Approaches

It’s no secret that for the previous couple of years, modern technologies have been pushing ethical boundaries under existing legal frameworks that weren’t made to suit them, leading to legal and regulatory minefields. To...

Large language models (LLMs) are rapidly evolving from easy text prediction systems into advanced reasoning engines able to tackling complex challenges. Initially designed to predict the following word in a sentence, these models have...

The flexibility to generate high-quality images quickly is crucial for producing realistic...

Retrieval-Augmented Generation (RAG) is a robust technique that enhances language models by incorporating external information retrieval mechanisms. While standard RAG implementations improve response relevance, they often struggle in complex retrieval scenarios. This text explores...

As powerful as today’s Automatic Speech Recognition (ASR) systems are, the sector is much from “solved.” Researchers and practitioners are grappling with a number of challenges that push the boundaries of what ASR can...

The AI stage at TechCrunch Disrupt 2024 got off to a fiery but constructive start on a panel about combating disinformation. But in a spirited exchange of views tempered by expressions of respect and...

ML Models Must Match Their Use Cases in Drug DiscoveryCo-authored by LabGenius’ CTO, Leo Wossnig.Drug discovery is historically slow, expensive, and riddled with failures — AI/ML is changing this paradigm.The drug development process stays...

Comparing Saturation Function and Partial Dependence for Response Curve GenerationResponse curves play an important role in marketing mix modeling by providing insights into the effectiveness of various marketing variables and their contribution to overall...