Quantum reinforcement learning protocols implemented in superconducting circuits.
problem Improving quantum devices through learning processes.
method Implementation of quantum reinforcement learning protocols using superconducting circuits.
result Feasibility analysis of quantum reinforcement learning protocols in superconducting circuits.
Machine learning improves quantum experiment design.
problem Designing complex quantum experiments to produce high-dimensional entangled states.
method Projective simulation model, machine learning to create new experiments.
result Machine learning system autonomously discovers experimental techniques.
Econophysics has developed as a research field that applies the formalism of Statistical Mechanics and Quantum Mechanics to address Economics and Finance problems. The branch of Econophysics that applies of Quantum Theory to Economics and Finance is called Quantum Econophysics. In Finance, Quantum Econophysics' contrib…
AI learns market manipulation through simulation, suggesting regulation.
problem Regulating AI to prevent market manipulation.
method Used a genetic algorithm in an artificial market simulation.
result AI discovered market manipulation as an optimal strategy.
AI in finance uses quantum logic for better decision-making.
problem Improving financial decision-making models using AI.
method Application of quantum logic in machine learning techniques.
result Advantages of quantum-inspired neural networks in finance.
Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.
Quantum algorithms speed up reinforcement learning policies in large state-action spaces.
problem Limitations of quantum access in training reinforcement learning policies.
method Designing quantum algorithms to train reinforcement learning policies.
result Quantum algorithms offer full quadratic speed-ups in sample complexity for well-behaved policies.
Researchers measure distances between quantum states to speed up machine learning.
problem Calculating distances between quantum states for machine learning is complex.
method Three-step method using many-particle interference to measure Hilbert-Schmidt distance.
result The method reduces complexity in calculating Euclidean distances between quantum states.
Survey on AI math foundations, focusing on neural networks.
problem Lack of rigorous mathematical foundation for AI.
method Survey and discussion of theoretical directions in AI.
result Discussion of open problems in AI math.
AI makes better decisions, leading to more rational markets.
problem How AI affects decision-making in markets.
method Investigates how AI makes decisions in markets compared to humans.
result AI decisions are more consistent and rational, making markets more rational.
New AI approach improves quantum device calibration by leveraging prior scientific discoveries.
problem Lack of abundant data in scientific disciplines hinders model generalizability.
method Introduces a new machine learning approach that combines prior scientific knowledge with data.
result Accuracy in predicting quantum device energy spectrum surpasses current state-of-the-art by over 20%.
Quantum-inspired model generates samples from data efficiently.
problem Unsupervised generative modeling from data.
method Matrix product states for efficient learning and direct sampling.
result Efficient direct sampling approach for generative tasks.
The paper shows how uncertainty quantification improves counterfactual explainability in AI.
problem Lack of foundational concepts in transparency research.
method Integrates uncertainty quantification into counterfactual explainability.
result Demonstrates competitive performance of an uncertainty-based explainer.
This paper reviews deep learning's latest progress and applications.
problem Challenges in deep learning models and applications.
method Analysis of existing models and new emerging models.
result Summarizes deep learning's applications in various AI fields.
Study on AI-driven modeling for high burnup accident-tolerant fuels in SMRs.
problem Design and optimization of high burnup accident-tolerant fuels for SMRs.
method Artificial intelligence and multi-scale modeling (neutronics, thermal hydraulics, fuel performance).
result Demonstrated the effectiveness of AI in modeling and optimizing SMR fuels.
Quantum methods speed up inference in Markov logic networks.
problem Efficient inference in Markov logic networks (MLNs).
method Analysis of graph structures and application of quantum protocols to Gibbs sampling.
result Exponential speedup in approximate probabilistic inference using quantum methods.
Machine learning speeds up quantum chemical calculations of excited states.
problem Accurate quantum chemical calculations of excited states are computationally expensive.
method Employing machine learning to speed up and advance excited-state simulations in various fields.
result Machine learning techniques can significantly reduce computational time for excited-state simulations.
The paper proposes a test for trading intelligence similar to the Turing Test.
problem Determining if trading programs exhibit intelligent behavior.
method Develops a test based on the Turing Test for financial trading programs.
result Introduces a method to assess trading intelligence in financial markets.
Mathematical framework using Riemannian geometry for intelligence and consciousness.
problem Lack of a unified mathematical framework for intelligence and consciousness.
method Conceptualizes intelligence as tokens in a high-dimensional space, using Riemannian geometry to describe structure and dynamics.
result Integrates geometric concepts to offer a unified framework for intelligence and consciousness.
Quantum circuits explained using Shapley values for better understanding.
problem Improving the explainability of quantum machine learning circuits.
method Applying Shapley values to quantify gate importance in quantum circuits.
result Quantum circuits can be explained by their gate importance, enhancing understanding and interpretability.
AI improves credit rating predictions over traditional methods.
problem Improving credit rating predictions for global corporate entities.
method Applying deep learning techniques, specifically neural networks with categorical embeddings, to a large dataset of corporate obligations.
result Deep learning models achieve adequate accuracy in predicting different credit rating classes.
Financial services need explainable AI for fair lending.
problem Challenges in credit decisioning due to compliance and ethical considerations.
method Use of explainable AI models to address fairness and ethics in lending.
result Explainable AI models improve fairness and ethics in lending decisions.
AI enhances cancer diagnostics using spectroscopy.
problem Early and accurate cancer diagnosis.
method Combining AI with spectroscopy-based techniques.
result AI improves cancer diagnostics speed and safety.
Paper reviews algebraic research in machine learning theory.
problem Understanding phase transitions in machine learning models.
method Algebraic approaches in statistical mechanics.
result Algebraic methods are essential for analyzing machine learning models with singularities.
Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive lis…
Artificial neural networks predict quantum entanglement types.
problem Predicting entanglement types of quantum states.
method Supervised learning and deep neural networks on algebraic varieties.
result Trained neural networks can classify entanglement types for up to 5 binary qubits and 3 qutrits.
The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and prese…
AI solutions matched prosthetic control goals in a challenge.
problem Matching time-varying velocity vectors in musculoskeletal models.
method Deep reinforcement learning approaches with various modifications.
result Many solutions used similar techniques but implemented unique modifications.
Chaos and nonlinear economic dynamics are addressed for a quantum coupled map lattice model of an artificial economy, with quantized supply and demand equilibrium conditions. The measure theoretic properties and the patterns that emerge in both the economic business volume dynamics' diagrams as well as in the quantum m…
Paper aims to use AI for detecting financial crimes, focusing on money laundering.
problem Financial institutions need better technologies to detect and predict financial crimes.
method Study recent works, develop a novel model for money laundering detection.
result Demonstrates a model for detecting money laundering cases with minimal human intervention.
Machine learning simplifies finance, but faces challenges.
problem Adopting machine learning in finance.
method Analyzing challenges in financial services adoption.
result Challenges in finance adoption of ML.
The paper discusses safety assessment for AI systems, focusing on machine learning models.
problem Safety assessment of AI systems, especially machine learning models, in safety-related applications.
method Analyzed AI models as statistical models and proposed a new budget allocation for AI safety.
result Safety assessment of AI systems requires a new approach focusing on the model used, not just the system.
AIBC uses AI and blockchain for efficient resource sharing.
problem Efficient resource sharing in large-scale decentralized systems.
method Two-consensus scheme with DABFT and DPoEV.
result Predictive deep learning for optimal BFT algorithm selection.
Generative AI decodes quantum codes without labeled data.
problem Efficient decoding of quantum error-correcting codes.
method Generative Transformers learn logical operators from unsupervised syndromes.
result Significantly better decoding accuracy than traditional methods.
Symmetry principles help in creating better AI representations.
problem Creating efficient and generalizable AI representations.
method Using symmetry transformations to guide representation learning.
result Symmetry principles improve data efficiency and generalizability in AI.
Explains AI basics and its neural origins.
problem Understanding AI and neural origins.
method Overview of AI and its biological roots.
result Highlights the evolution of AI models.
ASLA learns atomic structures using neural networks and reinforcement learning.
problem Designing materials and drugs with desired properties.
method Atomistic structure learning algorithm (ASLA) using a convolutional neural network and reinforcement learning.
result ASLA can predict optimal structural arrangements of atoms for various target properties.
AI improves healthcare diagnostics and predictions.
problem Data heterogeneity and model limitations in AI for health.
method Review of AI applications in health informatics.
result AI enhances disease diagnosis and prediction.
Paper tackles AI risks by customizing metrics and models.
problem AI risks are multidimensional and immaturely managed.
method Decomposes AI risks into data protection, fairness, etc., and develops metrics and models.
result Customized metrics and models reduce AI risk uncertainty.
Machine learning benefits from causal inference concepts.
problem Machine learning's hard problems are related to causality.
method Introduces key concepts linking machine learning and causal inference.
result Machine learning and AI's challenges are intertwined with causality.
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelli…
AI enhances quantitative investment for better returns and risk control.
problem Achieving stable returns through AI in quantitative investment.
method Application of AI technology in quantitative investment strategies.
result AI improves investment performance and risk management.
Study finds no significant difference in neural network weights with quantum random numbers.
problem Effects of biased quantum random numbers on neural network initialization.
method Empirical study using quantum hardware and classical pseudo-random numbers.
result No statistically significant difference found between quantum random numbers and other types.
XAI improves transparency of machine learning algorithms.
problem Complex machine learning algorithms lack understandable explanations.
method Definition and classification of explainability, review of existing methods.
result Current explanatory methods for deep neural networks are insufficient.
The problem of replicating the flexibility of human common-sense reasoning has captured the imagination of computer scientists since the early days of Alan Turing's foundational work on computation and the philosophy of artificial intelligence. In the intervening years, the idea of cognition as computation has emerged …
The ABC algorithm's social interactions are characterized through a novel interaction network.
problem Understanding complex behavior in swarm intelligence systems.
method Definition and analysis of the interaction network for the ABC algorithm.
result ABC exhibits dynamic information flow but lacks continuous coordination.
Chess engines Stockfish and LCZero differ in their approach to solving endgame puzzles.
problem Comparing machine and human chess problem-solving abilities.
method Used Plaskett's Puzzle to compare Stockfish and LCZero's performance.
result Stockfish outperforms LCZero on the puzzle.
Survey of algorithmic assurances for trust in AI agents.
problem Ensuring trust in AI agents designed for human-autonomy relationships.
method Formal definition and classification of algorithmic assurances, synthesis of research across AI communities.
result Algorithmic assurances fall along a spectrum impacting agent core functionality.