Your interests sorted by most relevant papers matching them
June 19, 2025
climate change
AI based insights:
The Climaborough project aims to quickly build climate solutions for cities. It uses a mix of low-code and no-code development techniques. This approach simplifies climate action for everyone involved. Low-code accelerates dashboard development, reducing coding needs. No-code allows non-technical users to easily create and adapt dashboards. An AI assistant helps with data and answers questions. This enhances data interaction and knowledge sharing. The project integrates with initiatives like NetZeroCities and UP2030. This expansion promotes collaborative climate action across Europe.
The Climaborough project aims to quickly build climate solutions for cities. It uses a mix of low-code and no-code development techniques. This approach simplifies climate action for everyone involved. Low-code accelerates dashboard development, reducing coding needs. No-code allows non-technical users to easily create and adapt dashboards. An AI assistant helps with data and answers questions. This enhances data interaction and knowledge sharing. The project integrates with initiatives like NetZeroCities and UP2030. This expansion promotes collaborative climate action across Europe.
AI based insights:
AI is becoming a weather detective, learning patterns to predict events. It uses data like temperature and humidity to forecast heatwaves. JAX, a high-performance computing tool, speeds up AI model training. Researchers combine AI with established meteorological practices. ERA5, a global weather dataset, is crucial for accurate predictions. Data assimilation merges observations with weather models. The 2021 Pacific Northwest heatwave was studied as a case example. AI models explore the upper bounds of extreme weather likelihoods. These models identify intensified blocking and Rossby wave patterns.
AI is becoming a weather detective, learning patterns to predict events. It uses data like temperature and humidity to forecast heatwaves. JAX, a high-performance computing tool, speeds up AI model training. Researchers combine AI with established meteorological practices. ERA5, a global weather dataset, is crucial for accurate predictions. Data assimilation merges observations with weather models. The 2021 Pacific Northwest heatwave was studied as a case example. AI models explore the upper bounds of extreme weather likelihoods. These models identify intensified blocking and Rossby wave patterns.
research automation
AI based insights:
Here’s a summary of the paper’s insights, presented simply: Researchers recognized that Large Language Models (LLMs) could revolutionize data science. However, existing LLM tools struggled with real-world complexity. These tools relied on fixed processes and inflexible coding. They couldn’t handle the nuanced expertise of human data scientists. The team developed AutoMind, a new LLM agent framework. It uses three key improvements to address this. First, AutoMind incorporates a knowledge base built from expert knowledge. Second, it employs a smart search algorithm to explore solutions. Third, the system adapts its code generation based on the task’s difficulty. Evaluations on standard data science benchmarks showed AutoMind performed better. It was more effective, efficient, and produced higher quality solutions. AutoMind represents a strong step toward truly automated data science.
Here’s a summary of the paper’s insights, presented simply: Researchers recognized that Large Language Models (LLMs) could revolutionize data science. However, existing LLM tools struggled with real-world complexity. These tools relied on fixed processes and inflexible coding. They couldn’t handle the nuanced expertise of human data scientists. The team developed AutoMind, a new LLM agent framework. It uses three key improvements to address this. First, AutoMind incorporates a knowledge base built from expert knowledge. Second, it employs a smart search algorithm to explore solutions. Third, the system adapts its code generation based on the task’s difficulty. Evaluations on standard data science benchmarks showed AutoMind performed better. It was more effective, efficient, and produced higher quality solutions. AutoMind represents a strong step toward truly automated data science.
AI based insights:
The study explores how artificial intelligence is changing social science research. Researchers surveyed 284 social scientists and conducted 15 interviews. A key innovation was splitting the survey group, asking some about “AI” and others about “Machine Learning.” This reflected the growing popularity of generative AI (genAI) tools. Many social scientists are now using genAI for tasks like summarizing research. Some found these tools unsatisfactory, while others have adopted them into their work. Participants expressed concern about genAI compared to traditional ML. They valued ML’s transparency and trust in its statistical basis. Ethical worries included automation bias, potential deskilling, and representational harm. Recommendations were made for developers, researchers, and policymakers.
The study explores how artificial intelligence is changing social science research. Researchers surveyed 284 social scientists and conducted 15 interviews. A key innovation was splitting the survey group, asking some about “AI” and others about “Machine Learning.” This reflected the growing popularity of generative AI (genAI) tools. Many social scientists are now using genAI for tasks like summarizing research. Some found these tools unsatisfactory, while others have adopted them into their work. Participants expressed concern about genAI compared to traditional ML. They valued ML’s transparency and trust in its statistical basis. Ethical worries included automation bias, potential deskilling, and representational harm. Recommendations were made for developers, researchers, and policymakers.
economics
AI based insights:
The research explores how economic systems are deeply connected to biological and social systems. It reveals a “panarchy” concept. This means economic ecosystems are embedded within larger systems. These systems have feedback loops and interdependence. The study highlights that economic competition often leads to negative outcomes like fragility and ecological damage. It’s not just isolated failures, but systemic dynamics at play. Researchers found a recurring theme of “mutualism,” where everyone benefits, suggesting sustainable practices are key. The literature supports the idea of “commensalism” too, where one benefits without harming the other. The analysis shows that economies are complex adaptive systems. This framework emphasizes understanding interactions for resilience.
The research explores how economic systems are deeply connected to biological and social systems. It reveals a “panarchy” concept. This means economic ecosystems are embedded within larger systems. These systems have feedback loops and interdependence. The study highlights that economic competition often leads to negative outcomes like fragility and ecological damage. It’s not just isolated failures, but systemic dynamics at play. Researchers found a recurring theme of “mutualism,” where everyone benefits, suggesting sustainable practices are key. The literature supports the idea of “commensalism” too, where one benefits without harming the other. The analysis shows that economies are complex adaptive systems. This framework emphasizes understanding interactions for resilience.
AI based insights:
The paper argues that optimizing decisions, while efficient, can cause harm if not carefully considered. It begins by recognizing that optimization often overlooks stakeholders and their values. These stakeholders, like individuals or groups affected by a system, may hold conflicting values – a situation called “value conflicts.” The document stresses that simply reducing bias isn’t enough. Instead, it advocates for “value-sensitive design,” which means building AI systems with ethical considerations and diverse stakeholder values from the start. This involves understanding the interconnectedness of various actors and their potential impacts. The authors propose a framework combining systems thinking with economic concepts like externalities. Systems thinking helps analyze complex relationships, while externalities quantify costs or benefits to third parties. This approach identifies who was affected and how, and when to incorporate these considerations into the optimization process. It addresses issues like ignorance, errors, and prioritizing short-term gains. Ultimately, the paper suggests a more responsible approach to optimization, acknowledging that efficiency shouldn’t come at the expense of fairness and well-being. It’s about building systems that are accountable and truly serve everyone involved.
The paper argues that optimizing decisions, while efficient, can cause harm if not carefully considered. It begins by recognizing that optimization often overlooks stakeholders and their values. These stakeholders, like individuals or groups affected by a system, may hold conflicting values – a situation called “value conflicts.” The document stresses that simply reducing bias isn’t enough. Instead, it advocates for “value-sensitive design,” which means building AI systems with ethical considerations and diverse stakeholder values from the start. This involves understanding the interconnectedness of various actors and their potential impacts. The authors propose a framework combining systems thinking with economic concepts like externalities. Systems thinking helps analyze complex relationships, while externalities quantify costs or benefits to third parties. This approach identifies who was affected and how, and when to incorporate these considerations into the optimization process. It addresses issues like ignorance, errors, and prioritizing short-term gains. Ultimately, the paper suggests a more responsible approach to optimization, acknowledging that efficiency shouldn’t come at the expense of fairness and well-being. It’s about building systems that are accountable and truly serve everyone involved.
neural networks
AI based insights:
Researchers are exploring a new way to model composite materials. They use neural networks that understand geometry, not just numbers. This approach focuses on “cross-ply laminates”—layers of materials at angles. These networks utilize Riemannian neural networks, a sophisticated technique. These networks operate on curved spaces, capturing material relationships. They account for material variability, a key challenge in composites. The framework uses SPD matrices to represent material stiffness. This leads to more accurate predictions, especially with induction heating.
Researchers are exploring a new way to model composite materials. They use neural networks that understand geometry, not just numbers. This approach focuses on “cross-ply laminates”—layers of materials at angles. These networks utilize Riemannian neural networks, a sophisticated technique. These networks operate on curved spaces, capturing material relationships. They account for material variability, a key challenge in composites. The framework uses SPD matrices to represent material stiffness. This leads to more accurate predictions, especially with induction heating.
AI based insights:
Recent advancements in time series forecasting are focused on building more accurate models. Researchers are using tools like transformers and graph neural networks. These models combine with techniques like decomposition for better results. Ensemble methods combine multiple models to improve overall performance. Decomposition techniques break down time series for increased accuracy. Graph Neural Networks model relationships between time series data. Transformers capture long-range dependencies within the data. Integrating external factors, like supply chain data, improves predictions. The Forecast-Then-Optimize (FTO) framework refines forecasts through optimization.
Recent advancements in time series forecasting are focused on building more accurate models. Researchers are using tools like transformers and graph neural networks. These models combine with techniques like decomposition for better results. Ensemble methods combine multiple models to improve overall performance. Decomposition techniques break down time series for increased accuracy. Graph Neural Networks model relationships between time series data. Transformers capture long-range dependencies within the data. Integrating external factors, like supply chain data, improves predictions. The Forecast-Then-Optimize (FTO) framework refines forecasts through optimization.
Note: Since we did not find tons of papers matching your selected interests we've included some additional topics that are popular. If you do not see your interests often try to add new ones or also include less specifics. Also be aware that if the topics is not represented in arxiv we wont be able to recommed it.
quantum computing
AI based insights:
The research tackles the challenges of running complex quantum computations. Near-term quantum computers face limitations like qubit count and errors. These issues hinder the efficient execution of large quantum circuits. Researchers developed a simulation tool for distributed quantum job scheduling. This tool uses reinforcement learning to optimize job allocation. It considers hardware limitations and incorporates error mitigation techniques. The simulation models circuit decomposition across multiple quantum processors. The goal is to improve computational throughput and reduce communication costs. Ultimately, the study demonstrates how parallel scheduling enhances quantum workflows.
The research tackles the challenges of running complex quantum computations. Near-term quantum computers face limitations like qubit count and errors. These issues hinder the efficient execution of large quantum circuits. Researchers developed a simulation tool for distributed quantum job scheduling. This tool uses reinforcement learning to optimize job allocation. It considers hardware limitations and incorporates error mitigation techniques. The simulation models circuit decomposition across multiple quantum processors. The goal is to improve computational throughput and reduce communication costs. Ultimately, the study demonstrates how parallel scheduling enhances quantum workflows.
AI based insights:
Scientists are building powerful computers using “qutrits.” Qutrits hold more information than a regular 0 or 1. They’re using symmetry protection to make simulations more accurate. This creates detailed models of the universe, like particle interactions. Gray code encoding improves accuracy and efficiency in these simulations. Researchers are focusing on increasing qutrit fidelity and scalability. Symmetry protection reduces errors and drift in the simulations. This is particularly important for complex theories like lattice gauge theories. The work details a plan for future qutrit hardware and code design.
Scientists are building powerful computers using “qutrits.” Qutrits hold more information than a regular 0 or 1. They’re using symmetry protection to make simulations more accurate. This creates detailed models of the universe, like particle interactions. Gray code encoding improves accuracy and efficiency in these simulations. Researchers are focusing on increasing qutrit fidelity and scalability. Symmetry protection reduces errors and drift in the simulations. This is particularly important for complex theories like lattice gauge theories. The work details a plan for future qutrit hardware and code design.
AI and society
AI based insights:
A study investigated public and expert opinions on AI’s potential to have feelings. Researchers surveyed 582 AI experts and 838 US participants. They asked about the likelihood of AI developing subjective experience. In 2024, the median AI researcher estimated a 1% chance, while the public guessed 5%. By 2034, estimates were 25% and 30%, respectively, and by 2100, 70% and 60%. The public believed AI subjective experience was less likely than researchers. Both groups agreed on the need for multidisciplinary expertise. Ultimately, safeguards were seen as necessary, though opinions on rights and protections were divided.
A study investigated public and expert opinions on AI’s potential to have feelings. Researchers surveyed 582 AI experts and 838 US participants. They asked about the likelihood of AI developing subjective experience. In 2024, the median AI researcher estimated a 1% chance, while the public guessed 5%. By 2034, estimates were 25% and 30%, respectively, and by 2100, 70% and 60%. The public believed AI subjective experience was less likely than researchers. Both groups agreed on the need for multidisciplinary expertise. Ultimately, safeguards were seen as necessary, though opinions on rights and protections were divided.