The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

Biases in data can be amplified by the training process, leading to distorted — or even unjust — results. And even when a model does work, it’s not always clear why. (Deep learning algorithms are ...

Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

Data training is the process of introducing preprocessed data into a machine learning model to learn from the data. During this phase, the model updates its internal parameters to reduce ...

The process used to build most of the machine-learning models we use today can't tell if they will work in the real world or not—and that’s a problem.

At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don't have much data. Conversely ...

Machine learning deals with software systems capable of changing in response to training data. A prominent style of architecture is known as the neural network , a form of so-called deep learning.

Bar and his team have now successfully automated the entire process of machine-learning training for the surgical platform, from model development to deployment – as well as model improvement.

Amazon claims that Trainium will offer the most teraflops of any machine learning instance in the cloud, where a teraflop translates to a chip being able to process 1 trillion calculations a second.

On April 18, 2025, the U.S. Court of Appeals for the Federal Circuit (CAFC) decided a case of first impression regarding the intersection of patent claims directed to machine learning training and ...