CNET

How Smart Do We Want AI to Be? World Models May Understand Things Better Than We Do

Step aside, LLMs. The next big step for AI is learning, reconstructing and simulating the dynamics of the real world.

How do AI coding agents work? We look under the hood.

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...
The Marshall Project

How We Cleaned Up and Clarified Federal Data on Deaths in Custody

Data collected under the Death in Custody Reporting Act has some serious problems. Here’s how we fixed some of them.
Data Center Frontier

How Geothermal Energy Is Gaining Ground in AI Data Center Power Strategies

As AI-driven power demand accelerates, geothermal energy is quietly moving from conceptual promise toward early execution: ...
ISACA

ISACA Is Now the Official CAICO for the US DoW’s CMMC Program

ISACA is the new CAICO, trusted by the Department of War to serve as the authority responsible for CMMC training and ...

Neel Somani on How Privacy-Preserving Machine Learning Is Changing the Digital Landscape

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...

Stop using ChatGPT for everything: I use these AI models for research, coding, and more (and which I avoid)

Obsessing over model version matters less than workflow.
Frontiers

AI for Decoding the Brain: Advances in Neural Signal Interpretation and Cognitive State Modelling

Recent advances in artificial intelligence (AI) and machine learning (ML) have transformed our ability to decode complex ...
IEEE

Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey

Abstract: Due to the greatly improved capabilities of devices, massive data, and increasing concern about data privacy, Federated Learning (FL) has been increasingly considered for applications to ...
IEEE

Data-Driven Innovation: A Literature Review, Conceptual Framework, and Research Agenda

Abstract: We systematically review the data-driven innovation (DDI) literature spanning 2009–2022, analyzing key studies, assessing the current state of DDI research in information systems and ...