Everything you need to know about symbolic artificial intelligence, the branch of AI that dominated for five decades.
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.
By Mona Eslamijam
Image credit: 123RF
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
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.
Membership inference attacks can detect examples used to train machine learning models even after those examples have been discarded.
In the past, unfulfilled promises in artificial intelligence caused a decline in interest and funding, also known as AI winter. The question is, will it happen again?
In a new NeurIPS paper, Geoffrey Hinton introduced the “forward-forward algorithm,” a new learning algorithm for artificial neural networks inspired by the brain.
The two main types of machine learning categories are supervised and unsupervised learning. In this post, we examine their key features and differences.
Dimensionality reduction slashes the costs of machine learning and sometimes makes it possible to solve complicated problems with simpler models.