A central challenge in recommendation systems is incentivizing exploration, encouraging users to select options that help the platform learn the information needed for better future decisions. In some ...

Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards ...

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, ...

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 ...

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, ...

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 ...