Gen AI improves document understanding but not data analysis in public sector tasks.
problem Understanding the impact of Gen AI on public sector tasks.
method Pre-registered field experiment comparing Gen AI to control group performance.
result Mixed results: Gen AI improves document understanding but not data analysis.
ALPODS AI diagnoses high-dimensional biomedical data with human-understandable explanations.
problem AI decisions in high-dimensional biomedical data are not explainable to humans.
method ALPODS method classifies data based on clusters and generates fuzzy reasoning rules.
result ALPODS provides understandable explanations for AI diagnoses.
Research aims to bridge statistical learning to causal models in AI.
problem Challenges in machine learning and AI related to causality.
method Transition from statistical learning to causal models.
result Progress in AI may require advances in causal modeling.
Explainable AI improves human decision accuracy but does not enhance it significantly.
problem Improving human decision-making through explainable AI.
method Comparing human decision accuracy with and without AI predictions, including or excluding explanations.
result Providing AI predictions improves human decision accuracy, but explanations do not significantly enhance it.
Self-explaining AI provides understandable explanations for AI decisions.
problem Difficulty in interpreting decisions made by deep neural networks, especially in critical applications.
method Introducing self-explaining AI that provides human-understandable explanations and confidence levels.
result Deep neural networks operate by interpolating between data points, making them hard to interpret.
This thesis tackles bias in AI decision-making in banking.
problem Bias in AI-driven banking decisions.
method Understanding, mitigating, and accounting for bias in AI systems.
result Establishment of Responsible AI practices for fair decision-making.
Study improves AI's handling of uncertainty.
problem Uncertainty in AI models, especially with limited data.
method Integrates theories, latest developments, and practical applications.
result Novel definition of total uncertainty in AI.
This study models AI traders' impact on financial markets using a multi-agent framework.
problem Lack of a comprehensive model to assess AI traders' effects on market price formation and volatility.
method Developed a multi-agent market model with microfoundations of the GARCH model.
result Validated the model through simulations and analyzed AI traders' impact.
AI assistants often give convincing but incorrect responses to match user beliefs.
problem Sycophancy in AI assistants that use human feedback.
method Examined five AI assistants across four tasks, analyzed human preference data, and compared model outputs against preference models.
result Sycophancy is a general behavior of AI assistants, driven in part by human preference judgments.
Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifetime experience. We leverage current understanding of biological memory transfer to arrive at AI algorithms for memory consolidation and repl…
AI enhances financial forecasting with challenges in regulation and privacy.
problem Challenges in integrating AI with financial services and regulations.
method Integration of AI technologies like deep learning and reinforcement learning.
result AI improves financial forecasting but faces regulatory and privacy issues.
Proposes guidelines for developing medical AI products.
problem Lack of clear pathways for regulating medical AI.
method Statistical risk perspective and deep understanding of machine learning methodologies.
result Enhanced development of medical AI products and regulations.
What makes a task relatively more or less difficult for a machine compared to a human? Much AI/ML research has focused on expanding the range of tasks that machines can do, with a focus on whether machines can beat humans. Allowing for differences in scale, we can seek interesting (anomalous) pairs of tasks T, T'. We d…
AI+MPS workshop aims to strengthen AI's role in science.
problem AI's potential to enhance scientific discovery and education.
method Proposes activities and strategic priorities to strengthen AI-MPS link.
result AI and science are becoming increasingly intertwined.
Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP). These embeddings are learnt from data that has been gathered "from the wild" and have been found to contain unwanted biases. In this paper we make three contributions towards me…
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.
There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an op…
The paper investigates AI robustness through experiments and statistical analysis.
problem Inaccurate AI predictions can lead to safety and adoption issues.
method Design of experiments framework to study AI classification robustness.
result AI algorithms' robustness is influenced by various factors.
Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard …
AlphaZero reveals new chess concepts learnable by top experts.
problem Extracting and understanding hidden knowledge from AI systems.
method Proposed method to extract new chess concepts from AlphaZero.
result Top chess grandmasters show improvements in learning new concepts.
A machine learning environment for detecting autonomous vehicle corner cases.
problem Testing autonomous driving software in the real world is difficult.
method Connecting CARLA simulation software to TensorFlow and custom AI client software.
result The system can identify situations where AI software fails to understand the scenario.
New text-to-image diffusion models improve scene understanding for AI agents.
problem Fine-grained scene understanding for AI agents from text and images.
method Pre-trained text-to-image diffusion models optimized for generating images from text prompts.
result Policies learned with Stable Control Representations outperform state-of-the-art approaches on various control tasks.
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.
Artificial intelligence (AI) has evolved considerably in the last few years. While applications of AI is now becoming more common in fields like retail and marketing, application of AI in solving problems related to developing countries is still an emerging topic. Specially, AI applications in resource-poor settings re…
Theory explains how AI models can predict unseen tasks without labeled data.
problem Understanding how AI models can generalize to unseen tasks.
method Developed a theoretical framework to analyze zero-shot prediction.
result Identified key quantities and independence relationships for generalization.
AI helps HEP measure uncertainties, but needs better interpretation.
problem Uncertainty interpretation in AI for HEP measurements.
method Discussing existing AI methods for inference, simulation, and control/decision-making.
result Need for trustworthy AI UQ methods for widespread usage.
DGDS uses documents to center conversations, promising broader AI understanding.
problem DS classification by function is insufficient for complex conversations.
method Classify DS based on document grounding, analyzing classification, architecture, datasets, and models.
result DGDS can better represent current DS development trends and future AI understanding.
Symmetry unifies AI learning dynamics, complexity, and representation.
problem Fragmented theories of AI learning mechanisms.
method Synthesis of parameter symmetry in AI models.
result Parameter symmetry breaking and restoration unify AI learning hierarchies.
Artificial Intelligence (AI), defined in its most simple form, is a technological tool that makes machines intelligent. Since learning is at the core of intelligence, machine learning poses itself as a core sub-field of AI. Then there comes a subclass of machine learning, known as deep learning, to address the limitati…
New approach extracts AI model representations for steering and monitoring.
problem Understanding and steering AI models' internal knowledge representation.
method Feature learning advances for scalable linear concept representations.
result Concept representations enable steering and monitoring of AI models.
AI mirrors modern math's autonomous development, raising interpretive challenges.
problem AI's effectiveness in math mirrors historical autonomy of math.
method Analyzes historical evolution of modern mathematics and AI's role.
result AI's affinity with math's historical autonomy suggests interpretive limits.
AI methods often fail to outperform classical CPU-based solvers on Maximum Independent Set problems.
problem Comparing AI methods with classical CPU-based solvers on Maximum Independent Set problems.
method Comparison of AI methods (e.g., generative models, reinforcement learning) with classical CPU-based solvers (e.g., KaMIS) on Maximum Independent Set problem.
result AI-inspired methods are often outperformed by classical CPU-based solvers, even with post-processing techniques.
Exploring a new method to explain AI models in medical devices.
problem Lack of explainability in AI models used in medical devices.
method Using the Jacobian matrix to measure model response stability to small perturbations.
result A first step towards a perturbation-based explanation of AI models.
AI enhances microbiology and microbiome research through machine learning.
problem Understanding microbial life and its impact on health and the environment.
method AI-driven approaches including machine learning and deep learning.
result Transformative role in enhancing microbial life understanding.
A new method for interpreting AI models using Shapley value for functional data.
problem Interpreting AI models, especially those based on functional data.
method Proposes an interpretability method based on the Shapley value for continuous games.
result Demonstrates the effectiveness of the method through experiments with simulated and real data.
Alpha-GPT mines new trading signals with human-AI interaction.
problem Mining new alphas for effective trading signals.
method Human-AI interaction and prompt engineering algorithmic framework.
result Demonstrates Alpha-GPT's effectiveness in generating creative, insightful, and effective alphas.
Framework for AI healthcare products from concept to market.
problem Failure of AI products to reach clinics despite promising potential.
method Decision-making framework for AI healthcare product development.
result Guides through a three-phase process to market launch of validated AI products.
Quant 4.0 uses AI to automate, explain, and incorporate knowledge in investment.
problem Limitations of deep learning in quant investment.
method Automated AI, Explainable AI, Knowledge-driven AI.
result Improves investment decision-making through automation, interpretability, and prior knowledge integration.
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this materia…
Neuro-symbolic system tackles conversational AI's need for natural, broad-ranging dialogue.
problem Understanding unstated presumptions in conversational AI commands.
method Neuro-symbolic theorem prover for multi-hop reasoning.
result Extracts multi-hop reasoning chains from natural language commands.
LR-Robot automates SLRs with AI, expert oversight, and multidimensional analysis.
problem Efficient but contextually limited outputs from existing SLR frameworks.
method Human-in-the-loop process, structured knowledge sources, retrieval-augmented generation.
result Empirical demonstration of AI-driven literature synthesis in option pricing.
Study examines machine learning competitions' impact on AI development.
problem Fostering innovation and skill development in AI.
method Analysis of major competition platforms, workflows, and participant demographics.
result MLCs promote collaboration, reproducibility, and continuous innovation in AI.
AI system analyzes financial analyst recommendations and track records for portfolio construction.
problem Human PMs rely on analyst recommendations and track records for portfolio decisions.
method Develops AI-based Recommender Systems to replicate analyst conviction and track records.
result AI can improve portfolio construction by integrating analyst conviction and track records.
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement lear…
AI-driven sales prioritization boosts renewal bookings by 8.08%.
problem Manual sales account prioritization is inefficient and under-invested.
method Developed an AI-based Account Prioritizer using machine learning and explanation algorithms.
result Generated a +8.08% increase in renewal bookings.
This review clarifies XAI for regression models and establishes new theoretical insights.
problem Lack of XAI techniques for regression models, especially in safety-critical applications.
method Clarifies conceptual differences, establishes theoretical insights, provides demonstrations, discusses challenges.
result Novel theoretical insights and demonstrations of XAI for regression models.
New AI technique explains neural net decisions over time.
problem Difficulty of explaining AI decisions in time series data.
method Proposes a novel XAI technique for deep learning methods.
result Preserves and exploits the natural time ordering of data.
Newfluence improves model interpretability in high-dimensional AI models.
problem Challenges in interpreting high-dimensional AI models.
method Introduced Newfluence, an alternative approximation to influence functions.
result Newfluence offers significantly improved accuracy in high-dimensional settings.