Creating AGI Within Human Control: How It Works, and What If It Fails?

AGI must also be equitable and inclusive. Bias in training data can lead to systemic discrimination. Thus, safe AGI is not just a technical problem but a deeply social one.

7. Is Safe AGI Possible?

Building AGI is not just a technical feat, but a moral responsibility. Researchers like Nick Bostrom warn about existential risks, while others like Yoshua Bengio and Eliezer Yudkowsky call for global cooperation on AGI safety protocols. The path to safe AGI requires transparency, aligned incentives, robust testing, and continuous human oversight.

AGI does not have to be our downfall---if we build it right.

References

• Amodei, D., et al. (2016). Concrete problems in AI safety. arXiv:1606.06565.

• Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

• Christiano, P., et al. (2017). Deep reinforcement learning from human preferences. arXiv:1706.03741.

• Goertzel, B., et al. (2014). The architecture of OpenCog. Proceedings of AGI Conference.

• Hubinger, E., et al. (2019). Risks from learned optimization in advanced machine learning systems. MIRI Report.

• OpenAI (2023). GPT-4 Technical Report. OpenAI.com.

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