Articles

How Much Should We Trust Artificial Intelligence

Considerable buzz surrounds artificial intelligence, and, indeed, AI is all around us. As with any software-based technology, it is also prone to vulnerabilities. Here, the author examines how we determine whether AI is sufficiently reliable to do its job and how much we should trust its outcomes.

Virtual Panel: Data Science, ML, DL, AI and the Enterprise Developer

InfoQ caught up with experts in the field to demystify the different topics surrounding AI, and how enterprise developers can leverage them today and thereby render their solutions more intelligently.

2017 State of Testing Report

The State of Testing 2017 report provides insights into the adoption of test techniques, practices, and test automation, and the challenges that testers are facing. This is fourth time that this survey has been done. InfoQ held an interview with the organizers of the State of Testing survey.

There's No AI (Artificial Intelligence) without IA (Information Architecture)

Artificial intelligence (AI) is increasingly hyped by everyone, from well-funded startups to well-known software brands. In this article the author describes the need for high-quality, structured data before AI technologies can be of use to organizations and their customers.

Article Series: An Introduction to Machine Learning for Software Developers

Get an introduction to some powerful but generally applicable techniques in machine learning for software developers. These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.

Book Review: Andrew McAfee and Erik Brynjolfsson's "The Second Machine Age"

Andrew McAffee and Erik Brynjolfsson begin their book The Second Machine Age with a simple question: what innovation has had the greatest impact on human history?

Article Series: Getting a Handle on Data Science as a Software Developer

Software developers and managers are realizing that they need data science among their skills, to be able to tackle pressing problems. In this series, field experts provide guidance to help us navigate among the available data analysis options. They explore ways of understanding where data science is needed and where it’s not, and how to turn it into an asset.

Solving Business Problems with Data Science

Enterprises are increasingly realising that many of their most pressing business problems could be tackled with the application of a little data science. This article, the first in a series, looks at the foundations of a successful business-orientated data science project.

The InfoQ Podcast: Cathy O'Neil on Pernicious Machine Learning Algorithms and How to Audit Them

In this week's podcast InfoQ’s editor-in-chief Charles Humble talks to Data Scientist Cathy O’Neil.  Topics discussed include her book “Weapons of Math Destruction,” predictive policing models, the teacher value added model, approaches to auditing algorithms and whether government regulation of the field is needed.

Book Review: Cathy O’Neil’s Weapons of Math Destruction

“Big Data has plenty of evangelists, but I’m not one of them,” writes Cathy O’Neil, a blogger (mathsbabe.org) and former quantitative analyst at the hedge fund DE Shaw who became sufficiently disillusioned with her hedge fund modelling that she joined the Occupy movement.

Will AI Surpass Human Intelligence? Interview with Prof. Jürgen Schmidhuber on Deep Learning