Latest news with #data
New York Times
5 hours ago
- Science
- New York Times
How Astronomers Will Deal With 60 Million Billion Bytes of Imagery
It was not that long ago that astronomers would spend a night looking through a telescope, making careful observations of one or a few points of light. Based on those few observations, they would extrapolate broad generalizations about the universe. 'It was all people could really do at the time, because it was hard to collect data,' said Leanne Guy, the data management scientist at the new Vera C. Rubin Observatory. Rubin, located in Chile and financed by the U.S. Department of Energy and the National Science Foundation, will inundate astronomers with data. Each image taken by Rubin's camera consists of 3.2 billion pixels that may contain previously undiscovered asteroids, dwarf planets, supernovas and galaxies. And each pixel records one of 65,536 shades of gray. That's 6.4 billion bytes of information in just one picture. Ten of those images would contain roughly as much data as all of the words that The New York Times has published in print during its 173-year history. Rubin will capture about 1,000 images each night. As the data from each image is quickly shuffled to the observatory's computer servers, the telescope will pivot to the next patch of sky, taking a picture every 40 seconds or so. It will do that over and over again almost nightly for a decade. The final tally will total about 60 million billion bytes of image data. That is a '6' followed by 16 zeros: 60,000,000,000,000,000. Rubin's 3.2 Gigapixel Camera At the heart of the Rubin observatory is the largest digital camera in the world, a supercooled grid with hundreds of high-resolution sensors. See how the camera works. By The New York Times PERU BOLIVIA BRAZIL ANDES MTS. PARAGUAY Vera C. Rubin Observatory URUGUAY Santiago ARGENTINA CHILE Atlantic Ocean Pacific Ocean By The New York Times Want all of The Times? Subscribe.
Yahoo
7 hours ago
- Business
- Yahoo
11 States That Dominate Social Security Benefits
You've probably heard of the Million Miler Club for airline travel. Meet the Million Household Club for Social Security. Check Out: Learn More: Eleven states have more than a million households receiving Social Security benefits, according to the latest data from the Social Security Administration. Those 11 states account for 57% of all of the households receiving Social Security benefits in the nation. While overall population certainly factors into these rankings, the list of most populous states isn't an exact match with the list of states with the most households receiving benefits. That's because the percentages of households receiving benefits vary widely from state to state. For example, the state with the largest number of households receiving help — California — actually has the fourth-lowest percentage of households receiving help (28.1%). Smaller West Virginia has by far the highest percentage of households receiving Social Security (41.2%). But that amounts to only about 300,000 households, only the 35th highest total among the 50 states. Here's a countdown of the Million Household Club, from the least amount of Social Security households to the most. We've also included each state's percentage of households receiving benefits, the average amount received and the annual cost of living. Also see the states that need Social Security the most. Number of households with Social Security income: 3,779,490 % of households with Social Security income: 28.1% Average Social Security income (annual): $23,022 Annual cost of living: $85,413 California's roughly 3.8 million households receiving benefits is more than 600,000 higher than the next highest state, Florida. The Golden State's average single-family home value ($809,893) is second only to Hawaii ($985,731) among the 50 states. Find Out: Also See: Number of households with Social Security income: 3,139,979 % of households with Social Security income: 36.7% Average Social Security income (annual): $24,048 Annual cost of living: $52,244 Known for its large numbers of retirees, Florida is actually second in the nation for percentage of residents age 65 and up. The Sunshine State's figure of 21.1% trails Maine (21.9%). Of Florida's 65+ population, 11.4% live below the poverty line. Florida also has the highest percentage of residents receiving benefits in this list (36.7%). See More: Number of households with Social Security income: 1,170,920 % of households with Social Security income: 29.2% Average Social Security income (annual): $22,934 Annual cost of living: $46,146 Less than 15% of Georgia residents are age 65 or older — the fourth-lowest total in the nation. The Peach State ranks in the middle of the pack for annual median household income (25th, $75,000) and average single-family home value (27th, $331,000). Number of households with Social Security income: 1,453,430 % of households with Social Security income: 29.1% Average Social Security income (annual): $23,429 Annual cost of living: $42,795 The Prairie State has the seventh-highest number of households receiving Social Security benefits. Nearly 10% of Illinois residents age 65 and up live below the poverty line. Number of households with Social Security income: 1,402,046 % of households with Social Security income: 34.7% Average Social Security income (annual): $24,503 Annual cost of living: $39,532 The nation's 10th-most populous state, Michigan ranks 12th lowest in annual cost of living. It ranks 15th in percentage of households receiving Social Security benefits (34.7%). Also Explore: Number of households with Social Security income: 1,085,771 % of households with Social Security income: 31.2% Average Social Security income (annual): $25,318 Annual cost of living: $64,532 New Jersey has the third-highest household median income in the nation ($101,050). Its annual cost-of-living ($64,532) is the country's fifth-highest. About 10% of the state's residents age 65 and older live below the poverty line. Number of households with Social Security income: 2,445,342 % of households with Social Security income: 31.9% Average Social Security income (annual): $23,330 Annual cost of living: $57,166 New York's average annual cost of living (about $57,000) is only the 13th highest in the nation. It depends on which part of the state you live in, of course, with average living costs in Upstate New York only a small fraction of costs in New York City. Number of households with Social Security income: 1,343,673 % of households with Social Security income: 32.1% Average Social Security income (annual): $23,610 Annual cost of living: $46,728 Roughly 17% of North Carolina's 10.6 million residents are age 65 or older. Of that group, a little more than 10 percent live below the poverty line. North Carolina ranks 37th in the country for annual household median income ($70,000). Find More: Number of households with Social Security income: 1,538,984 % of households with Social Security income: 31.9% Average Social Security income (annual): $22,438 Annual cost of living: $39,178 Ohio offers the 10th lowest average annual cost of living in the United States ($39,178). The Buckeye State is middle-of-the-pack when it comes to percentage of residents age 65 and up beneath the poverty line — its figure of 9.5% ranks 24th. Number of households with Social Security income: 1,829,023 % of households with Social Security income: 34.9% Average Social Security income (annual): $23,989 Annual cost of living: $42,196 The Keystone State has the ninth-highest percentage of residents age 65 and up (19.1%). About 9% of those residents find themselves below the poverty line. Number of households with Social Security income: 2,720,364 % of households with Social Security income: 25.3% Average Social Security income (annual): $22,536 Annual cost of living: $43,956 Texas has the fifth-highest percentage of 65-and-up residents living below the poverty line in the nation (11.7%). The Lone Star State's 3.9 million residents age 65+ is third in the U.S., trailing only California and Florida. Methodology: For this study, GOBanking Rates identified each state's total population, population age 65 and over, total households, household median income, households that receive Social Security income, the average Income for households that receive Social Security income, and the percentage of people aged 65 and over who are below the poverty line (all sourced from the U.S. Census 2023 5-year American Community Survey). Cost-of-living indexes were sourced from Sperling's BestPlaces. Using average expenditures for people age 65 and over as sourced from the Bureau of Labor Statistics Consumer Expenditure Survey, the average expenditure cost was calculated. The average single-family home value was sourced from the Zillow Home Value Index. By assuming a 10% down payment and using the national average 30-year fixed mortgage rate as sourced from the Federal Reserve Economic Data, the average mortgage cost was calculated. Using the average mortgage and average expenditure costs, the average total monthly and annual cost of living was calculated. All data was collected on and is up to date as of May 15, 2025. More From GOBankingRates The 5 Car Brands Named the Least Reliable of 2025 This article originally appeared on 11 States That Dominate Social Security Benefits Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
7 hours ago
- Business
- Yahoo
Uber Expands AI Data Platform to Power Next-Gen Enterprise and AI Lab Needs
SAN FRANCISCO, June 20, 2025--(BUSINESS WIRE)--Uber Technologies, Inc. (NYSE: UBER) today announced a major expansion of its AI data services business, Uber AI Solutions, making its technology platform available to support AI labs and enterprises around the world. The new offerings include customized data solutions for building smarter AI models and agents, global digital task networks, and tools to help companies build and test AI models more efficiently. Over the past decade, Uber has developed deep expertise in collecting, labeling, testing, and localizing data for its own global operations, including optimizing the search of places or menu items, training self-driving car systems, building Gen AI agents for customer support, and translating content in more than 100 languages. Now that same expertise is being made available to other businesses through Uber AI Solutions, the company's dedicated team focused on powering the next generation of artificial intelligence. "We're bringing together Uber's platform, people, and AI systems to help other organizations build smarter AI more quickly," said Megha Yethadka, GM and Head of Uber AI Solutions. "With today's updates, we're scaling our platform globally to meet the growing demand for reliable, real-world AI data." What's New Global digital task platformUber AI Solutions is now available in 30 countries with a platform that connects enterprises to global talent, including experts in coding, finance, law, science, and linguistics. These tasks include annotation, translation, and editing for multi-lingual and multi-modal content. Powered by Uber's foundational platforms for identity, verification, payments and more, this expands Uber's gig work model into the world of AI. A new data foundryA new service that provides ready-to-use and custom-collected datasets—including audio, video, image, and text—to train large AI models. Built with data collected by individuals around the world using Uber technology, the data foundry supports use cases on generative AI, mapping, speech recognition, and others, with built-in privacy and compliance. Agentic AI supportUber AI Solutions is offering the tools and data to help train smart AI agents, including realistic task flows, high-quality annotations, simulations and multilingual support, helping AI agents understand and navigate real-world business processes. Shared infrastructure for AI buildersUber is making its internal platforms available to enterprise clients. These are the same platforms Uber uses to manage large-scale annotation projects and validate AI outputs, and includes AI-powered smart onboarding, quality checks, smart task decomposition and routing, and feedback loops to ensure accuracy and efficiency. Building the human intelligence layer for AI With these advancements, Uber AI Solutions is poised to become the human intelligence layer for AI development worldwide—combining software, operational expertise, and its massive global scale. Looking ahead, Uber is building an AI-powered interface that will allow clients to simply describe their data needs in plain language, letting the platform handle setup, task assignment, workflow optimization, and quality management for scalable AI training. About Uber Uber's mission is to create opportunity through movement. We started in 2010 to solve a simple problem: how do you get access to a ride at the touch of a button? More than 61 billion trips later, we're building products to get people closer to where they want to be. By changing how people, food, and things move through cities, Uber is a platform that opens up the world to new possibilities. View source version on Contacts Uber Press Contact: press@
Reuters
9 hours ago
- Business
- Reuters
India's infrastructure output rises 0.7% year-on-year in May
NEW DELHI, June 20 (Reuters) - India's infrastructure output (ININFR=ECI), opens new tab grew 0.7% year-on-year in May, government data showed on Friday. The index, which tracks activity across eight sectors and makes up 40% of the country's industrial production, grew at a revised 1% in April, compared to the initial estimate of 0.5%.
Forbes
9 hours ago
- Business
- Forbes
Great AI Needs Great (Synthetic) Data
Jennifer Chase is Chief Marketing Officer and Executive Vice President at SAS. Every year, I am asked what marketing innovation I am most excited about, and for 2025, my answer may be surprising. I know you're probably expecting me to say AI agents or AI-created interactive marketing assets, but bear with me as I explain just why I think synthetic data generation should be the most hotly anticipated tech by marketers this year. As marketers, we are not data poor. However, we are data starved. And by that, I mean marketers are starved of cost-effective, high-quality data that we can use to create hyper-personalized marketing. For AI models to effectively run, the model input data must be complete and of good quality. And too often, our datasets have gaping holes. Synthetic data generation is a component of generative AI (GenAI), and with this tech, marketers can generate artificial datasets that share the attributes and characteristics of real customer data, but without any liabilities and limitations. According to Gartner, 'By 2026, 75% of businesses will use generative AI to create synthetic customer data, up from less than 5% in 2023.' Why is this important? Well, for marketers, I believe there are three main reasons: We need good quality data for the development of AI applications. However, this can be a challenge when privacy considerations and regulations are of utmost importance. Synthetic data can help with data privacy by creating data with the same patterns as real data, but with none of the identifying information. This level of data anonymity can help us safeguard personal data. As communications and marketing leaders, we are the trusted stewards of customer data, and I am excited about the role synthetic data can play in helping us protect it. Eradicating bias in our datasets should be a paramount consideration for all marketers. Not only is it unethical, but it also leads to inaccurate analyses that can negatively affect campaign and customer journey effectiveness. The wonder of synthetic data generation is that we can create more representative datasets. For instance, certain groups may be underrepresented, leading to biased model predictions. However, using synthetic data generation, we can create supplementary data for underrepresented groups, ensuring a fair distribution. Additionally, synthetic data can be designed to exclude biases that are often present in datasets. Organizations spend a lot of time acquiring and preparing data. And it's not a one-time process. Data decays. The generation of synthetic data can help limit some of the associated costs that come with that decay. A great way to improve efficiency using synthetic data in marketing is using it to perform look-alike modeling. Using generated data with the same features, structures and attributes as real-life datasets can help brands identify new audiences quickly and at-scale. Something marketers probably don't spend much time thinking about is the cost of data labeling. This is a hidden cost associated with data analysis. Annotating large datasets is time-consuming and expensive. When using data-generation technology, make sure it's designed to include data labeling automatically. Synthetic data has tremendous upside, from privacy protection to mitigating bias and reducing costs, all while improving overall marketing effectiveness. However, with this potential comes responsibility. Marketers must establish clear governance within their organization around when to use synthetic data. Beyond this, make sure you have defined guidelines for labeling and identifying the use of synthetic data to avoid misuse and misunderstanding. As a CMO, I'm always looking for ways to reduce costs while not reducing effectiveness, and synthetic data fits this bill for me. With the myriad ways it can aid marketing, especially in rapid experimentation, I believe synthetic data is going to cement its place in the continued evolution of marketing. Forbes Communications Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?
