AI

ImageNet Roulette is part of an art and technology exhibit called Training Humans. Upload a photo and the algorithm will give you a classification. Some of the labels are funny, others are racist.

ImageNet Roulette is meant in part to demonstrate how various kinds of politics propagate through technical systems, often without the creators of those systems even being aware of them.

We did not make the underlying training data responsible for these classifications. We imported the categories and training images from a popular data set called ImageNet, which was created at Princeton and Stanford University and which is a standard benchmark used in image classification and object detection.

I uploaded a photo of me and the label I received was “beard.” Accurate.

We hear the first two terms all the time from Apple. They can be confusing. So, in order to help differentiate between the terms, the TechRepublic has written up a short but helpful tutorial for business people.

The first step is communicating what the definitions are for AI, machine learning (ML), and deep learning. There is some argument that AI, ML, and deep learning are each individual technologies. I view AI/ML/deep learning as successive stages of computer automation and analytics that are built on a common platform.

A traffic planning example makes it clear.

In the Animal-AI Olympics, AI will be given tests originally designed to test animal cognition in a US$10,000 competition.

The Animal-AI Olympics is the creation of a team of researchers at the Leverhulme Centre for the Future of Intelligence in Cambridge, England, along with GoodAI, a Prague-based research institute. The competition is part of a bigger project at the Leverhulme Centre called Kinds of Intelligence, which brings together an interdisciplinary team of animal cognition researchers, computer scientists, and philosophers to consider the differences and similarities between human, animal, and mechanical ways of thinking.