New research on so-called “negation neglect” finds that LLMs in a roughly analogous situation don’t behave that way. They appear to learn from the statistical patterns in their training text more than ...

In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...

Within the next couple of decades, the Key Largo woodrat could go extinct if the Burmese python population continues as is, a ...

A significant shift is under way in artificial intelligence, and it has huge implications for technology companies big and small. For the past half-decade, most of the focus in AI has been on training ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...

While the tech world obsesses over headlines about the $100 million price tag to train GPT-4, the real economic story is happening in inference: the ongoing cost of actually running AI models in ...

The creators of the open source project vLLM have announced that they transitioned the popular tool into a VC-backed startup, Inferact, raising $150 million in seed funding at an $800 million ...

In this work, we develop a new framework for designing experiments that are robust to model misspecification through generalised Bayesian inference. This repository contains the files needed to ...

Abstract: Conventional neural network-based machine learning algorithms often encounter difficulties in data-limited scenarios or where interpretability is critical. Conversely, Bayesian ...

As frontier models move into production, they're running up against major barriers like power caps, inference latency, and rising token-level costs, exposing the limits of traditional scale-first ...