conference

**Conference** Top AI conferences include NeurIPS (Neural Information Processing Systems—broad ML/AI, ~10K attendees), ICML (International Conference on Machine Learning—theory and algorithms), ICLR (International Conference on Learning Representations—deep learning focus), ACL (Association for Computational Linguistics—NLP), CVPR (Computer Vision and Pattern Recognition), and AAAI (general AI). These venues showcase cutting-edge research with rigorous peer review (15-25% acceptance rates). Publication venues: (1) conferences (primary in AI/ML—faster than journals, 6-month review cycle), (2) journals (JMLR, PAMI, AIJ—longer review, archival), (3) workshops (specialized topics, less competitive). ArXiv preprints: researchers post papers to arXiv.org before/during conference review—enables rapid dissemination and community feedback. Major AI labs (OpenAI, DeepMind, Meta, Google) often release technical reports directly. Reading strategy: (1) follow top conferences (proceedings available online), (2) monitor arXiv (cs.LG, cs.CL, cs.CV categories), (3) use aggregators (Papers with Code, Hugging Face Daily Papers), (4) follow influential researchers on Twitter/social media. Impact metrics: citation count, h-index, but note that practical impact (deployed systems, open-source adoption) increasingly valued alongside academic citations. Staying current: AI moves fast—papers from 2-3 years ago may be outdated, focus on recent work and foundational classics. Conference attendance: valuable for networking, learning trends, and recruiting, but expensive—many offer virtual options or recorded talks.