Create accountLogin

Recent searches

No recent searches

Search options

Only available when logged in.
sfba.social is one of the many independent Mastodon servers you can use to participate in the fediverse.
A Mastodon instance for the San Francisco Bay Area. Come on in and join us!

Administered by:

Server stats:

2.4K
active users

sfba.social: AboutProfiles directoryPrivacy policy

Mastodon: AboutGet the appKeyboard shortcutsView source codev4.3.4

#ai4good

2 posts2 participants0 posts today
Public
Envision

sam from the blindlife made the best out of CSUN planning by using ally, the most accessible ai assistant. from conference schedules to picking out a coffee, navigating the hustle and bustle was a breeze. lead your day with more confidence. customize & meet your ally today, download the ally app or go to ally.me?utm_source=social&utm_

Sam from The Blind Life stands outside a convention center, holding up his phone while using the Ally app.
Sam from The Blind Life looks upward while interacting with the Ally app on his phone, against a blue background with orange curves.
Sam from The Blind Life holds his phone close while using the Ally app, focused and engaged, in front of a blue background with orange curves.
#LetsEnvision#PerceivePossibility#AllyEverywhere
Public
Envision

In honor of #IWD2025, we chat with Abby, Envision’s iOS engineer, about her journey as a woman breaking barriers in tech.

She shares insights on:
- Becoming an engineer from a UI/UX background
- Building accessible AI-powered apps
- Encouraging more women to enter STEM

Plus, find out which ally AI personalities she created for herself!

Listen to our podcast on Spotify or find it on Apple Podcasts.

open.spotify.com/episode/5hRS6

#AccelerateAction #AI4Good #Accessibility

open.spotify.com/episode/5hRS6

SpotifyWomen in Tech: Abby’s Journey in AI Assistance & Engineering for Accessibility #IWDEnvision Podcast · Episode
Public
Dan Stowell

I'm pleased to share this, a preprint of our first work on predicting electrical grids using graph neural networks: "Enhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal Features" led by Ugochukwu Orji arxiv.org/abs/2502.08376 #ai4good #forecasting #GNN #electricalgrid

arXiv.orgEnhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal FeaturesAccurate power load forecasting is essential for the efficient operation and planning of electrical grids, particularly given the increased variability and complexity introduced by renewable energy sources. This paper introduces GAT-LSTM, a hybrid model that combines Graph Attention Networks (GAT) and Long Short-Term Memory (LSTM) networks. A key innovation of the model is the incorporation of edge attributes, such as line capacities and efficiencies, into the attention mechanism, enabling it to dynamically capture spatial relationships grounded in grid-specific physical and operational constraints. Additionally, by employing an early fusion of spatial graph embeddings and temporal sequence features, the model effectively learns and predicts complex interactions between spatial dependencies and temporal patterns, providing a realistic representation of the dynamics of power grids. Experimental evaluations on the Brazilian Electricity System dataset demonstrate that the GAT-LSTM model significantly outperforms state-of-the-art models, achieving reductions of 21. 8% in MAE, 15. 9% in RMSE and 20. 2% in MAPE. These results underscore the robustness and adaptability of the GAT-LSTM model, establishing it as a powerful tool for applications in grid management and energy planning.
ExploreLive feeds
About