In the age of Transformers and neural nets that write poetry, it’s tempting to assume deep learning dominates every corner of AI. But in quantitative investing, the roots tell a different story. A recent paper—QuantBench: Benchmarking AI Methods for Quantitative Investment1—delivers a grounded reminder: tree-based models still outperform deep learning (DL) methods across key financial prediction tasks. ...
As artificial intelligence surges toward superhuman capabilities, one truth becomes unavoidable: the strength of our oversight must grow just as fast as the intelligence of the systems we deploy. Simply hoping that “better AI will supervise even better AI” is not a strategy — it’s wishful thinking. Recent research from MIT and collaborators proposes a bold new way to think about this challenge: Nested Scalable Oversight (NSO) — a method to recursively layer weaker systems to oversee stronger ones1. One of the key contributors, Max Tegmark, is a physicist and cosmologist at MIT renowned for his work on AI safety, the mathematical structure of reality, and existential risk analysis. Tegmark is also the founder of the Future of Life Institute, an organization dedicated to mitigating risks from transformative technologies. ...
Training AI agents to navigate complex environments has always faced a fundamental bottleneck: the overwhelming number of possible actions. Traditional reinforcement learning (RL) techniques often suffer from inefficient exploration, especially in sparse-reward or high-dimensional settings. Recent research offers a promising breakthrough. By leveraging Vision-Language Models (VLMs) and structured generation pipelines, agents can now automatically discover affordances—context-specific action possibilities—without exhaustive trial-and-error. This new paradigm enables AI to focus only on relevant actions, dramatically improving sample efficiency and learning speed. ...
Logos, Metron, and Kratos: Forging the Future of Conversational Agents Conversational agents are evolving beyond their traditional roles as scripted dialogue handlers. They are poised to become dynamic participants in human workflows, capable not only of responding but of reasoning, monitoring, and exercising control. This transformation demands a profound rethinking of the design principles behind AI agents. In this Cognaptus Insights article, we explore a new conceptual architecture for next-generation Conversational Agents inspired by ancient Greek notions of rationality, measurement, and governance. Building on recent academic advances, we propose that agents must master three fundamental dimensions: Logos (Reasoning), Metron (Monitoring), and Kratos (Control). These pillars, grounded in both cognitive science and agent-based modeling traditions, provide a robust foundation for agents capable of integrating deeply with human activities. ...
Artificial Intelligence (AI) has transitioned from a promising concept to a critical driver of business scalability, particularly within complex industries like insurance. Large Language Models (LLMs) now automate knowledge-intensive processes, transforming workflows previously constrained by manual capacity. However, effective AI-driven automation involves more than technical deployment—it demands nuanced strategic adjustments, comprehensive understanding of workflow dynamics, and meticulous validation. In this detailed case study, Cognaptus Insights examines how If P&C Insurance, a leading insurer operating across the Nordic and Baltic regions, leveraged AI-driven Business Process Automation. The study employs Object-Centric Process Mining (OCPM) as an analytical lens, providing a robust framework for evaluating impacts, uncovering subtle workflow interactions, and formulating evidence-based best practices.1 ...
Humans famously “never forget” like elephants—or at least that’s how the saying goes. Yet, traditional conversational AI still struggles to efficiently manage very long conversations. Even with extended context windows up to 2 million tokens, current AI models face challenges in effectively understanding and recalling long-term context. Enter a new AI memory architecture inspired by the human hippocampus: one that promises to transform conversational agents from forgetful assistants into attentive conversationalists capable of months-long discussions without missing a beat. ...
Generative AI is often praised for its creativity—composing symphonies, painting surreal scenes, or offering quirky new business ideas. But in some contexts, especially research and data processing, consistency and accuracy are far more valuable than imagination. A recent exploratory study by Utrecht University demonstrates exactly where Large Language Models (LLMs) like Claude 3 Opus shine—not as muses, but as meticulous clerks. When AI Becomes the Analyst The research project explores three different use cases in which generative AI was employed to perform highly structured research data tasks: ...
“You expect AI to be dumber than humans. But when it’s smarter and still fails, that’s when it hurts.” Earlier this month, Cursor AI’s chatbot “Sam” fabricated a nonexistent refund policy, confidently explaining to users why it was entitled to keep their subscription money—even when those users were eligible for a refund1. The backlash was immediate. Users lost trust. Some cancelled their subscriptions entirely. ...
Introduction: The Retail Map Is Not the Territory In fast-growing cities like Nairobi, Jakarta, or Lagos, deciding where to plant the next store is less about gut feeling and more about navigating an entangled network of demand, accessibility, cost, and government regulations. At Cognaptus, we developed a multi-layered AI-driven framework that not only mimics real-world logistics but also learns and forecasts future retail viability. This article explores how we combined predictive analytics, geospatial clustering, graph theory, and multi-objective optimization to determine where new retail nodes should thrive — balancing today’s needs with tomorrow’s complexities. ...
When large language models (LLMs) learned to think step-by-step, the world took notice. Chain-of-Thought (CoT) reasoning breathed new life into multi-step arithmetic, logic, and even moral decision-making. But as multimodal AI evolved, researchers tried to bring this paradigm into the visual world — by editing images step-by-step instead of all at once. And it failed. In the recent benchmark study Complex-Edit: CoT-Like Instruction Generation for Complexity-Controllable Image Editing Benchmark1, the authors show that CoT-style image editing — what they call sequential editing — not only fails to improve results, but often worsens them. Compared to applying a single, complex instruction all at once, breaking it into sub-instructions causes notable drops in instruction-following, identity preservation, and perceptual quality. ...