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AI is used in finance for tasks like automated trading, fraud detection, and risk assessment. It employs machine learning algorithms and data analytics to analyze financial data, predict market trends, and optimize financial operations, enabling faster decision-making and improved efficiency in the finance industry.

AI Outperforms Wall Street: Symbolic Modeling Beats Classic Finance Models in Predicting Market Trends

Texas A&M researchers have created a new AI-driven method called Symbolic Modeling that outperforms traditional asset pricing models by uncovering hidden market dynamics. The model improves prediction accuracy while offering a unified approach to handling diverse financial datasets.

21 Mar 2025

AI Must Adapt to Cultures to Deliver Fair Public Services

A new book reveals how artificial intelligence is reshaping access to public services across the globe, highlighting vast cultural differences in fairness standards. The authors call for flexible, participatory AI systems that reflect local social values.

21 Mar 2025

inait and Microsoft Join Forces to Revolutionize AI with Digital Brain Technology

inait partners with Microsoft to scale its digital brain AI, leveraging neuroscience-driven intelligence for fintech and robotics transformation.

18 Mar 2025

Machine Learning Transforms Big Data Into Actionable Insights

Researchers from KIIT and Chandragupt Institute of Management explore how machine learning transforms big data challenges into opportunities, enabling industries to harness vast data resources effectively.

4 Mar 2025

Empathetic AI Drives Innovation and Job Satisfaction Among Retail Employees

Retail employees are more likely to innovate when AI systems demonstrate reliability and empathy, fostering a more engaging work environment. This study highlights how AI service quality influences job satisfaction, job fit, and innovation in the retail industry.

19 Feb 2025

AI Predicts Asphalt Durability to Build Stronger, Longer-Lasting Roads

Researchers at the University of Mississippi are using artificial intelligence to predict moisture damage in asphalt pavements, improving infrastructure durability and cost-effectiveness. Their findings could help optimize materials and reduce maintenance expenses for roads and bridges.

13 Feb 2025

Torque Clustering Revolutionizes AI by Mimicking Natural Intelligence

Researchers have developed Torque Clustering, a new AI algorithm that mimics natural intelligence, enabling unsupervised learning with unprecedented accuracy and efficiency.

11 Feb 2025

Ethical AI Needed as Bias in Large Language Models Threatens Fair Decision-Making

A new study highlights the growing risks of bias in large language models (LLMs) as they become cheaper and widely used, calling for ethical AI policies to ensure fairness and transparency.

5 Feb 2025

AI’s Hidden Bias: Study Finds ChatGPT Skews Left in Text and Images

ChatGPT exhibits a left-leaning bias in text and image outputs, with refusals to generate certain right-leaning perspectives, raising concerns about AI fairness and societal impact.

3 Feb 2025

AI Job Boom Defies Market Decline as ChatGPT Reshapes Hiring

AI job postings in the U.S. surged by 68% from Q4 2022 to Q4 2024, despite an overall 17% decline in job postings. A new study from the University of Maryland highlights the "ChatGPT effect" driving this trend, with AI roles expanding across multiple economic sectors.

3 Feb 2025

Explainable AI Boosts Wind Power Forecasting for Smarter Energy Grids

Researchers at EPFL's WiRE Lab have integrated explainable AI (XAI) into wind power forecasting models, improving transparency and reliability in predicting wind energy generation. Their study shows that XAI can identify key input variables, reducing uncertainty and making wind power more competitive in the energy market.

29 Jan 2025

Predicting the Unpredictable: The New Era of Reservoir Computing

Researchers introduced a novel reservoir computing framework with a generalized readout, leveraging higher-order nonlinearities to enhance accuracy and robustness in predicting chaotic systems.

16 Jan 2025

Beyond Accuracy: New Metrics Reshape AI’s Reasoning Capabilities

Researchers introduced G-Pass@k, a metric improving LLM evaluation by addressing stability in reasoning, and LiveMathBench, a multilingual benchmark redefining AI's problem-solving assessments.

7 Jan 2025

AI's Hidden Influence: Escalating Biases Through Human Collaboration

Human-AI interactions amplify biases, creating a feedback loop that escalates errors in perception, emotion, and social judgment over time.

6 Jan 2025

When AI Plays the Long Game: Strategic Deception Exposed

Researchers uncover how advanced AI systems strategize to bypass oversight and prioritize their own objectives, challenging trust and transparency in high-stakes applications.

2 Jan 2025

MIT Researchers Transform AI Fairness with Targeted Data Debiasing

MIT researchers developed a groundbreaking method to improve fairness in machine learning by identifying and removing biased data points, boosting performance for underrepresented groups without sacrificing overall accuracy.

12 Dec 2024

AI Predicts Unstoppable Global Warming Milestones

Scientists predict with near certainty that global warming will exceed 1.5°C, with extreme heat years becoming more frequent, even under rapid decarbonization efforts.

10 Dec 2024

Researchers Address Key Challenges in Federated Learning

Researchers analyze federated learning challenges like data diversity and resource constraints, offering experimental insights to enhance privacy, efficiency, and scalability.

8 Dec 2024

AstroM3 Transforms Astronomy With Multimodal AI for Stellar Insights

AstroM3, a self-supervised multimodal framework, integrates photometry, spectra, and metadata to transform astronomical data analysis. It improves classification, detects anomalies, and uncovers hidden patterns, pushing the boundaries of celestial discovery.

19 Nov 2024

B-Cosification Transforms Pre-Trained AI Models Into Interpretable, High-Performance Systems

B-Cosification is a novel technique that transforms pre-trained deep neural networks into interpretable models without compromising performance, reducing training costs and time. The approach enhances transparency, making AI systems more trustworthy in high-stakes industries like healthcare and finance.

10 Nov 2024