Morning Overview on MSN

Forecasters are watching a system off Florida that could become the season’s next tropical storm

Residents along Florida’s Gulf Coast face rising rain chances and potential coastal hazards this weekend as the National ...
Observer

The World Cup Is Proving the Business Value of A.I. Prediction Models

Bawden explores how A.I.-powered prediction models have evolved from simple tournament forecasts into trusted infrastructure ...

Statistical method broadens forecasts by modeling uncertainty beyond average outcomes

When it comes to statistics, we usually expect to be informed about what happens "on average." But sometimes the key ...
RMD Open

Development and assessment of an assisted diagnosis model using machine learning for identifying adult-onset Still’s disease in fever of unknown origin: a retrospective …

Background Adult-onset Still’s disease (AOSD) is a systemic autoinflammatory disorder lacking a gold-standard diagnostic ...
IEEE

Probabilistic Graphical Models Meet Deep Learning for Semantic Segmentation: Mathematical connections and recent developments

Abstract: Semantic segmentation, also known as spatially dense image classification, plays a crucial role in image analysis, bridging the fields of image processing and machine learning. It has wide ...
The Conversation

Probability underlies much of the modern world – an engineering professor explains how it actually works

Probability underpins AI, cryptography and statistics. However, as the philosopher Bertrand Russell said, “Probability is the most important concept in modern science, especially as nobody has the ...
The New York Times

Are Models Getting Even Skinnier?

The era of size inclusivity is seemingly over. Our critic traces the shift and hopes designers might learn from it. By Vanessa Friedman I know models have always been skinny, but it seems to me they ...
Futurism

Large Language Models Will Never Be Intelligent, Expert Says

Are tech companies on the verge of creating thinking machines with their tremendous AI models, as top executives claim they are? Not according to one expert. We humans tend to associate language with ...
GitHub

A Python library that supports the constrained analysis of probabilistic graphical models

Conin supports constrained inference and learning for hidden Markov models, Bayesian networks, dynamic Bayesian networks and Markov networks. Conin interfaces with the pgmpy python library for the ...
Hacker

Understanding Bayesian Networks: A Graphical Model for Uncertainty

Bayesian Network (BN) [15] is a probabilistic graphical model that represents a set of random variables and their conditional probabilities via a directed acyclic graph (DAG). In these graphical ...
Frontiers

A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...