The Odyssey of AI Alignment

Описание: #ai #technology #artificialintelligence #science #education #ethics #robot #humanity

Artificial Intelligence has undergone remarkable advancements in recent years, prompting discussions about the need for AI alignment - ensuring that the goals and behavior of advanced AI systems are in line with human values and intentions. As we stand on the precipice of creating AI systems with the potential to outpace human capabilities, addressing AI alignment becomes imperative to avoid unintended consequences. AI alignment involves the process of designing AI systems that not only accomplish the tasks they are designed for, but also adhere to the values and intentions of human society


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Acknowledgements:


Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety.
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
Christiano, P., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human feedback. Advances in Neural Information Processing Systems.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Hadfield-Menell, D., Dragan, A., Abbeel, P., & Russell, S. J. (2016). Cooperative inverse reinforcement learning.
Leike, J., Martic, M., Krakovna, V., Ortega, P. A., Everitt, T., Lefrancq, A., ... AI safety gridworlds. arXiv preprint arXiv:1711.09883.27 Nov 2017
Leike, J., Krueger, D., Everitt, T., Martic, M., Legg, S. (2018). Scalable agent alignment via reward modeling: A research direction.
Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.