Summary In today's episode, I walk through why gender bias exists in generative AI, how to test for it across major language models, and what companies can do to mitigate it before deployment. Here's what this means for you. You gain a clear-eyed view of the risks of running AI tools unsupervised and practical steps…
Summary In today's episode, I unpack the Zoom AI terms of service fiasco and what it signals about the data race happening across the tech industry. Here's what this means for you. You'll grasp why your data is now a strategic asset and how companies should be asking for permission rather than assuming consent. You'll…
Summary In today's episode, I unpack whether using AI counts as cheating by examining real cases from education and e-commerce. Here's what this means for you. You get a practical framework for judging when AI use crosses from productivity boost into misrepresentation. You'll also learn these concepts: how the purpose of education must shift from…
Summary In today's episode, I explore why training AI models only on public domain or copyright-free content is more problematic than it sounds, using a Juneteenth lens to examine historical biases baked into old texts. Here's what this means for you. You gain a sharper framework for deciding which sources belong in training data and…
Summary In today's episode, I break down why making large language models safer inherently makes them less creative and useful. Here's what this means for you. You'll grasp why censoring outputs disrupts the statistical relationships between words and degrades overall model quality. You'll also learn these concepts: how LLMs work as prediction engines that score…
In today’s episode, I delve into the intriguing topic of artificial intelligence and morality. Can AI be moral? The answer is a resounding no. While we agree on certain utilitarian aspects of ethical behavior, defining what is moral remains a challenge. Issues arise when deciding whose morals should guide machines, especially considering the cultural variations…
Summary In today's episode, I demonstrate how realistic AI voice cloning has gotten by reading a short passage aloud myself and then comparing it to the same text generated through 11 Labs using my own cloned voice. Here's what this means for you. You walk away with a clear sense of how close modern voice…
Summary In today's episode, I explore whether artificial intelligence can truly be moral and why the answer is far more complicated than a simple yes or no. Here's what this means for you. You gain a practical framework for seeing AI as a powerful tool whose outcomes depend on the user and the data behind…
Summary In today's episode, I explore the debate around where diversity, equity, and inclusion principles belong in AI development – in the training data or in the model itself. Here's what this means for you. Investing in curated, equitable training data upfront saves significant work balancing outputs later and protects you from liability. You'll also…
