paper-with-me

Papers

Forecasting Transformative AI: An Expert Survey

2019-01-24 · Ross Gruetzemacher, David Paradice, Kang Bok Lee

Transformative AI technologies have the potential to reshape critical aspects of society in the near future. However, in order to properly prepare policy initiatives for the arrival of such technologies accurate forecasts and timelines are necessary. A survey was administered to attendees of three AI conferences during the summer of 2018 (ICML, IJCAI and the HLAI conference). The survey included questions for estimating AI capabilities over the next decade, questions for forecasting five scenarios of transformative AI and questions concerning the impact of computational resources in AI research. Respondents indicated a median of 21.5% of human tasks (i.e., all tasks that humans are currently paid to do) can be feasibly automated now, and that this figure would rise to 40% in 5 years and 60% in 10 years. Median forecasts indicated a 50% probability of AI systems being capable of automating 90% of current human tasks in 25 years and 99% of current human tasks in 50 years. The conference of attendance was found to have a statistically significant impact on all forecasts, with attendees of HLAI providing more optimistic timelines with less uncertainty. These findings suggest that AI experts expect major advances in AI technology to continue over the next decade to a degree that will likely have profound transformative impacts on society.

📄 PDF Abstract BibTeX arXiv:1901.08579

Code (0)

등록된 구현이 없습니다.

Tasks

Survey

Similar Papers 제목 키워드 기반

Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs

2025-10-29 · Alexander Sternfeld, Andrei Kucharavy, Dimitri Percia David, Alain Mermoud 외 arxiv

Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Communication Technologies (ICTs). Traditional expert-based methods struggle …

Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

2022-01-07 · Omid Orang, Petrônio Cândido de Lima e Silva, Frederico Gadelha Guimarães

Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCM have similarities to recur…

SurveyTime SeriesTime Series AnalysisTime Series Forecasting

Human-AI Interaction in Industrial Robotics: Design and Empirical Evaluation of a User Interface for Explainable AI-Based Robot Program Optimization

2024-04-30 · Benjamin Alt, Johannes Zahn, Claudius Kienle, Julia Dvorak 외

While recent advances in deep learning have demonstrated its transformative potential, its adoption for real-world manufacturing applications remains limited. We present an Explanation User Interface (XUI) for a state-of…

Deep Learning

The Evolution of LLM Adoption in Industry Data Curation Practices

2024-12-20 · Crystal Qian, Michael Xieyang Liu, Emily Reif, Grady Simon 외

As large language models (LLMs) grow increasingly adept at processing unstructured text data, they offer new opportunities to enhance data curation workflows. This paper explores the evolution of LLM adoption among pract…

Large Language Models for Security Operations Centers: A Comprehensive Survey

2025-09-13 · Ali Habibzadeh, Farid Feyzi, Reza Ebrahimi Atani arxiv

Large Language Models (LLMs) have emerged as powerful tools capable of understanding and generating human-like text, offering transformative potential across diverse domains. The Security Operations Center (SOC), respons…