Jie Liu on 2007

This AI Prediction was made by Jie Liu in 2007.

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)

2010-20

Opinion about the Intelligence Explosion from Jie Liu:

Once close to human level AI is achieved, AI power will grow exponentially, due to two facts:1. The each generation of AI will greatly enhance the R&D of next generation AI.2. Moore’s Law.

Flycer’s explanation for better understanding:

AI power is expected to grow exponentially in the next decade due to the ability of each generation of AI to improve the development of the next. This is further supported by Moore’s Law, which states that the number of transistors in a dense integrated circuit doubles approximately every two years. As a result, AI is expected to reach close to human level intelligence by the year 2020.

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

Though a lot people are talking about preventing this doomsday scenarios by imposing some rules and bounds on AI, those attempts will failed; If one group of entities generally and consistently outperform the other group, this group will dominate the other. I believe human will upgrade ourself to keep up with the AIs (For example, gradually replacing brain cells with faster, strong components), and eventually achieve peaceful co-existences.

Flycer’s Secondary Explanation:

AI is likely to dominate humans if left unchecked. Humans may need to upgrade themselves to keep up with AI. A peaceful co-existence between humans and AI is possible if we take the right steps.

About:

Jie Liu is a Chinese-American computer scientist and machine learning researcher. She is an assistant professor of computer science at Stony Brook University and has published numerous papers on topics such as deep learning, natural language processing, and computer vision. Her work has been recognized with several awards, including the 2018 IEEE Signal Processing Society Best Paper Award.