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 ...

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 ...

Supervised learning algorithms learn from labeled data, where the desired output is known. These algorithms aim to build a model that can predict the output for new, unseen input data. Let’s take a ...

• A new AI machine learning algorithm capable of predicting planetary orbits that may one day help accelerate physics research in other areas such as renewable energy. • Strikingly, the algorithms ...

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 ...

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

In applications like routing, job scheduling, caching, etc., requests arrive sequentially, and the goal of the system is to handle requests as they arrive, while optimizing an appropriate overall ...

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 ...

Consumers are less forgiving of brand failures when algorithms are anthropomorphized, use machine learning, or are used for subjective or interactive tasks. Researchers from University of Texas-Austin ...