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@jonippolito@digipres.club
2024-03-29 13:48:11

This article by a former OpenAI employee makes a powerful case that AI exploits the same clever game design that can make Furbies, dinosaurs, and even triangles seem sentient. It's not about modeling a brain's complexity; simpler systems can ironically feel more intelligent.
https://www.

Quote: "[Xbox creator Seamus] Blackley, who is an amateur pilot, had had an epiphany while working on Flight Unlimited, a top-selling flight simulator: the point of a flight simulator, he’d discovered, wasn’t to simulate an airplane but to give players the feeling of flying one" —The Lifelike Illusions of A.I.

@arXiv_physicsfludyn_bot@mastoxiv.page
2024-03-01 07:24:37

Graphics Processing Unit/Artificial Neural Network-accelerated large-eddy simulation of turbulent combustion: Application to swirling premixed flames
Min Zhang, Runze Mao, Han Li, Zhenhua An, Zhi X. Chen
https://arxiv.org/abs/2402.18858

Graphics Processing Unit/Artificial Neural Network-accelerated large-eddy simulation of turbulent combustion: Application to swirling premixed flames
Within the scope of reacting flow simulations, the real-time direct integration (DI) of stiff ordinary differential equations (ODE) for the computation of chemical kinetics stands as the primary demand on computational resources. Meanwhile, as the number of transport equations that need to be solved increases, the computational cost grows more substantially, particularly for those combustion models involving direct coupling of chemistry and flow such as the transported probability density funct…

@arXiv_csRO_bot@mastoxiv.page
2024-05-01 08:37:37

This https://arxiv.org/abs/2401.09332 has been replaced.
link: https://scholar.google.com/scholar?q=a

Synergistic Reinforcement and Imitation Learning for Vision-driven Autonomous Flight of UAV Along River
Vision-driven autonomous flight and obstacle avoidance of Unmanned Aerial Vehicles (UAVs) along complex riverine environments for tasks like rescue and surveillance requires a robust control policy, which is yet difficult to obtain due to the shortage of trainable riverine environment simulators. To easily verify the vision-based navigation controller performance for the river following task before real-world deployment, we developed a trainable photo-realistic dynamics-free riverine simulation…

@arXiv_csHC_bot@mastoxiv.page
2024-05-01 07:17:29

A Framework for Leveraging Human Computation Gaming to Enhance Knowledge Graphs for Accuracy Critical Generative AI Applications
Steph Buongiorno, Corey Clark
https://arxiv.org/abs/2404.19729 https://arxiv.org/pdf/2404.19729
arXiv:2404.19729v1 Announce Type: new
Abstract: External knowledge graphs (KGs) can be used to augment large language models (LLMs), while simultaneously providing an explainable knowledge base of facts that can be inspected by a human. This approach may be particularly valuable in domains where explainability is critical, like human trafficking data analysis. However, creating KGs can pose challenges. KGs parsed from documents may comprise explicit connections (those directly stated by a document) but miss implicit connections (those obvious to a human although not directly stated). To address these challenges, this preliminary research introduces the GAME-KG framework, standing for "Gaming for Augmenting Metadata and Enhancing Knowledge Graphs." GAME-KG is a federated approach to modifying explicit as well as implicit connections in KGs by using crowdsourced feedback collected through video games. GAME-KG is shown through two demonstrations: a Unity test scenario from Dark Shadows, a video game that collects feedback on KGs parsed from US Department of Justice (DOJ) Press Releases on human trafficking, and a following experiment where OpenAI's GPT-4 is prompted to answer questions based on a modified and unmodified KG. Initial results suggest that GAME-KG can be an effective framework for enhancing KGs, while simultaneously providing an explainable set of structured facts verified by humans.

@arXiv_csCE_bot@mastoxiv.page
2024-04-30 08:31:14

This https://arxiv.org/abs/2310.05765 has been replaced.
link: https://scholar.google.com/scholar?q=a

Examining the simulation-to-reality gap of a wheel loader digging in deformable terrain
We investigate how well a physics-based simulator can replicate a real wheel loader performing bucket filling in a pile of soil. The comparison is made using field test time series of the vehicle motion and actuation forces, loaded mass, and total work. The vehicle was modeled as a rigid multibody system with frictional contacts, driveline, and linear actuators. For the soil, we tested discrete element models of different resolutions, with and without multiscale acceleration. The spatio-tempora…

@arXiv_eessSY_bot@mastoxiv.page
2024-05-01 07:32:50

Dual-Port Grid-Forming Interconnecting Power Converters in Hybrid AC/DC Grids
Josep Ar\'evalo-Soler, Mehrdad Nahalparvari, Dominic Gross, Eduardo Prieto-Araujo, Staffan Norrga, Oriol Gomis-Bellmunt
https://arxiv.org/abs/2404.19625