Algorithm-based strategy shows promise in reducing urban poverty
Peer-Reviewed Publication
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 19-Nov-2025 02:11 ET (19-Nov-2025 07:11 GMT/UTC)
When the COVID-19 pandemic hit, aid organizations worldwide struggled to identify vulnerable households quickly and fairly. Many people who needed help were left behind.
Woojin Jung, an assistant professor at the Rutgers School of Social Work, said she has found a better strategy. Her team has developed a method that blends sociodemographic data and household surveys with community perceptions and satellite imagery to predict urban poverty – and to put people at the center of aid targeting.
The exploration-exploitation dilemma is a long-standing topic in deep reinforcement learning. In recent research, a noise-driven enhancement for exploration algorithm has proposed for UAV autonomous navigation. This algorithm introduces a differentiated exploration noise control strategy based on the global navigation training hit rate and the specific situations encountered by the UAV in each episode. Furthermore, it designs a noise dual experience replay buffer to amplify the distinct effects of noisy and deterministic experiences. This approach reduces the computational cost associated with excessive exploration and mitigates the problem of the navigation policy converging to a local optimum.