Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests. Systems controlled by next-generation computing ...

Training standard AI models against a diverse pool of opponents — rather than building complex hardcoded coordination rules — ...

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 ...

Housing tends to be a key part of household wealth, but despite its importance, it has been difficult to measure the value of a property. In a new article, researchers have studied the impact of a ...

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...

WEST LAFAYETTE, Ind. — To expand the availability of electricity generated from nuclear power, several countries have started developing designs for small modular reactors (SMRs), which could take ...

This course covers three major algorithmic topics in machine learning. Half of the course is devoted to reinforcement learning with the focus on the policy gradient and deep Q-network algorithms. The ...

The original version of this story appeared in Quanta Magazine. Imagine a town with two widget merchants. Customers prefer cheaper widgets, so the merchants must compete to set the lowest price.