Active learning represents a transformative paradigm in machine learning, aimed at reducing the annotation burden by selectively querying the most informative data points. This approach leverages ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

In recent years, machine learning (ML) algorithms have proved themselves to be remarkably useful in helping people deal with different tasks: data classification and clustering, pattern revealing, ...

The algorithms that underlie modern artificial-intelligence (AI) systems need lots of data on which to train. Much of that data comes from the open web which, unfortunately, makes the AIs susceptible ...

Humans have struggled to make truly intelligent machines. Maybe we need to let them get on with it themselves. A little stick figure with a wedge-shaped head shuffles across the screen. It moves in a ...

AI is not limited to diagnostics or imaging. It also plays a transformative role in biomedical research, computational ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

AI (Artificial Intelligence) is a broad concept and its goal is to create intelligent systems whereas Machine Learning is a ...

Deep-learning algorithms significantly enhance clinicians’ ability to correctly identify paediatric elbow fractures, a notoriously challenging diagnosis.