Adversarial examples are slight manipulations that cause machine learning algorithms to misclassify images while going unnoticed to the human eye.
If you think someone without your desktop login won’t be able to access your computer’s files, think again. Anyone with mediocre IT skills can take your your hard disk, plug it as a secondary drive...
Large language models suffer from fundamental problems, such as failing at math and reasoning. Augmented language models address some of these problems.
Data augmentation improves machine learning performance by generating new training examples from existing data.
Recurrent neural networks enable computers to process text, videos, time series, and other sequential data.
OpenAI o1 and o3 are very effective at math, coding, and reasoning tasks. But they are not the only models that can reason.
One of the most basic practices every cybersecurity guide will recommend is not to click on links and attachments contained in emails coming from unknown sources, and to think twice even if they come from...
Semi-supervised learning helps you solve classification problems when you don't have labeled data to train your machine learning model.
Explainable AI helps peer into the black box of neural networks and deep learning algorithms, an important requirement for using automation in many domains.
Convolutional neural networks (CNN), or ConvNets, have become the cornerstone of artificial intelligence (AI) in recent years. Their capabilities and limits are an interesting study of where AI stands today.