📚 Academic Citation: Grybeniuk, D. & Ivchenko, O. (2026). The Anticipation Gap: Research Transitions Academia Refuses to Make. Anticipatory Intelligence Series. Odesa National Polytechnic University. DOI: Pending Zenodo registration Abstract This analysis identifies critical research transitions that academic foresight literature systematically avoids despite their urgent practical necessity. While academia has built extensive frameworks around scenario…
Category: Anticipatory Intelligence
Anticipatory Intelligence Gap Research by Dmytro Grybeniuk
The Future of Anticipatory Intelligence: Beyond the Hype Cycle
The Future of Anticipatory Intelligence: Beyond the Hype Cycle Authors: Dmytro Grybeniuk & Oleh Ivchenko Abstract After thirteen articles dissecting anticipatory intelligence—its gaps, priorities, and emerging solutions—we arrive at the question that matters: where is this field actually headed? Not where we wish it would go or what the grant proposals promise, but what the…
Emerging Solutions and Research Directions: Beyond the Current Paradigm
Emerging Solutions and Research Directions: Beyond the Current Paradigm Authors: Dmytro Grybeniuk & Oleh Ivchenko Abstract Having identified the critical gaps in anticipatory intelligence and prioritized them by tractability and impact, we now survey the emerging technical approaches that might actually close these gaps. Spoiler: most won’t. The literature is heavy on incremental refinements and…
Synthesis of Gap Analysis Findings: A Priority Matrix for Anticipatory Intelligence
Synthesis of Gap Analysis Findings: A Priority Matrix for Anticipatory Intelligence Authors: Dmytro Grybeniuk & Oleh Ivchenko Abstract After dissecting ten critical gaps in anticipatory intelligence systems, we now face the uncomfortable task of prioritization. Not all problems are created equal—some are merely annoying engineering challenges, while others represent fundamental theoretical barriers that could define…
Gap Analysis: Computational Scalability of Anticipatory Systems
📚 Academic Citation: Grybeniuk, D., & Ivchenko, O. (2026). Gap Analysis: Computational Scalability of Anticipatory Systems. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18700636 Abstract Anticipatory intelligence systems — those capable of modeling causal futures rather than merely extrapolating from historical patterns — demand computational resources that scale non-linearly with the complexity of the…
Gap Analysis: Cross-Domain Transfer of Anticipatory Models
📚 Academic Citation: Grybeniuk, D., & Ivchenko, O. (2026). Gap Analysis: Cross-Domain Transfer of Anticipatory Models. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18682333 Abstract Anticipatory intelligence systems — those designed not merely to detect current states but to model causal futures — are expensive to build. Enormously, stubbornly expensive. The data pipelines, domain…
Gap Analysis: Real-Time Adaptation to Distribution Shift
📚 Academic Citation: Grybeniuk, D., & Ivchenko, O. (2026). Gap Analysis: Real-Time Adaptation to Distribution Shift. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18672412 Abstract Distribution shift — the statistical divergence between the data a model trained on and the data it encounters in production — is the quiet destroyer of AI reliability. Unlike…
Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains
📚 Academic Citation: Dmytro Grybeniuk & Oleh Ivchenko. (2026). Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18662985 Abstract The explainability-accuracy tradeoff represents one of the most economically consequential yet technically intractable gaps in anticipatory AI systems. High-stakes domains—healthcare diagnostics, financial underwriting, legal risk assessment, and autonomous systems—demand…
Anticipatory Intelligence: Gap Analysis — Cold Start Problem in Predictive Modeling
📚 Academic Citation: Grybeniuk, D. & Ivchenko, O.. (2026). Anticipatory Intelligence: Gap Analysis — Cold Start Problem in Predictive Modeling. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18648784 The $300 Million Launch That Never Learned In March 2020, Quibi launched with $1.75 billion in funding, 175 employees, and zero understanding of its audience. The…
Anticipatory Intelligence: Gap Analysis — Exogenous Variable Integration in RNN Architectures
📚 Academic Citation: Grybeniuk, D. & Ivchenko, O. (2026). Anticipatory Intelligence: Gap Analysis — Exogenous Variable Integration in RNN Architectures. Anticipatory Intelligence Series. Odesa National Polytechnic University. DOI: Pending Zenodo registration Abstract Recurrent neural networks (LSTMs, GRUs) dominate time series forecasting but share a critical architectural limitation: external signals—weather forecasts, economic indicators, news sentiment—enter through…