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Artificial intelligence has made remarkable strides in generating coherent and contextually aware responses through large language models (LLMs). However, a persistent challenge remains: maintaining a consistent persona and contextual continuity over long interactions. Traditional LLMs, even when fine-tuned for specific tasks, often struggle to recall past conversations or maintain nuanced behaviors that define a unique [...]

Artificial intelligence has reached unprecedented heights, yet its transformative power is often offset by a persistent lack of transparency. Decisions made by AI systems, especially those leveraging deep learning, can seem opaque even to their creators. The call for explainability is not just philosophical—it is a regulatory, ethical, and operational demand. At the heart of [...]

Retrieval-Augmented Generation (RAG) has rapidly become a cornerstone in the field of modern natural language processing. By combining large language models (LLMs) with external information retrieval systems, RAG enables dynamic, context-sensitive responses to user queries. This architecture addresses one of the central limitations of pure LLMs: their tendency to "hallucinate" information or produce convincing but [...]

Over the past decade, the robotics industry has undergone a profound evolution, propelled by advances in artificial intelligence, knowledge representation, and memory architectures. The integration of knowledge graphs and ontological memory into robotics has unlocked new possibilities, allowing machines to reason, adapt, and interact in increasingly sophisticated ways. This article profiles 25 global robotics startups [...]

One of the enduring challenges in robotics is enabling intelligent agents to recall, persist, and reason about experiences over time. While state-of-the-art perception and control architectures can endow a robot with impressive capabilities, they often lack robust mechanisms for long-term memory. This gap becomes especially apparent in real-world mobile robots, which must not only perceive [...]

In the rapidly evolving landscape of artificial intelligence and information retrieval, two prominent paradigms have emerged for storing, organizing, and retrieving knowledge: ontology-based memory systems and vector stores. While both approaches aim to empower machines with the ability to recall information, reason, and facilitate decision-making, they embody fundamentally distinct philosophies and technical architectures. A nuanced [...]

Designing digital experiences that feel intuitive and responsive demands more than just beautiful interfaces; it requires systems capable of understanding, anticipating, and adapting to users’ evolving needs. As advances in artificial intelligence and context-aware computing change the landscape, a new class of user experience (UX) frameworks has emerged—those that leverage persistent context to enable anticipatory [...]

Graphs are fundamental data structures for representing complex relationships. As data scales, the size and complexity of graphs can explode, presenting both computational and cognitive challenges. In the context of semantic technologies and knowledge representation, techniques for compressing and managing these graphs become crucial. Among the most effective methods are n-ary reification and role chaining—two [...]

In the rapidly evolving landscape of knowledge engineering, ontologies have become central to structuring, sharing, and reasoning over complex domains. Yet, for many subject matter experts—biologists cataloging new species, medical professionals modeling disease pathways, or legal scholars formalizing statutes—the barrier of learning OWL (Web Ontology Language) syntax and logic can be daunting. This challenge has [...]

As the boundaries of artificial intelligence (AI) continue to expand, so do its infrastructural requirements. One of the most critical bottlenecks in modern AI systems is memory: the bandwidth, latency, and architecture of memory can dramatically influence the performance and efficiency of AI models, particularly at scale. In recent years, an array of startups has [...]

Ontology population—the process of enriching ontologies with new instances, properties, and relationships—is a critical task for maintaining the relevance and utility of semantic structures in diverse scientific and industrial domains. As ontologies grow in complexity and scope, manual curation becomes increasingly unscalable, while fully automated approaches often lack the nuanced understanding required for high-quality results. [...]

In the rapidly evolving landscape of artificial intelligence and robotics, the concept of a "triple"—a fundamental data structure in knowledge representation—has become a cornerstone in connecting perception, reasoning, and action. Exploring the lifecycle of a triple from its inception to its final influence on robotic decision-making not only offers insight into the inner workings of [...]

In the rapidly evolving landscape of data science and knowledge graphs, the importance of recording data provenance cannot be overstated. Provenance—the detailed documentation of the origins, transformations, and history of data—serves as the backbone of transparency, reproducibility, and trust in scientific workflows, business intelligence, and artificial intelligence systems. Without a systematic approach to provenance, researchers [...]

The relationship between machine memory and human memory remains a fertile ground for interdisciplinary inquiry. As artificial intelligence systems increasingly mimic, complement, or even surpass certain facets of human cognition, it becomes essential to understand both the profound differences and subtle similarities between these two forms of memory. This understanding not only informs theoretical perspectives [...]

Robotics has rapidly evolved from isolated, single-purpose machines into complex, interconnected agents capable of autonomous decision-making. As these systems gain responsibility and autonomy, their ability to remember, interpret, and act upon context becomes critical—not just for efficiency, but for safety. Yet, despite remarkable advances, many robotics incidents can be traced back to a simple but [...]

In recent years, the landscape of ontology storage has been shaped not only by advances in technology but also by evolving standards and legislative frameworks. As organizations increasingly rely on ontologies to represent complex domains, the need for robust, interoperable, and compliant storage solutions has become paramount. The trajectory of these changes is defined by [...]

Reinforcement Learning (RL) has emerged as the cornerstone of modern artificial intelligence research, powering systems from game-playing agents to autonomous robots. At its core, RL relies on agents learning through interactions with environments, guided by the principle of maximizing cumulative reward. However, the design of reward signals—how, when, and what to reward—remains a delicate and [...]

GraphQL has fundamentally transformed the way modern applications interact with data sources, offering an expressive and flexible API layer. When the underlying data is not a traditional database but an ontology memory—a semantic model of knowledge encoded in frameworks like OWL or RDF—the design patterns and engineering challenges differ significantly. This tutorial explores how to [...]

In the digital age, knowledge workers have become the architects of modern progress. Their insights, ideas, and connections fuel innovation and drive organizations forward. Yet, despite an abundance of note-taking applications and information management tools, the true potential of personal knowledge often remains locked inside siloed apps, fragmented across devices, and disconnected from the broader [...]

As artificial intelligence systems become increasingly integrated into our daily lives, the need for transparency, interpretability, and effective communication of their internal processes intensifies. Central to this endeavor is the translation of ontological logs—structured, often machine-oriented records of AI reasoning—into explanations comprehensible to human users. The journey from ontological logs to user-friendly narratives is not [...]

In the realm of modern medicine, the integration of artificial intelligence and robotics has catalyzed a quiet revolution. Among the latest advancements, the collaboration between Partenit and surgical robotics platforms stands as a hallmark of interdisciplinary innovation. This partnership has not merely elevated the technical prowess of surgical robots; it has fundamentally redefined how procedural [...]

In the rapidly evolving field of robotics, the concept of semantic digital twins has emerged as a transformative approach for integrating physical robot telemetry with high-level ontological models. This integration not only enhances the understanding and management of robotic systems but also provides a robust foundation for intelligent decision-making, advanced diagnostics, and human-robot collaboration. Understanding [...]

In the rapidly evolving landscape of autonomous systems, drones are becoming essential agents that capture, process, and act on data in real-time. The architectural challenge is to leverage both the immediacy of edge computation and the scalability of the cloud. This article navigates the design of a split architecture, where edge drones synchronize their local [...]

Large Language Models (LLMs) have emerged as transformative tools in the landscape of artificial intelligence, opening new avenues for the alignment of disparate ontologies. The alignment of ontologies—the mapping of concepts and relationships across different structured knowledge representations—is a longstanding challenge in knowledge engineering, one that is foundational for semantic interoperability across databases, knowledge graphs, [...]

Artificial intelligence systems increasingly rely on ontologies and curated datasets to structure knowledge, understand context, and make decisions. However, ontologies and training data are not immune to bias—both explicit and implicit—which can propagate through to agent behavior, leading to unfair, unreliable, or unexplainable outcomes. Addressing bias is thus not simply a technical detail but a [...]

Integrating persistent memory into conversational AI systems represents a significant leap in the quest for more contextually aware and intelligent agents. Over the years, the challenge of maintaining long-term conversational context in chatbots has persisted, with most solutions relying on ephemeral memory that fades with each session. However, with the advent of tools like Partenit [...]

In the rapidly advancing field of artificial intelligence, the drive to create models that are not only powerful but also interpretable and efficient has become paramount. As researchers and developers strive to balance model complexity with practical utility, three key performance axes demand close attention: retention depth, latency, and explainability. While numerous benchmarks exist for [...]

Over the past decade, the landscape of artificial intelligence and machine learning has been significantly shaped by the emergence of Model-as-a-Service (MaaS) providers. These companies offer scalable, on-demand access to advanced machine learning models, democratizing AI and lowering the barriers for organizations of all sizes. Understanding the evolution of MaaS, the intricacies of their pricing [...]