Recent Advances in Machine Learning Technologies

Abstract The last three years have experienced a massive consolidation and diversification of machine-learning research and deployment. Breakthroughs in language, vision and multimodal tasks continue to rely on foundation models and transformers, whereas efficiency, adaptively and retrieval have emerged as themes. The paper is a survey of the main technological advances (20232025): foundation and multimodal models, retrieval-augmented generation (RAG), parameter-efficient fine-tuning (PEFT) models such as LoRA variants, run-time/compute optimizations such as FlashAttention-2, and generative vision model advances (e.g., SDXL) or promptable segmentation (SAM). We talk about practical use, evaluation issues (robustness, alignment, hallucination), and research directions such as continual learning, causal representation learning and greener training. Simultaneously, image understanding and generation have been extended by vision technologies like DINOv2, the Segment Anything Model (SAM), and image generation models like high-fidelity diffusion models such as SDXL. These innovations have facilitated their use in knowledge assistants, content creation, perception of robotics, and deployment of edges. Although things have been developing at a very fast pace, there are still difficulties related to the reduction in hallucination, assessment reliability, ecological sustainability, and congruence. The paper is a systematic review of some of the important technologies, system applications, constraints, and future research trends that define the present machine learning landscape.