Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.

By 2030, it’s expected that the market for streaming data will eclipse $73 billion, growing nearly 20% each year until then. More impressively, the machine learning market—which brought in $15 ...

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine ...

For example, by starting with an ImageNet classifier, researchers in Bangalore, India, used transfer learning to train a model to locate kidneys in ultrasound images using only 45 training examples.

Machine learning is becoming an important tool in many industries and fields of science. But ML research and product development present several challenges that, if not addressed, can steer your ...

Level 2 is continual learning: ML systems that incorporate new data and update in real-time, for which she defines real-time to be in the order of minutes.

Machine learning-based models need to be complemented by real-time data from third-party sources, as more businesses are discovering. Some examples of real-time data include: ...

Imagine having a super-powered lens that uncovers hidden secrets of ultra-thin materials used in our gadgets. Research led by ...

Data poisoning is a type of adversarial ML attack that maliciously tampers with datasets to mislead or confuse the model. The goal is to make it respond inaccurately or behave in unintended ways.

Almost all machine learning models function via a learning rule that changes the so-called weights, also known as parameters, of the program as the program is fed examples of data, and, possibly ...

Before data scientists can run machine learning models to tease out insights, they’re first going to need to transform the data—reformatting it or perhaps correcting it—so it’s in a ...