References & Further Reading

📖 Primary References

Vaswani, A., Shazeer, N., Parmar, N., et al. "Attention Is All You Need". In: Advances in Neural Information Processing Systems 30 (NeurIPS 2017). Curran Associates, Inc., 2017, pp. 5998–6008.
Journal • View Source →
LeCun, Y., Bengio, Y., & Hinton, G. "Deep Learning". In: Nature 521.7553 (2015): 436–444.
Journal • DOI: 10.1038/nature14539
Russell, S. & Norvig, P. Artificial Intelligence: A Modern Approach. 4th ed. Pearson Education, 2020.
Book • ISBN: 978-0134610205
Bostrom, N. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014.
Book • Philosophy / Ethics
OpenAI. "GPT-4 Technical Report". OpenAI Research, 2023.
Technical Report • arXiv:2303.08774
Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006.
Book • Foundational Text

📚 Further Reading

2024 • MIT Press

The Alignment Problem: Machine Learning and Human Values

Michal Kosinski explores the challenges of aligning AI systems with human values and ethics in the age of large language models.

CS229: Machine Learning (Supervised Learning)

Comprehensive lecture notes and materials on statistical pattern recognition, learning theory, and optimization methods.

🎥 Video Available Free

📰

Review Article

Nature Reviews AI

Large Language Models: A Survey

A comprehensive survey covering the history, architecture, training techniques, and applications of large language models.

The Future of Knowledge: AI & Encyclopedias

Interview series with leading researchers on how AI is transforming knowledge creation, verification, and dissemination.

🎵 Audio 4 Episodes

🌐 External Resources

arXiv.org e-Print archive — Open access repository for scholarly articles in computer science and related fields.
arxiv.org
Papers with Code — Linking research papers to source code implementations and benchmarks.
paperswithcode.com
Google Scholar — Search engine for scholarly literature across all disciplines and sources.
scholar.google.com

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