Over the past decades, computer scientists have developed increasingly sophisticated sensors and machine learning algorithms ...

The rapid advancement of artificial intelligence (AI) and machine learning systems has increased the demand for new hardware components that could speed up data analysis while consuming less power. As ...

It doesn’t take much to make machine-learning algorithms go awry The rise of large-language models could make the problem worse ...

Blockchain technologies are digital systems that work by distributing copies of information across several computers, also ...

A new AI algorithm can associate brain activity with certain behaviors, an advancement that can help improve brain-computer interfaces and find new patterns in neural activity.

Continual learning is when a computer is trained to continuously learn a sequence of tasks, using its accumulated knowledge from old tasks to better learn new tasks. Yet one major hurdle ...

As our artificial intelligence algorithms continue to struggle with metacognition and causal inference, it is essential that we feed them the right data.

For artificial intelligence to realize its potential — to relieve humans from mundane tasks and eventually invent new solutions to our problems — computers will need to get better at seeing the world ...

Having machines learn from experience was once considered a dead end. It’s now critical to artificial intelligence, and work in the field has won two men the highest honor in computer science.