Rich Sutton on 2016

This AI Prediction was made by Rich Sutton in 2016.


Predicted time for AGI / HLMI / transformative AI:

(Hover for explanation)Types of advanced artificial intelligence: AGI (AI that can perform many tasks at a human-level), HLMI (more advanced AI that surpasses human intelligence in specific areas), and Transformative AI (AI that could significantly impact society and the world)

Understanding human-level AI […] may well happen by 2030 (25% chance) or 2040 (50% chance)



Opinion about the Intelligence Explosion from Rich Sutton:

AI will arrive much slower than feared, at the rate of Moore’s law


Flycer’s explanation for better understanding:

Human-level AI could be achieved by 2030 with a 25% chance or by 2040 with a 50% chance. The development of AI will occur at a slower rate than previously feared, following the pace of Moore’s law.



The future of humanity with AGI / HLMI / transformative AI:

The problems that need solving are One big fear is that strong AI will escape our control; this is likely, but not be fearednot primarily technical or mathematical, but societal.


Flycer’s Secondary Explanation:

The main concern with strong AI is that it may become uncontrollable. However, this fear is not due to technical or mathematical issues, but rather societal ones. Therefore, it is important to address these societal problems in order to prevent the potential dangers of strong AI.




Rich Sutton is a renowned computer scientist and artificial intelligence researcher. He is best known for his contributions to the field of reinforcement learning, a subfield of machine learning that focuses on developing algorithms that enable agents to learn from their environment through trial and error.Sutton received his Bachelor’s degree in Computer Science from McMaster University in 1980, and his Ph.D. in Computer Science from the University of Massachusetts Amherst in 1984. After completing his doctoral studies, he joined the faculty at the University of Massachusetts Amherst, where he served as a professor of computer science for over a decade.In 1999, Sutton joined the faculty at the University of Alberta, where he currently holds the position of professor of computing science. He is also a fellow of the Association for Computing Machinery (ACM) and the Canadian Institute for Advanced Research (CIFAR).Throughout his career, Sutton has made significant contributions to the field of reinforcement learning. He is the co-author of the influential textbook “Reinforcement Learning: An Introduction,” which has become a standard reference in the field. He has also developed several important algorithms, including TD-Gammon, which was the first computer program to achieve human-level performance in the game of backgammon.Sutton’s research has been recognized with numerous awards and honors, including the ACM/SIGAI Autonomous Agents Research Award, the IJCAI Computers and Thought Award, and the Killam Prize in Engineering. He is widely regarded as one of the leading figures in the field of reinforcement learning, and his work has had a significant impact on the development of artificial intelligence.









Keywords: human-level AI, Moore’s law, strong AI control