When Will AI Exceed Human Performance?
Advances in artificial intelligence (AI) will have massive social consequences. Self-driving technology might replace millions of driving jobs over the coming decade. In addition to possible unemployment, the transition will bring new challenges, such as rebuilding infrastructure, protecting vehicle cyber-security, and adapting laws and regulations . New challenges, both for AI developers and policy-makers, will also arise from applications in law enforcement, military technology, and marketing . To prepare for these challenges, accurate forecasting of transformative AI would be invaluable.
Several sources provide objective evidence about future AI advances: trends in computing hardware , task performance , and the automation of labor . The predictions of AI experts provide crucial additional information. We survey a larger and more representative sample of AI experts than any study to date [10, 11]. Our questions cover the timing of AI advances (including both practical applications of AI and the automation of various human jobs), as well as the social and ethical impacts of AI.
Time Until Machines Outperform Humans
AI would have profound social consequences if all tasks were more cost effectively accomplished by machines. Our survey used the following definition: “High-level machine intelligence” (HLMI) is achieved when unaided machines can accomplish every task better and more cheaply than human workers. 1 arXiv:1705.08807v2 [cs.AI] 30 May 2017 Each individual respondent estimated the probability of HLMI arriving in future years. Taking the mean over each individual, the aggregate forecast gave a 50% chance of HLMI occurring within 45 years and a 10% chance of it occurring within 9 years. Figure 1 displays the probabilistic predictions for a random subset of individuals, as well as the mean predictions. There is large inter-subject variation: Figure 3 shows that Asian respondents expect HLMI in 30 years, whereas North Americans expect it in 74 years.
Original PDF article here: https://arxiv.org/pdf/1705.08807.pdf