Shelf Blog

AI hallucinations refer to instances where AI systems, particularly language models, generate outputs that are inconsistent, nonsensical, or even entirely fabricated. This issue is especially...

While large language models excel in mimicking human-like content generation, they also pose risks of producing confusing or erroneous responses, often stemming from poor data quality.  Poor data...

Large language models have an impressive ability to generate human-like content, but they also run the risk of generating confusing or inaccurate responses. In some cases, LLM responses can be...

Reinforcement Learning from Human Feedback (RLHF) is a cutting-edge approach in artificial intelligence (AI) that blends human intelligence with machine learning to teach computers how to perform...

AI for predictive analytics represents a cutting-edge approach to forecasting future trends and making data-driven decisions with precision. By integrating AI, you can uncover deep insights,...

Extract, Transform, and Load, or ETL, is a crucial data management process, especially in the AI and machine learning space. It's like a data-moving team that takes information from various sources,...

Data augmentation is a pivotal technique in the realm of Natural Language Processing (NLP). It’s used to expand and diversify training datasets, thereby enhancing the performance and robustness of...

Foundation models are a cornerstone in how we approach, develop, and implement AI technologies. These models, with their ability to learn from vast datasets and adapt to a multitude of tasks,...

Navigating the multifaceted nature of human language is a unique challenge for machines. Ambiguous words, phrases, sentences, and contexts make language models struggle to understand and interpret...