For a minimal example of how to use the environment framework, refer to examples/simple-calculator. For the environment and training data used in our paper, see AgentBench FC. For reproducing the ...

anthropomorphism: When humans tend to give nonhuman objects humanlike characteristics. In AI, this can include believing a ...

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

Abstract: Cognitive task analysis methods have been extensively applied across various fields since the 1980s. Among these, the GOMS (Goals, Operators, Methods, Selection rules) model stands out as ...

That joyfulness can extend even to the more mundane lessons, like learning new vocabulary, which historically has involved ...

What is supervised learning and how does it work? In this video/post, we break down supervised learning with a simple, real-world example to help you understand this key concept in machine learning.

Over the past decades, roboticists have introduced a wide range of systems that can effectively tackle some real-world problems. Most of these robots, however, often perform poorly on tasks that they ...

As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a ...

Abstract: Due to its property of not requiring prior knowledge of the environment, reinforcement learning (RL) has significant potential for solving quantum control problems. In this work, we ...