LeCun, 65, joined Facebook in December 2013 as the founding director of Fundamental AI Research, known as FAIR. He remains a Silver Professor at New York University, where he has ...

Abstract: To tackle the challenge of data diversity in sentiment analysis and improve the accuracy and generalization ability of sentiment analysis, this study first cleans, denoises, and standardizes ...

Like humans, artificial intelligence learns by trial and error, but traditionally, it requires humans to set the ball rolling by designing the algorithms and rules that govern the learning process.

An artificial-intelligence algorithm that discovers its own way to learn achieves state-of-the-art performance, including on some tasks it had never encountered before. Joel Lehman is at Lila Sciences ...

Imagine a town with two widget merchants. Customers prefer cheaper widgets, so the merchants must compete to set the lowest price. Unhappy with their meager profits, they meet one night in a ...

Parents visiting their children’s kindergarten class for the first time may think they’ve arrived at the wrong room, especially if they expect it to resemble the kindergarten they attended as ...

High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...

W4S operates in turns. The state contains task instructions, the current workflow program, and feedback from prior executions. An action has 2 components, an analysis of what to change, and new Python ...

Cardano (ADA) is still in negative territory, unable to rebound as the broader crypto market struggles to recover from a dramatic crash this week that led to approximately $110 billion being erased ...

South Korea’s AI textbook program was meant to personalize learning, reduce inequality, and lighten teachers’ workload. The initiative was rolled back following complaints about inaccuracies, data ...

Abstract: In Federated Learning (FL), stragglers refer to the slow devices with delayed updates that stall the model aggregation at the server. Because of the inevitable system heterogeneity, low-end ...