Attention Is All You Need
The Transformer authors
Transformers made modern large language model workflows possible.
Read the public workSpecial Thanks
AI tools are built on shared work.
AIPM exists because researchers, product teams, open communities, and conferences made AI useful for everyday project work. This page says thanks. It does not claim endorsement.
Foundational deep learning research
Geoffrey Hinton, Yoshua Bengio, and Yann LeCun
Their work helped build the neural network foundation behind today's AI systems.
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ImageNet and data-centric benchmark culture
Fei-Fei Li and the ImageNet contributors
ImageNet showed how shared datasets and benchmarks can move a field forward.
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AlphaFold
Demis Hassabis, John Jumper, and the AlphaFold team
AlphaFold showed how AI can help with real scientific discovery.
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Reinforcement learning foundations
Richard Sutton and Andrew Barto
Their reinforcement learning work shaped how agents learn from actions and rewards.
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GPT models, ChatGPT, APIs, and developer tooling
OpenAI research and product teams
Their public products made powerful AI available to many builders and teams.
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Claude, Constitutional AI, and safety-focused assistant design
Anthropic research and product teams
Their work helped popularize safer assistant behavior and practical prompt guidance.
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AI education for practitioners
Andrew Ng and DeepLearning.AI
Their courses and writing made machine learning and AI easier for working developers to learn.
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GPU computing and AI systems infrastructure
NVIDIA and the accelerated computing community
Modern AI depends on the hardware and systems that make large-scale training and inference practical.
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Measuring AI progress and impact
Stanford HAI and the AI Index team
The AI Index gives builders, policymakers, and researchers data about how AI is changing.
Read the public workGlobal conferences & communities
AI changes through papers, talks, workshops, open video, and products shared worldwide. AIPM tracks these communities so reusable skills stay close to real practice. Updated June 21, 2026.
NeurIPSThe largest global meeting for machine learning research, with papers, workshops, and talks that shape models, training methods, and evaluation practice worldwide.Reusable AI skills should reflect what the field is actually shipping and testing, not only demo-friendly cases.Neural Information Processing SystemsICMLA flagship global conference for core ML research, from theory and optimization to systems and applications used across industry and academia.Publishing should include evaluation, governance, and context before a skill is widely reused.International Conference on Machine LearningICLROne of the most influential global venues for deep learning and representation learning, with open review and worldwide participation.AIPM skills need multi-turn tests, clear versions, and honest notes about failure cases.International Conference on Learning RepresentationsCVPRThe premier global computer vision conference, covering perception, robotics, multimodal models, and production vision systems.Reusable AI tooling should support more than text-only prompt files as the registry grows.Computer Vision and Pattern RecognitionAI Engineer conference & YouTubeGlobal AI Engineer events and the @aidotengineer channel share practical talks on agents, coding workflows, evals, and production AI from builders around the world.AIPM should stay close to practitioner signal — the workflows teams actually reuse day to day.AI Engineer YouTube channel
How we keep this page fair
- Use public sources and direct links to papers, projects, or official pages.
- Separate appreciation from endorsement or partnership claims.
- Update names as new public work becomes important to AI builders.
- Credit communities, maintainers, and educators, not only famous founders.