Fuentes · Parte XLII
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Fuentes y lecturas del capítulo#
- NIST. Artificial Intelligence Risk Management Framework 1.0. 2023.
- Google. Machine Learning Crash Course: Datasets, Generalization, and Overfitting. Consulta: 7 de octubre de 2026.
- Peter Flach. Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, 2012.
- Shalmali Joshi et al. A Review of the Role of Causality in Developing Trustworthy AI Systems. 2022.
- Adarsh Subbaswamy, Peter Schulam y Suchi Saria. Preventing Failures Due to Dataset Shift. AISTATS, 2019.
- Richard S. Sutton y Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 2018.
Fuentes y lecturas del capítulo#
- David E. Rumelhart, Geoffrey E. Hinton y Ronald J. Williams. Learning Representations by Back-propagating Errors. Nature, 1986.
- Ian Goodfellow, Yoshua Bengio y Aaron Courville. Deep Learning. MIT Press, 2016.
- Ashish Vaswani et al. Attention Is All You Need. 2017.
- NIST. Four Principles of Explainable Artificial Intelligence. 2021.
- Richard S. Sutton. The Bitter Lesson. 2019.
Fuentes y lecturas del capítulo#
- Ashish Vaswani et al. Attention Is All You Need. 2017.
- Tom B. Brown et al. Language Models Are Few-Shot Learners. 2020.
- Patrick Lewis et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. 2020.
- NIST. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. 2024.
- Dor Muhlgay et al. Generating Benchmarks for Factuality Evaluation of Language Models. 2024.
Fuentes y lecturas del capítulo#
- Alec Radford et al. Learning Transferable Visual Models From Natural Language Supervision. ICML, 2021.
- Jonathan Ho, Ajay Jain y Pieter Abbeel. Denoising Diffusion Probabilistic Models. 2020.
- NIST. Reducing Risks Posed by Synthetic Content. 2024.
- C2PA. Content Credentials Technical Specification. Versión 2.4, consulta: 7 de octubre de 2026.
- NIST. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. 2024.
Fuentes y lecturas del capítulo#
- NIST. Artificial Intelligence Risk Management Framework 1.0. 2023.
- NIST. AI Resource Center: Testing, Evaluation, Verification and Validation. Consulta: 7 de octubre de 2026.
- Margaret Mitchell et al. Model Cards for Model Reporting. 2019.
- Inioluwa Deborah Raji et al. Closing the AI Accountability Gap. 2020.
- Chuan Guo et al. On Calibration of Modern Neural Networks. ICML, 2017.
- Percy Liang et al. Holistic Evaluation of Language Models. 2022.
Fuentes y lecturas del capítulo#
- NIST. Research Opportunities for Advancing Measurement Science for Manufacturing Robotics. 2024.
- NIST. Robotics Program. Proyectos y metrología de percepción, manipulación, movilidad e interacción; consulta: 7 de octubre de 2026.
- ISO. ISO 10218-1:2025 — Robotics — Safety requirements — Part 1: Industrial robots. 2025.
- ISO. ISO 10218-2:2025 — Robotics — Safety requirements — Part 2: Industrial robot applications and robot cells. 2025.
- Jeremy Marvel, Megan Zimmerman y Shelly Bagchi. State-of-the-Art in Human-Robot Interaction. NIST, 2018.
Fuentes y lecturas del capítulo#
- NIST. Mary Frances Theofanos, Yee-Yin Choong y Theodore Jensen. AI Use Taxonomy: A Human-Centered Approach. 2024.
- NASA. R. Eric Chancey. A Human-Autonomy Teaming Framework for Flight Operations. 2020.
- NASA. David D. Woods y Nadine B. Sarter. Learning from Automation Surprises and “Going Sour” Accidents. 1998.
- Federal Aviation Administration. Human Factors in Aviation Safety. Informes de uso operacional y diseño de interfaces; actualización consultada: 7 de octubre de 2026.
- International Labour Organization. Effects of digitalization on the human centricity of social security administration and services. 2023.
- International Labour Organization. Revolutionizing health and safety: the role of AI and digitalization at work. 2025.