Reinforcement learning (RL) is a branch of artificial intelligence where an agent learns to make decisions through interaction with its environment. The principles of RL rely on concepts of agents, environments, states, actions, reward signals, policies, value functions, and a balance of exploration and exploitation.
Agents interact with their environment, which provides different states that form…
Researchers at Purdue University have presented GTX, a new system designed to efficiently manage large-scale dynamic graphs while supporting high-throughput read-write transactions and competitive graph analytics. This invention solves the issue which current transactional graph systems have with handling temporal localities and hotspots, two common features of real-world graphs.
Notably, managing such graphs is vital…
Researchers from Purdue University have unveiled a new tool, GTX, to address the challenges faced by transactional graph systems in handling large-scale graphs. GTX is designed to be highly efficient in managing dynamic graphs that feature high update arrival rates, temporal localities, and hotspots. Such capabilities are vital for applications including fraud detection, recommendation systems,…
The convergence of imagination, technology, and AI opens up limitless opportunities for fashion designers. AI is viewed as a creative collaborator rather than just a tool and is changing how fashion design, manufacturing, and personalization work. This article features 12 of the top AI fashion designer tools that bring together data, style, and machine intelligence…
Artificial Intelligence (AI) is reshaping the fashion industry, making waves from design to manufacturing to personalization. It is increasingly being considered a creative collaborator, rather than just a tool. This article explores 12 top AI fashion designer tools that illustrate this new era of design where intuition meets data, style encounters algorithmic accuracy, and machine…
Generic transport equations, which consist of time-dependent partial differential equations (PDEs), model the movement of extensive properties like mass, momentum, and energy in physical systems. Originating from conservation laws, such equations shed light on a range of physical phenomena, extending from mass diffusion to Navier-Stokes equations. In science and engineering fields, these PDEs can be…
The application of Graph Neural Network (GNN) for motion planning in robotic systems has surfaced as an innovative solution for efficient strategy formation and navigation. Using GNN, this approach can assess the graph structure of an environment to make quick and informed decisions regarding the best path for a robot to take. Three major systems…
Natural Language Processing (NLP) is an evolving field in which large language models (LLMs) are becoming increasingly important. The fine-tuning of these models has emerged as a critical process for enhancing their specific functionalities without imposing substantial computational demands. In this regard, researchers have been focusing on LLM modifications to ensure optimal performance even with…
Significant advancements have been made in the field of robotics research with the integration of large language models (LLMs) into robotic systems. This development has enabled robots to better tackle complex tasks that demand detailed planning and sophisticated manipulation, bridging the gap between high-level planning and robotic control. However, challenges persist in transforming the remarkable…