The EU AI Act's high-risk provisions hit enforcement. Distribution shift monitoring went from research curiosity to SaaS checkbox. This retrospective measures our predictions again...
Across Chapters 1 through 14 of this series, we built a careful taxonomy of data mining methods — classification trees, clustering algorithms, regression models, association rules, and dimensionality reduction techniques. Each method occupied a well-defined place. Each had known strengths, assumptions, and failure modes. That taxonomy served the field faithfully for over two decades.
ALsobeh and Alkurdi introduce EcoAI-Resilience, a multi-objective optimization framework that simultaneously targets three goals: maximizing sustainability impact from AI deployment, enhancing economic resilience, and minimizing environmental costs. The framework is trained and validated on data from 53 countries across 14 sectors over the period 2015–2024. The authors report extraordinarily hi...
Six gaps. $537 billion in annual friction. This synthesis constructs a priority matrix for the technical deficiencies identified across the Anticipatory Intelligence series. Using a three-dimensional scoring framework — economic impact, feasibility, and dependency structure — we rank which problems deserve immediate research investment and which will wait decades for breakthroughs that may neve...
Odesa National Polytechnic University, Department of Economic Cybernetics · PhD Candidate, ML in Pharma Economics
Natural language interaction is central to human-robot collaboration. Humanoid robots operating in indoor environments must process continuous speech, detect wake words at low latency, perform automatic speech recognition (ASR) on-device to avoid cloud latency and privacy concerns, parse intent from variable linguistic input, and respond with synthesised speech — all within power and computatio...
Safe interaction with unstructured environments and direct physical contact with humans requires that humanoid robots move beyond position-stiff control toward compliant, force-aware systems. This article specifies the force control subsystem for the Open Humanoid platform, addressing impedance and admittance control architectures, distributed force-torque sensing at joints and end-effectors, c...