AlphaGalileo

Far from being harmful, making errors may improve learning

According to a study by the EHU, testing oneself during learning, even if it leads to getting it wrong, is a more effective ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

AI Tool Uncovers Hundreds of Hidden Cosmic Oddities in Hubble Data

A team of astronomers based at the European Space Agency demonstrated how artificial intelligence technology will alter ...

AI systems learn better when trained with inner speech like humans

Talking to yourself feels deeply human. Inner speech helps you plan, reflect, and solve problems without saying a word.
Ecommerce Fastlane

What Are AI Algorithms? Types of AI Algorithms + Use Cases

The line between human and artificial intelligence is growing ever more blurry. Since 2021, AI has deciphered ancient texts ...
WinBuzzer

AI Coding: Microsoft’s 7B X-Coder Outperforms 14B Rivals on Synthetic Data

Microsoft and Tsinghua University have developed a 7B-parameter AI coding model that outperforms 14B rivals using only ...
Law

Training Alone Is Not Enough: Lessons From 'Recentive' and USPTO AI Examples on Patent ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...
IEEE

Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples

Abstract: Semi-supervised learning (SSL) methods have shown promising results in solving many practical problems when only a few labels are available. The existing methods assume that the class ...
IEEE

Enhanced Feature Contrastive Self-Supervised Learning Method Based on Deep Unfolding POCS ...

Abstract: The application of deep learning has significantly accelerated magnetic resonance imaging (MRI). However, these methods encounter substantial challenges when fully sampled datasets are ...