There are concerns about whether or not the programmers building the savvy algorithms that run our lives are still in control of their creations.

MicroAlgo's quantum machine learning algorithms leverage the parallelism and efficiency of quantum computing to accelerate the execution of machine learning tasks, enabling the processing of more ...

Machine learning is all about getting computers to "understand" new concepts, but it's still a pretty inefficient process, often requiring hundreds of examples for training. That may soon change ...

Quantum computers promise enormous computational power, but the nature of quantum states makes computation and data ...

Small-scale quantum computers can enhance machine learning performance, as shown in an experimental study using a photonic quantum processor.

However, training deep learning models requires a great deal of computing power. Another drawback to deep learning is the difficulty of interpreting deep learning models.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

How did machine learning come about? Building algorithms capable of doing this, using the binary “yes” and “no” logic of computers, is the foundation of machine learning – a phrase which ...

The biological world is computational at its core, argues computer scientist Leslie Valiant. His “ecorithm” approach uses computational concepts to explore fundamental mysteries of evolution and the ...