The National Oceanic and Atmospheric Administration (Noaa) late last year launched a suite of artificial intelligence-powered global weather forecast models which it said would improve speed, efficiency, and accuracy. In March, an agency official said those models are being trained with centuries of weather data. Artificial intelligence is a valuable tool for weather prediction, but only when it is well-trained with ample data, said Monica Medina, who served as Noaa's principal deputy undersecretary of commerce for oceans and atmosphere from 2009 to 2012.
The purpose of this guide is to provide a more complete context for federal data users and stakeholders that will inspire them to consider a broader range of data types in their research and advocacy; we also hope it will also inform national dialogues about the future of federal data.
Smart energy management just took a step closer to becoming simpler. This week, the organizations behind Matter, the smart-home interoperability standard, and the OpenADR protocol, which sends signals between the grid and the home, announced an agreement to work together. This should make it easier for connected appliances to participate in demand response programs (DR) and, hopefully, save you money.
Deploying a massive, fully precise 16-bit neural network into production often requires renting top-tier cloud instances that destroy an application's profit margins. Applying algorithmic pruning removes mathematically redundant weights, while quantization compresses the remaining parameters from 16-bit floating points down to 8-bit or 4-bit integers. For instance, if a retail enterprise deploys a customer service chatbot, quantizing the model allows it to run on significantly cheaper, lower-memory GPUs without any noticeable drop in conversational quality.
Our results show that testing computer networks with automatically generated digital twins can achieve high accuracy and significantly faster speeds than traditional simulator-based testing.
The case for a warehouse-native CDP starts with control and data centralization. In this model, the data warehouse becomes the single source of truth, with tools layered on top for identity resolution, segmentation and activation.
You make a small change to your table, adding a single row, and it affects data lake performance because, due to the way they work, a new file has to be written that contains one row, and then a bunch of metadata has to be written. This is very inefficient, because formats like Parquet really don't want to store a single row, they want to store a million rows.
