Those misclassifications, known as adversarial examples, have long been seen as a nagging weakness in machine-learning models.

How machine learning “sees” the world Before we get to how adversarial examples work, we must first understand how machine learning algorithms parse images and videos.

1. Demand Prediction Engine: A Technological Leap from "Passive Response" to "Active Anticipation" ...

With appropriate strategy and execution, machine-learning capabilities like the three examples described above have potential to make life easier for the modern-day advertiser.

Machine learning is reshaping seveal industries, influencing social interactions, and even venturing into the realm of ...

How machine learning “sees” the world Before we get to how adversarial examples work, we must first understand how machine learning algorithms parse images and videos.

“Successful machine learning is only as good as the data available, which is why it needs new, updated data to provide the most accurate outputs or predictions for any given need,” said ...

The key distinction between traditional approaches and machine learning is that in machine learning, a model learns from examples rather than being programmed with rules.

Machine learning is just that – learning that is understandable only by machines. One final example: in a game of Tetris in which a robot was required to “not lose” the program pauses “the ...

Discover what black box models are, their applications in finance and investing, and examples of how they drive decision-making without revealing internal processes.