References
Research, reports, and primary sources cited throughout AI Maxims.
Foundations & AI Architecture
Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS 2017.
Brown, T. et al. (2020). Language Models are Few-Shot Learners. NeurIPS 2020.
Bommasani, R. et al. (2021). On the Opportunities and Risks of Foundation Models. Stanford CRFM.
Anthropic. (2024). Claude Model Cards and System Cards. anthropic.com/research.
OpenAI. (2024). GPT-4 Technical Report. openai.com/research.
AI Safety, Alignment & Ethics
Bender, E. et al. (2021). On the Dangers of Stochastic Parrots. FAccT 2021.
Russell, S. (2019). Human Compatible. Viking.
EU AI Act. (2024). Regulation (EU) 2024/1689. Official Journal of the EU.
Prompt Engineering & AI Practice
Wei, J. et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in LLMs. NeurIPS 2022.
White, J. et al. (2023). A Prompt Pattern Catalog. arXiv:2302.11382.
Schulhoff, S. et al. (2024). The Prompt Report. arXiv:2406.06608.
AI in Professional Practice
Goldman Sachs Research. (2023). The Potentially Large Effects of AI on Economic Growth.
McKinsey Global Institute. (2023). The Economic Potential of Generative AI.
World Economic Forum. (2023). Future of Jobs Report 2023.
Cybersecurity & AI Threats
FBI Internet Crime Complaint Center. (2024). Internet Crime Report 2023.
ENISA. (2023). Threat Landscape for AI Systems.
Chesney, R. & Citron, D. (2019). Deep Fakes. California Law Review, 107.
All Citations in Full
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