Peter Song 
Large Language Models have become ubiquitous in our digital lives, yet they harbor a troubling tendency to fabricate information with unwavering confidence. These “hallucinations” aren’t abstract theoretical concerns—they’re real occurrences that have affected legal cases, medical advice, academic research, and everyday decision-making. By examining concrete examples across different domains, we can better understand the scope, … Read more
Large Language Models have transformed how we interact with artificial intelligence, powering everything from chatbots to writing assistants. But beneath their impressive capabilities lies a persistent challenge: hallucinations. These aren’t psychedelic experiences—they’re instances where AI confidently presents false information as fact. Understanding how often this happens, why it occurs, and what it means for users … Read more
In the rapidly evolving landscape of artificial intelligence, we face a fundamental paradox. The most powerful AI models—deep neural networks, ensemble methods, and complex machine learning algorithms—often operate as “black boxes,” delivering impressive results while concealing their decision-making processes. This opacity creates a critical challenge: how can we trust and responsibly deploy AI systems when … Read more
Machine learning pipelines have become the backbone of modern AI applications, processing sensitive data and making critical decisions across industries. However, as these systems grow more sophisticated, they also become attractive targets for malicious actors. Securing machine learning pipelines isn’t just about protecting data—it’s about safeguarding model integrity, preventing adversarial attacks, and ensuring compliance with … Read more
Traditional keyword-based search engines often fall short when users search for concepts rather than exact terms. If someone searches for “canine companions” in a pet database, they might miss results about “dogs” entirely. This is where semantic search engines powered by vector databases revolutionize information retrieval by understanding meaning rather than just matching words. Semantic … Read more
Working with large datasets in pandas can quickly become a performance bottleneck if not handled properly. As data volumes continue to grow, the difference between optimized and unoptimized pandas code can mean the difference between analysis that completes in minutes versus hours. This comprehensive guide explores proven strategies to dramatically improve pandas performance when dealing … Read more
The democratization of large language models faces a significant challenge: fine-tuning these massive neural networks requires enormous computational resources and memory that most organizations and individual researchers simply don’t have access to. Enter LoRA (Low-Rank Adaptation), an elegant solution that has revolutionized how we adapt pre-trained language models for specific tasks. This technique allows you … Read more
Large Language Models have evolved dramatically over the past few years, with one of the most significant advancements being the expansion of context windows. Modern LLMs can now process tens of thousands or even hundreds of thousands of tokens in a single conversation, opening up unprecedented possibilities for complex tasks. However, with great power comes … Read more
Large language models have become integral to countless applications, from hiring tools and medical diagnostics to content generation and customer service. Yet these powerful systems inherit and often amplify the biases present in their training data, leading to outputs that can perpetuate stereotypes, discrimination, and unfair treatment. A model trained on biased data doesn’t just … Read more
Understanding the variance-bias tradeoff is fundamental to building effective machine learning models. This concept lies at the heart of model selection, helping data scientists navigate the delicate balance between models that are too simple and those that are overly complex. Through real-world examples and practical insights, we’ll explore how this tradeoff impacts your model’s performance … Read more
