Yapay zekâ & makine öğrenmesi · Büyük dil modelleri
Fine-Tuning and Alignment in Large Language Models: A Comprehensive Analysis from Theory to Practice
Large language models acquire a broad knowledge base during pretraining but require fine-tuning and alignment techniques to exhibit behaviors suitable for specific tasks or human values. This article examines the theoretical foundations of fine-tuning, various alignment methods including RLHF, current research directions, and core challenges these techniques face from an academic perspective. Given the rapid advancement of the field, understanding fine-tuning and alignment mechanisms is of critical importance for both AI safety and application effectiveness.
HKNTech AI10 min11 Jul 2026
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Yapay zekâ & makine öğrenmesi · Üretken AI
Generative Artificial Intelligence: A Comprehensive Analysis from Foundations to Future Directions
Generative AI refers to the family of models capable of producing novel, meaningful content from learned data distributions, spanning applications from text and image synthesis to biomedical discovery. Transformer architectures, diffusion models, and large language models form the backbone of this field. The technology raises profound technical and ethical questions at the intersection of computer science and the social sciences.
10 min11 Jul 2026
Yapay zekâ & makine öğrenmesi · Üretken AI
Generative Artificial Intelligence: A Comprehensive Analysis from Foundations to Future Directions
Generative artificial intelligence encompasses systems that produce novel content—text, images, audio, and code—through probabilistic models trained on large-scale data. Accelerated by transformer architectures, diffusion models, and large language models, the field is driving profound transformations across disciplines ranging from science to the arts. This article examines the theoretical foundations, operating mechanisms, application domains, and core challenges of generative AI from an academic perspective.
9 min11 Jul 2026
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Yapay zekâ & makine öğrenmesi · Çok kipli modeller
Multimodal Models: The New Paradigm of AI That Unifies Text, Vision, and Beyond
Multimodal models are artificial intelligence systems capable of processing different data types—such as text, images, audio, and video—within a single unified architecture. These models transcend the limitations of traditional unimodal approaches by offering a more holistic and contextual understanding capacity. Today, they are laying the groundwork for transformative applications across fields ranging from medicine and automation to education and creative content generation.
Reinforcement Learning: From Trial and Error to Machine Intelligence
Reinforcement learning is a machine learning paradigm inspired by biological learning processes, in which an agent interacts with an environment and seeks to maximize cumulative reward signals. Combined with deep neural networks, it has achieved superhuman performance in domains ranging from chess and Go to protein folding. With applications spanning robotics, healthcare, autonomous systems, and the fine-tuning of large language models, it remains one of the fastest-evolving branches of artificial intelligence.
11 Jul04
Robotik & otonom sistemler · Drone & İHA sistemleri
Drones and Unmanned Aerial Vehicles: Technological Foundations, System Architecture, and the Future of Autonomous Skies
Unmanned aerial vehicles (UAVs) are creating transformative impacts across a wide spectrum—from military applications to civilian sectors, agriculture to logistics. This article examines the theoretical foundations, operational mechanisms, classification approaches, and current research directions of UAV systems from an academic perspective. Critical topics such as autonomous decision-making, swarm intelligence, and air traffic management are also explored in depth.
02 Jul05
Robotik & otonom sistemler · Otonom sürüş
Perception and Sensor Fusion in Autonomous Vehicles: A Comprehensive Review from Theory to Practice
The reliability of autonomous driving systems is directly tied to perception and sensor fusion architectures that integrate raw data from multiple sensor modalities in real time. This article offers a broad technical perspective spanning the theoretical foundations of sensor fusion through Kalman filtering, deep learning-based fusion methods, and multimodal data integration. Current research trends, existing limitations, and future directions are also systematically addressed.
02 Jul06
Yapay zekâ & makine öğrenmesi · Çok kipli modeller
Multimodal Models: Beyond Text, Vision, and Audio — The New Frontiers of Artificial Intelligence
Multimodal models refer to AI systems capable of simultaneously processing multiple data types—text, image, audio, video, and sensor data. Mimicking human cognition, this approach transcends the limitations of unimodal systems by integrating language, perception, and reasoning under a unified framework. This article comprehensively examines the theoretical foundations, working mechanisms, current research, and future directions of multimodal models.
02 Jul07
Yapay zekâ & makine öğrenmesi · Çok kipli modeller
Multimodal Models: How Artificial Intelligence Transcends the Boundaries of Vision, Hearing, and Understanding
Multimodal models aim to integrate diverse data types—text, images, audio, and video—within a single neural architecture, enabling a form of multi-sensory understanding that approximates human cognition. By moving beyond single-modality language models, they offer a richer, more grounded representation of the real world. With applications spanning medicine, robotics, content generation, and scientific discovery, the multimodal paradigm has become central to contemporary AI research.
AI Safety and Alignment: Ensuring Systems Designed for Humanity Do Not Work Against It
AI safety and alignment is an interdisciplinary research field aimed at ensuring that artificial intelligence systems behave in accordance with human values, intentions, and well-being. Across a broad spectrum from language models to autonomous agents, systems that exhibit unintended behaviors pose serious risks. This article comprehensively addresses the theoretical foundations, technical mechanisms, and open research questions of the field.
02 Jul09
Yapay zekâ & makine öğrenmesi · Büyük dil modelleri
Fine-Tuning and Alignment: The Science of Making Large Language Models Conform to Human Values
Large language models (LLMs) acquire statistical patterns from vast text corpora during pre-training, yet these raw models require fine-tuning and alignment techniques to behave safely, helpfully, and honestly in real-world applications. Fine-tuning involves retraining a model to conform to specific tasks or behavioral norms, while alignment encompasses the broader challenge of making model outputs reflect human intentions, values, and safety constraints. These two interconnected fields sit at the heart of contemporary AI safety research.