AGI or Artificial General Intelligence

Artificial General Intelligence (AGI) refers to the hypothetical development of artificial intelligence systems that can match or exceed human-level abilities across a wide range of cognitive tasks, from reasoning and problem-solving to creativity and social interaction. It is one of the most important potential outcomes of the AI race currently underway! Unlike narrow AI systems that excel at specific, well-defined tasks, AGI would possess general intelligence akin to the human mind, with the capacity to adapt and learn in flexible and open-ended ways.

The potential development of AGI is of critical importance for the future of humanity. If realized, AGI could hold the key to solving many of the world's most pressing problems, from curing diseases and reversing climate change to expanding our scientific understanding and unlocking new avenues for technological progress. AGI systems with superintelligent capabilities could vastly accelerate the pace of innovation and discovery, potentially ushering in a new era of abundance, prosperity, and longevity for humanity.

However, the advent of AGI also raises profound concerns and challenges. The creation of an intelligence that surpasses human abilities in every domain poses significant risks, both known and unknown. There are concerns about the potential for AGI systems to become misaligned with human values and interests, leading to unintended consequences or even existential threats to humanity. Addressing the challenges of ensuring that AGI systems are safe, ethical, and beneficial to humanity as a whole will require concerted efforts from researchers, policymakers, and the public to develop robust frameworks for the development and deployment of these transformative technologies.

Scientific Articles on AGI

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

  2. Yudkowsky, E. (2008). Artificial Intelligence as a Positive and Negative Factor in Global Risk. Springer.

  3. Russell, S. J. (2019). Human Compatible: AI and the Problem of Control. Viking.

  4. Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.

  5. Bryson, J. J. (2018). Stable Artificial Intelligence. ArXiv.

  6. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete Problems in AI Safety. ArXiv.

  7. Shanahan, M. (2015). The Technological Singularity. MIT Press.

  8. Müller, V. C., & Bostrom, N. (2016). Future Progress in Artificial Intelligence: A Survey of Expert Opinion. Fundamental Issues of Artificial Intelligence.

  9. Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018). When Will AI Exceed Human Performance? Evidence from AI Experts. ArXiv.

  10. Yampolskiy, R. V. (2016). Artificial Superintelligence: A Futuristic Approach. CRC Press.

  11. Clark, J., & Amodei, D. (2016). Faulty Reward Functions in the Wild. OpenAI.

  12. Hadfield-Menell, D., Russell, S. J., Abbeel, P., & Dragan, A. (2016). Cooperative Inverse Reinforcement Learning. Advances in Neural Information Processing Systems.

  13. Leike, J., Krueger, D., Everitt, T., Hutter, M., Saftien, J., & Amodei, D. (2018). Debate. ArXiv.

  14. Carey, R., Langlois, G., Leike, J., Cotra, A., & Tooth, G. (2021). Translucent Boxes: Opening the Black Box of AI Systems. ArXiv.

  15. Hubinger, E., van Merwijk, C., Mikulik, V., Skalse, J., & Garrabrant, S. (2019). Constituional Crises in Algorithmic Governance. ArXiv.

  16. Krakovna, V., Orseau, L., Martic, M., & Legg, S. (2020). Avoiding Side Effects in Complex Environments. ArXiv.

  17. Christiano, P. F., Shlegeris, B., & Amodei, D. (2018). Supervising Strong Learners by Amplifying Weak Experts. ArXiv.

  18. Turner, A. M., Hadfield-Menell, D., & Tadepalli, P. (2020). Conservative Agency via Attainable Utility Preservation. ArXiv.

  19. Drexler, K. E. (2019). Reframing Superintelligence: Comprehensive AI Services as the Key Path to Existential Safety. Institute for Applied AI.

  20. Baum, S. D. (2017). On the Promotion of Safe and Socially Beneficial Artificial Intelligence. AI & Society.

  21. Yampolskiy, R. V. (2013). Artificial Superintelligence: Safety and Security. CRC Press.

  22. Dafoe, A. (2018). AI Governance: A Research Agenda. Centre for the Governance of AI.

  23. Reisman, D., & Pearce, P. (2018). Existential Risk and Cost-Effective Biosafety Control Measures. Foresight Institute.

  24. Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., ... & Anderljung, M. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. ArXiv.

  25. Gibney, E. (2017). The Scientist Who Wants to Upload Humanity to a Computer. Nature.

  26. Turchin, A., & Denkenberger, D. (2020). Classification of Global Catastrophic Risks Connected with Artificial Intelligence. AI & Society.

  27. Russell, S. J., Dewey, D., & Tegmark, M. (2015). Research Priorities for Robust and Beneficial Artificial Intelligence. AI Magazine.

  28. Fossa, J. (2021). Artificial General Intelligence and the Human Mental Model. Minds and Machines.

  29. Sotala, K., & Yampolskiy, R. V. (2015). Advantages and Disadvantages of Artificial Intellects. International Journal of Machine Consciousness.

  30. Waser, M. R. (2013). Rational Universal Benevolence: Simulated Recursively Self-Improving Altruistic Intelligence. Complexity.

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