Removing the stigma from NFTs
In the ‘How to Talk About NFTs Without Getting Everyone Mad at You’ discussion, our panelists discuss how to navigate the road to acceptance.Read More
How 2022 became the year of generative AI
In 2022, generative AI saw better algorithms and larger models and datasets, which created better images and higher-quality software code.Read More
20 Best Early Black Friday Deals: Robot Vacuums, iPads, Instant Pots
The big sale isn’t here quite yet, but you can already get killer deals on laptops, streaming sticks, tablets, and even Instant Pots.
The Fallout of the FTX Collapse
As Sam Bankman-Fried’s crypto empire crumbles, its customers, and other crypto traders, are paying the price.
Meal Kits Like Blue Apron Get My Picky Kids to Eat Real Food
Are you tearing your hair out trying to feed a family of picky eaters? Try a subscription service like Blue Apron.
Review: ‘Black Panther: Wakanda Forever’ Is Not Your Typical Marvel Movie
Grief slows the pace of Ryan Coogler’s sequel but makes it more meaningful in the process.
Home Office Setup Guide: Webcams, Desks, Chairs, and More (2022)
Gear can make or break your home office setup. Here’s our ultimate list of nearly 100 items, from monitors and desks to webcams and chairs.
Machine learning of binary ‘yes/no’ systems may improve medical diagnoses, financial risk analysis, and more
Similar to a mouse racing through a maze, making “yes” or “no” decisions at every intersection, researchers have developed a way for machines to swiftly learn all the twists and turns in a complex data system.
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
*= Equal Contributions Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from users with user contributing …
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Characterizing Emergent Phenomena in Large Language Models
Posted by Jason Wei and Yi Tay, Research Scientists, Google Research, Brain Team The field of natural language processing (NLP) has been revolutionized by language models trained on large amounts of text data. Scaling up the size of language models often leads to improved performance and sample efficiency on a range of downstream NLP tasks. …
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