相关论文: Learning Occupational Task-Shares Dynamics for the…
In the rapidly evolving field of autonomous driving, reliable prediction is pivotal for vehicular safety. However, trajectory predictions often deviate from actual paths, particularly in complex and challenging environments, leading to…
Allocating tasks to heterogeneous robot teams in environments with uncertain task requirements is a fundamentally challenging problem. Redundantly assigning multiple robots to such tasks is overly conservative, while purely reactive…
This paper examines how digital transformation reshapes employment structures within Chinese listed firms, focusing on occupational functions and task intensity. Drawing on recruitment data classified under ISCO-08 and the Chinese Standard…
Recent waves of technological transformation are reshaping work in uncertain and hard-to-predict ways. However, jobs at the forefront of the digitizing economy offer an early glimpse of these changes and leave rich activity traces. We…
Despite widespread speculation about artificial intelligence's impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for…
In this paper we conduct a longitudinal analysis of the structure of labour markets in the US over 7 decades of technological, economic and policy change. We make use of network science, natural language processing and machine learning to…
Machine learning plays an ever-bigger part in online recruitment, powering intelligent matchmaking and job recommendations across many of the world's largest job platforms. However, the main text is rarely enough to fully understand a job…
Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then can be used by anyone in the community (including those without data or compute resources to…
The proliferation of Artificial Intelligence (AI) in workplaces stands to change the way humans work, with job satisfaction intrinsically linked to work life. Existing research on human-AI collaboration tends to prioritize performance over…
Economic issues, such as inflation, energy costs, taxes, and interest rates, are a constant presence in our daily lives and have been exacerbated by global events such as pandemics, environmental disasters, and wars. A sustained history of…
Since late 2022, generative AI has taken the world by storm, with widespread use of tools including ChatGPT, Gemini, and Claude. Generative AI and large language model (LLM) applications are transforming how individuals find and access data…
The temporal lag between actions and their long-term consequences makes credit assignment a challenge when learning goal-directed behaviors from data. Generative world models capture the distribution of future states an agent may visit,…
Simultaneous load forecasting across multiple entities (e.g., regions, buildings) is crucial for the efficient, reliable, and cost-effective operation of power systems. Accurate load forecasting is a challenging problem due to the inherent…
AI Scaling has traditionally been synonymous with Scaling Up, which builds larger and more powerful models. However, the growing demand for efficiency, adaptability, and collaboration across diverse applications necessitates a broader…
Many tasks, particularly those involving interaction with the environment, are characterized by high variability, making robotic autonomy difficult. One flexible solution is to introduce the input of a human with superior experience and…
Learned dynamics models combined with both planning and policy learning algorithms have shown promise in enabling artificial agents to learn to perform many diverse tasks with limited supervision. However, one of the fundamental challenges…
Widespread integration of Generative AI tools is transforming white-collar work, reshaping how workers define their roles, manage their tasks, and collaborate with peers. This has created a need to develop an overarching understanding of…
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real-world environments, such as urban streets or crowded areas, the…
Does technological change destroy or create jobs? New technologies may replace human workers, but can simultaneously create jobs if workers are needed to use these technologies or if new economic activities emerge. Furthermore,…
Current efforts in AI safety prioritize filtering harmful content, preventing manipulation of human behavior, and eliminating existential risks in cybersecurity or biosecurity. While pressing, this narrow focus overlooks critical…