In brief Defining the Timeline: How 2025 Context Shapes Our AGI Forecasts The question of when AGI will arrive is inseparable from how we define AGI itself. Different researchers, companies, and consortiums vary in their interpretation, ranging from systems that can autonomously conduct cross-domain problem solving to a more nuanced benchmark of generalization across tasks …
En bref The following article examines whether Large Language Models (LLMs) are a pinnacle of genuine artificial intelligence or whether they primarily excel at mimicking human thought. It begins with a precise look at what we mean by “real AI” versus “simulation,” then moves through the mechanics of LLMs, how researchers judge understanding, and what …
En bref Résumé d’ouverture Geoffrey Hinton’s intellectual odyssey traces the emergence of neural networks from a niche curiosity to the backbone of contemporary artificial intelligence. Born in 1947, he stands as a link between the early explorations of cognitive psychology and the practical, scalable systems that power today’s AI ecosystems. His lineage stretches back to …
En bref In the rapidly evolving field of artificial intelligence, terminology acts as a shared compass. By 2025, researchers and practitioners rely on a robust vocabulary to communicate about data representations, learning paradigms, model architectures, evaluation metrics, and governance practices. The landscape spans foundational ideas—data, features, labels, and loss functions—through to state-of-the-art constructs like transformers, …
In a fast-moving landscape where machines increasingly augment human decision-making, understanding key concepts in artificial intelligence is no longer optional. This comprehensive guide dives deep into foundational ideas, practical architectures, real-world deployments, ethical considerations, and the evolving terminology that shapes AI practice in 2025. Readers will move beyond buzzwords toward a structured map of terms, …
En bref In 2025, the AI landscape has matured into a dense, interoperable ecosystem where language models, data pipelines, and governance practices intersect across industries. Companies deploy transformers and large language models (LLMs) to automate writing, coding, translation, and decision support, while researchers push advances in safety, alignment, and interpretability. The language of AI is …
En bref: – A comprehensive journey through the language of artificial intelligence, from fundamental terms to cutting-edge architectures and deployment practices. – Clear explanations of ML paradigms, neural models, and major platform ecosystems used in industry in 2025. – Practical examples, real-world case studies, and a curated glossary of terms tied to OpenAI, DeepMind, IBM …
En bref Across the last decade, the language of artificial intelligence has shifted from academic jargon to everyday parlance. As AI systems become embedded in business, healthcare, and consumer tech, understanding the vocabulary is not a luxury but a necessity. This piece examines how language is used to describe capabilities, limits, and risks, and how …
En bref In the rapidly evolving field of artificial intelligence, terminology acts as a shared compass. By 2025, the language of AI has widened beyond technical staff to product teams, policy makers, and everyday practitioners who work with AI-enabled tools. This glossary goes beyond simple definitions to explain how terms emerge from real systems, models, …
In the rapidly evolving field of artificial intelligence, the vocabulary you use shapes how you think about problems, design systems, and communicate insights. This guide is a practical roadmap to the language of AI as it stands in 2025, weaving together foundational terms, contemporary models, tooling ecosystems, and governance considerations. As you read, you’ll notice …