Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Trending Now Mashable Selects Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net All Series Weather conditions will continue deteriorating as the storm barrels toward the coast Friday. Hurricane Isaias is making its way toward landfall on the Gulf Coast late tonight or early Saturday, according to the National Weather Service. The storm strengthened to Category 3 on Friday morning as it continued to push its way toward shore, the National Hurricane Center confirmed . Spaghetti models predict the storm to make landfall somewhere between Gulf Shores, Alabama and Fort Walton Beach, Florida, according to storm tracking site Cyclocane . Spaghetti models are collections of storm-tracking forecasts that plot the storm's path and are overlaid on a map to provide a rough consensus on its track.

The Department of War hosted 'Falcon Peak 25.2' to explore cost-effective counter-drone technology to eliminate drone incursions over sensitive national security sites.

Pre-trained language models(PLM) have made impressive results in various NLP tasks. It has been revealed that one of the key factors to their success is the parameters of these models implicitly learn all kinds of knowledge during pre-training. However, encoding knowledge implicitly in the model parameters has two fundamental drawbacks. First, the knowledge is neither editable nor scalable once the model is trained, which is especially problematic in that knowledge is consistently evolving. Second, it lacks interpretability and prevents humans from understanding which knowledge PLM requires for a certain problem. In this paper, we introduce PlugLM, a pre-training model with differentiable plug-in memory(DPM). The key intuition is to decouple the knowledge storage from model parameters with an editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the DPM. To justify this design choice, we conduct evaluations in three settings including: (1) domain adaptation. PlugLM obtains 3.95 F1 improvements across four domains on average without any in-domain pre-training. (2) knowledge update. PlugLM could absorb new knowledge in a training-free way after pre-training is done. (3) in-task knowledge learning. PlugLM could be further improved by incorporating training samples into DPM with knowledge prompting.