Summary In today's episode, I break down why blocking AI crawlers largely fails to protect content and explain how retrieval augmented generation actually works to extend a model's knowledge. Here's what this means for you. You'll understand the real difference between prompting, fine-tuning, and RAG so you can choose the right approach for feeding new…
Summary In today's episode, I tackle the question of whether AI-generated art is really art and explore the copyright questions swirling around it. Here's what this means for you. You'll get a framework for judging machine-made creativity on your own terms and a clear view of the shifting legal landscape. You'll also learn these concepts:…
Summary In today's episode, I break down whether you need something like Llama to build your own custom GPT-style language model and explain the real differences between open source and open weights. Here's what this means for you. You get a practical decision framework for choosing the right customization method based on what your model…
Summary In today's episode, I break down a recent paper showing how easily attackers strip alignment from large language models like GPT-4 and Llama 2. Here's what this means for you. You need to understand that the safety guardrails on these models wear surprisingly thin, which matters enormously if you plan to deploy them in…
Summary In today's episode, I break down why today's text-based generative AI struggles with sarcasm and other tone-dependent language, even when the underlying words are statistically predictable. Here's what this means for you. When your AI outputs miss the mark on nuance, the culprit is usually the missing audio and visual cues that humans use…
Summary In today's episode, I break down the difference between great data, good data, and bad data, and why data quality matters more than tools or talent in AI applications. Here's what this means for you. You will see why feeding AI high-quality data is the single biggest factor in producing reliable, valuable results, no…
Summary In today's episode, I break down the three main categories of machine learning and AI that businesses like printers need to understand, using the FOG acronym. Here's what this means for you. You gain a clear mental model for choosing the right AI tool whether you need to forecast outcomes, organize messy data, or…
Summary In today's episode, I tackle the complex question of how to mitigate bias in AI systems and explain why no single definition of fairness satisfies everyone. Here's what this means for you. You'll gain a practical framework for making and documenting fairness decisions inside your own AI deployments. You'll also learn these concepts: the…
Summary In today's episode, I break down two architectural approaches for building large language models that handle tax and accounting data, including the trade-offs of each. Here's what this means for you. You get a clear framework for deciding whether to fine-tune a model or build an interpreter that connects to an up-to-date data source.…
Summary In today's episode, I explain why training AI on AI-generated content actually improves output quality instead of degrading it. Here's what this means for you. You discover that language models produce statistically average responses, which means well-prompted AI output acts as above-average training material that lifts overall quality beyond what messy human internet content…
