The agent acquires a vocabulary of neuro-symbolic concepts for objects, relations, and actions, represented through a ...

New research shows learning erosion is real—but evidence-anchored design, governance, and assessment can turn AI into an ...

Explore the challenges facing UGC's NEP-driven higher education reforms as colleges struggle to implement new curriculum and ...

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 ...

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its applications. Supervised learning is a type of Machine Learning which learns ...

Abstract: Accurate drug–drug interaction (DDI) prediction is crucial for optimizing the efficacy of combination therapies and minimizing adverse effects. Most existing methods rely on single features ...

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.

Section 1. Purpose. United States leadership in Artificial Intelligence (AI) will promote United States national and economic security and dominance across many domains. Pursuant to Executive Order ...

Abstract: A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and ...

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...

The new reinforcement learning system lets large language models challenge and improve themselves using real-world data instead of curated training sets. Meta researchers have unveiled a new ...