Communications of the ACM

Teaching LLMs to Give Better Answers

As a result, researchers are exploring ways to embed better logic into AI. The goal isn’t so much to make LLMs smarter; it’s ...
Tech Times

ICML 2026 Opens in Seoul Next Week: Record 23,918 Submissions Signal AI Agent Safety Era

ICML 2026 opens in Seoul on July 6 with a record 23,918 submissions — more than double last year — and a research program ...
Chicago Sun-Times

Housing program to give eligible Chicagoans up to $70,000 toward down payment

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The New York Times

Coding After Coders: The End of Computer Programming as We Know It

In the era of A.I. agents, many Silicon Valley programmers are now barely programming. Instead, what they’re doing is deeply, deeply weird. Credit...Illustration by Pablo Delcan and Danielle Del Plato ...
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 ...
www.cs.cmu.edu

Probabilistic Programming Languages

Probabilistic programming is an approach to computing based on the idea that probabilistic models can be naturally and efficiently represented as executable code. This idea has enabled researchers to ...
eLife

Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models

This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain ...
Forbes

The Rise Of The AI Inference Economy

Forbes contributors publish independent expert analyses and insights. I write about the economics of AI. When OpenAI’s ChatGPT first exploded onto the scene in late 2022, it sparked a global obsession ...
mccormick.northwestern.edu

A Scalable Probabilistic Computer

Ising machines demonstrate significant potential to tackle computationally complex challenges, including combinatorial optimization problems related to logistics, manufacturing, finance, and AI. The ...
IEEE

Improving Post-Training Quantization via Probabilistic Programming

Abstract: Post-training quantization (PTQ) is an effective solution for deploying deep neural networks on edge devices with limited resources. PTQ is especially attractive because it does not require ...
The Motley Fool

What Is AI Inference?

AI inference uses trained data to enable models to make deductions and decisions. Effective AI inference results in quicker and more accurate model responses. Evaluating AI inference focuses on speed, ...