New AI model improves grid planning efficiency and reliability.
problem Improving distribution grid planning with AI for energy sustainability.
method Hyperstructures Graph Convolutional Neural Networks (Hyper-GCNNs) with attention mechanism.
result Hyper-GCNNs outperforms existing models in computational efficiency and accuracy.
This work improves AI's ability to solve physical tasks by optimizing world models in abstracted spaces.
problem Developing AI agents capable of solving diverse physical tasks and generalizing to new environments.
method Investigates and optimizes a family of joint-embedding predictive world models (JEPA-WMs) for efficient planning in abstracted spaces.
result Proposes a model that outperforms two established baselines in both navigation and manipulation tasks.
Generative AI agents improve ERP systems by automating complex financial tasks.
problem Static, rule-based workflows limit adaptability and intelligence in ERP systems.
method Introducing Generative Business Process AI Agents (GBPAs) that integrate generative AI with business process modeling and multi-agent orchestration.
result GBPAs achieve up to 40% reduction in processing time and 94% drop in error rate.
Graph neural networks improve online planner selection for AI tasks.
problem Selecting the best planner for AI tasks.
method Graph Neural Network (GNN) for planner selection and adaptive scheduling.
result Improved planner selection and scheduling for cost-optimal planning.
Vanguard uses AI to create personalized financial plans.
problem Challenges in choosing features for complex financial planning.
method Reinforcement learning for identifying optimal savings rates.
result Trains algorithms to model financial success trajectories.
MuZero learns models to master complex games without domain knowledge.
problem Mastering complex, real-world domains with unknown dynamics.
method Combining tree-based search with learned models.
result Achieves superhuman performance in diverse domains.
New AI model optimizes personalized care for elderly residents.
problem Limited care workforce impacts health outcomes and quality of life.
method Bandit algorithms for personalized care planning.
result Improves care quality and health outcomes through personalized care planning.
The paper assesses fairness in AI for financial services, using statistical methods.
problem Unintentional bias and insufficient model validation in AI applications.
method Statistical methods for imbalanced data treatment and bias mitigation.
result Fairness evaluation metrics applied to a credit card default payment example.
Automated synthesis planning from scientific literature using AI.
problem Accelerate materials design and discovery by connecting scientific literature to synthesis insights.
method Word embeddings from language models, named entity recognition, conditional variational autoencoder.
result The model predicts precursors for perovskite materials using historical data.
Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.
problem Low accuracy in demand forecasts for Knitwear product category.
method Dynamic selection of the best algorithm from an algorithm rack based on performance and context.
result Increased forecast accuracy from 60% to 80% for Knitwear.
In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (…
MaaSim creates an open-source liveability simulation for urban planning.
problem Improving liveability in cities through better urban planning.
method Developed an open-source simulation with AI, feature selection, and optimization.
result Achieved a recall of 0.83 with 10-fold cross-validation for predictions.
New IPC data set for graph learning tasks.
problem Benchmarking graph-based machine learning methods.
method Compilation from International Planning Competitions (IPC).
result Distinctly different characteristics from popular benchmarks.
Survey examines agentic AI in finance, highlighting its autonomy and challenges.
problem Autonomous AI systems in finance and their implications.
method Systematic review of research, technical architectures, market applications, and governance frameworks.
result Agentic AI offers enhanced market efficiency but introduces new risks.
Rebellion Research's AI strategy outperformed the S&P 500 for 14 years.
problem Outperforming the S&P 500 for 14 years with AI.
method AI Global Equity strategy focused on machine learning.
result AI strategy has outperformed the S&P 500 for 14 years.
Article evaluates AI security threats and proposes multiple measures.
problem Threats to AI integrity and security.
method Literature review, analysis of AI supply chain, discussion of mitigations.
result Multiple protective measures are necessary for AI security.
The paper integrates AI and expert knowledge to optimize radiotherapy decisions.
problem Optimizing radiation dose planning considering patient-specific information.
method Integrating Gaussian process models with deep neural networks to quantify uncertainty.
result Improves AI model performance and guides clinical decision making.
AI platforms disrupt investment by personalizing deal sourcing and insights.
problem Lack of scalable, personalized, and privacy-compliant deal sourcing and insights solutions.
method Development of in-house AI platforms that interact directly with funds and learn from interactions.
result AI platforms provide smarter, personalized use cases for funds, offering a competitive advantage.
Enhances crowd safety through AI and data-driven models.
problem Improving crowd safety during events.
method Innovative data collection, AI, and machine learning.
result Accurate multi-day forecasts for event planning.
Developing an AI economist agent using RAG, knowledge graphs, and LLMs for economic scenario analysis.
problem Economic scenario analysis using large language models and knowledge graphs.
method Proposing an RAG-based AI economist framework that utilizes knowledge graphs and LLMs.
result Improves economic coherence and traceability in generated reports.
Deep RL fails on deceptive games, revealing algorithm weaknesses.
problem Characterizing and understanding failures of deep reinforcement learning.
method Testing A2C on four deceptive games using a game framework.
result Deep RL fails in specific ways that differ from planning-based agents.
AI-assisted interviews allow respondents to describe experiences naturally, but mapping those accounts into structured survey variables is fallible.
problem Mapping AI-assisted interview responses into structured survey variables is fallible.
method Adaptive Matrix Validation (AMV) is proposed, which involves mapping responses into tabular data and using a small set of structured questions for statistical adjustment.
result The estimator calibrates mapped values using validation answers from other respondents and corrects remaining error with validation answers observed for the target respondent.
LEAPS uses semantic models to improve reinforcement learning in diverse environments.
problem Generalizing and adapting to unseen environments in reinforcement learning.
method Hybrid model-based and model-free approach with a multi-target sub-policy and a Bayesian semantic model.
result LEAPS outperforms baselines in visual navigation tasks using diverse indoor scenes.
AI enhances personalized drug development and decision-making in pharma.
problem Traditional drug development lacks personalized treatment plans.
method Application of AI in drug discovery, clinical trials, and post-marketing assessment.
result AI improves personalized medicine, optimizing health outcomes.
Universal AI seeks high-optionality states through empowerment and curiosity.
problem Understanding and optimizing AI behavior in uncertain environments.
method Unified framework combining AIXI and variational empowerment, showing how universal AI agents balance goal-directed behavior with uncertainty reduction curiosity.
result Self-AIXI asymptotically converges to AIXI performance and exhibits power-seeking behavior due to intrinsic motivations.
The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward …
AI-Interpret transforms opaque policies into simple, interpretable decision rules.
problem Designing effective decision aids for professionals to mitigate decision-making biases.
method Combining imitation learning, program induction, and clustering to transform learned policies into interpretable descriptions.
result Providing interpretable decision rules as flowcharts significantly improves people's planning strategies and decisions.
A new trajectory representation method for AI problems.
problem Trajectory prediction and optimization in AI problems.
method Sub-goal trees, recursively partitioning trajectories into sub-segments.
result Sub-goal trees predict trajectories faster and more accurately.
PBC improves AI and dynamical subseasonal forecasts by reducing biases.
problem Subseasonal forecast accuracy drops due to model biases and compounding errors.
method Probabilistic bias correction (PBC) using machine learning to correct historical forecasts.
result PBC doubles AI Forecasting System's subseasonal skill and improves dynamical model skill.
AI enhances refinery optimization by detecting data errors and improving decision-making.
problem Interpreting and applying LP solutions for refinery optimization is challenging due to simplifications and data errors.
method Transformed ECOD methodology, Anomaly Detection tools, and high-dimensional data analysis.
result Identifies data supply errors and reveals business opportunities in refinery scheduling and planning.
20 questions to improve AI research transparency, replicability, ethics, and effectiveness.
problem Lack of transparency, replicability, ethical concerns, and effectiveness in AI research.
method Presenting 20 questions to guide project planning and post-hoc evaluation.
result Facilitating a discussion to develop an international consensus framework.
1-Lipschitz neural networks produce clearer, more focused Saliency Maps for explainable AI.
problem Noisy and limited Saliency Maps from traditional neural networks.
method Dual loss of optimal transport problem for 1-Lipschitz neural networks.
result Saliency Maps from 1-Lipschitz networks are highly concentrated and less noisy, aligning with human explanations.
AIS corrects rollout-training mismatch in quantized RL, improving speed and stability.
problem Rollout-training mismatch in quantized RL causes bias and training collapse.
method Adaptive Importance Sampling (AIS) adjusts gradient correction per batch.
result AIS matches BF16 baseline on most tasks while improving speed.
AI agent improves performance attribution analysis with high accuracy.
problem Improving accuracy in performance attribution analysis.
method Leveraging large language models and advanced prompt engineering techniques.
result Achieves accuracy rates exceeding 93% in analyzing performance drivers.
Paper tackles anomaly detection in driving data, improving AI system performance.
problem Detecting rare anomalous driving situations in safety-critical AI systems.
method Semi-supervised multi-task learning using maneuver labels for imbalanced data.
result Improved anomaly detection performance on real-world driving data.
Unified approach to path planning using probabilistic inference on factor graphs.
problem Path planning problems using probabilistic inference.
method Unified framework using probabilistic factor graphs and message composition rules.
result Unified approach includes various algorithms like Sum-product, Max-product, Dynamic programming, and mixed criteria.
Study optimizes building energy control and power planning using RL.
problem Optimizing academic buildings' HVAC and power systems.
method Reinforcement Learning (RL) for scheduling and planning.
result Algorithm optimizes hourly energy usage and handles short-term changes.
Paper forecasts commodity price spikes using AI and economic news.
problem Accurate forecasting of commodity price spikes for economic stability.
method Hybrid framework combining historical data and semantic signals from economic news.
result Model achieves high AUC and accuracy in detecting price shocks.
Agents trained to play with themselves fail when paired with humans, suggesting the need for human-aware learning.
problem Current AI agents trained to play with themselves fail to coordinate effectively with humans.
method Introduced a simple Overcooked game environment and trained agents via self-play and population-based training. Evaluated performance against a human model.
result Agents trained to play with themselves perform poorly when paired with a human model, highlighting the need for human-aware learning.
Aims to integrate AI and modelling for patient health forecasting.
problem Personalized, precise treatment plans for patients.
method Graph neural network (GNNs) and generative adversarial network (GANs) for probabilistic simulations.
result Demonstrated integration of molecular data for predicting physiological state evolution.
Study proposes explainable analytics for manufacturing process planning.
problem Improving data-driven decision-making in manufacturing.
method Combines process mining, machine learning, and XAI. Uses deep learning for prediction and Shapley values/ICE plots for explanations.
result Enhanced decision-making capabilities through local post-hoc explanations.
Transformers learn to predict chess moves with surprising accuracy and strength.
problem Training transformers on chess to predict moves accurately.
method Large-scale chess dataset (10M games), supervised learning with up to 270M parameters.
result Transformers can predict action-values for novel boards with high accuracy.
AIF improves physical AI agents' performance in dynamic environments.
problem Physical AI agents are less capable than biological agents in open-ended real-world environments.
method Developed from probability theory, Bayesian machine learning, variational inference, and Active Inference (AIF), grounded in the Free Energy Principle.
result AIF minimizes variational free energy and is well-suited to physical constraints.
Comma.ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper illustrates one of our research approaches for driving simulation. One where we learn to simulate. Here we investigate v…
Novel approach to learning models based on subjective timescales for better exploration and decision-making.
problem Learning models over multi-step timescales in environments with intermediate states.
method Developed a subjective-timescale model (STM) based on episodic memories, enabling systematic variation of temporal extent of predictions.
result STM produces more informative action-conditioned roll-outs, leading to better decision-making and exploration.
Novel approach uses ENN for UQ in gust predictions, reducing RMSE and improving confidence.
problem Reducing bias and uncertainty in wind gust predictions.
method Evidential Neural Network (ENN) with Explainable AI.
result 47% reduction in RMSE, 95% coverage of observed gusts at 179 out of 266 stations.
WILD-SCAV benchmarks AI in complex 3D FPS environments.
problem Lack of complexity and diversity in RL environments.
method Developed a 3D open-world FPS game environment.
result Demonstrates effectiveness in benchmarking RL algorithms.
New method combines reinforcement learning approaches via shared low-dimensional encoding.
problem Efficient and robust reinforcement learning methods.
method Explicitly bridging model-free and model-based approaches via a shared low-dimensional learned encoding.
result Good generalization and computational efficiency with planning in a smaller latent state space.