InfoQ

MIT's Recursive Language Models Improve Performance on Long-Context Tasks

Researchers at MIT's CSAIL published a design for Recursive Language Models (RLM), a technique for improving LLM performance ...
Euractiv

AI companies are not being as transparent as the EU wants

Under the EU’s AI Act, companies that build large foundation models – such as those underpinning popular chatbots like ...
Scientific American

The next AI revolution could start with world models

Why today’s AI systems struggle with consistency and how emerging world models aim to give machines a steady grasp of space ...

The 5 best label makers that will make you feel like a professional organizer

The best label makers upgrade your organization efforts. I tested 13 digital and Bluetooth models for functionality, ease of ...
Developer Tech

The systems behind how Spotify runs recommendations

As Spotify’s recommendation systems grew more complex, experimentation and personalisation started pulling in opposite ...

Blue Yonder transforms retail supply chains with new AI-driven innovations for unified planning and execution

Retail supply chains are facing immense pressure today, as rising complexity and siloed operations are leading to shrinking margins and unhappy customers. To ...
National Academies of Sciences%2c Engineering%2c and Medicine

Making Machine Learning Safer in High-Stakes Settings

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
IEEE

The Evolution of AI: From Classical Machine Learning to Modern Large Language Models

Abstract: This paper provides a comprehensive review of the evolution of artificial intelligence from early symbolic, rule-based systems to modern large language models (LLMs) and retrieval-augmented ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

Introduction: Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as ...
Scientific Research Publishing

Explainable Machine Learning in Risk Management: Balancing Accuracy and Interpretability ()

1 School of Taxation and Public Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, China. 2 School of Business, Computing and Social Sciences, University of Gloucestershire ...
Scientific Research Publishing

Molnar, C. (2022) Interpretable Machine Learning. A Guide for Making Black Box Models Explainable, Self-Published.

ABSTRACT: We consider various tasks of recognizing properties of DRSs (Decision Rule Systems) in this paper. As solution algorithms, DDTs (Deterministic Decision Trees) and NDTs (Nondeterministic ...