Forbes contributors publish independent expert analyses and insights. I track enterprise software application development & data management. Data is real. We enjoy the use of real world substantiated ...

Advances in high-throughput omic technologies allow for assaying a growing compendium of molecular layers, ranging from genome and epigenome profiling and transcriptomics to proteomics and ...

So-called “unlearning” techniques are used to make a generative AI model forget specific and undesirable info it picked up from training data, like sensitive private data or copyrighted material. But ...

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology ...

Despite the potential of generative models for open-data sharing, an underlying assumption is that the generated samples are unique and not mere patient data replicas. This is crucial, as the primary ...

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models. Two years ago, Yuri Burda and Harri ...

Cover -- Title Page -- Copyright Page -- Table of Contents -- Acknowledgments -- 1 Introduction and Background -- 1.1 Introduction -- 1.2 What This Book Is Not About ...

Enterprise AI success depends on data readiness for AI, including scalable architecture and reliable data pipelines. Vector databases enable AI systems to retrieve relevant information from large ...

From boardroom bedlam to courtroom drama, Sam Altman has had a tumultuous three months. In December, the New York Times filed a federal lawsuit against OpenAI, alleging that the company infringed on ...

Sophie Bushwick: To train a large artificial intelligence model, you need lots of text and images created by actual humans. As the AI boom continues, it's becoming clearer that some of this data is ...