<p><strong>Welcome to Xplinary AI: Talking about Complex AI in Simple Ways!</strong> If you're curious about artificial intelligence but don’t want to drown in technical jargon, this is the podcast for you! We break down the most mind-bending concepts in AI into fun, bite-sized discussions that anyone can enjoy. Whether you're a seasoned tech enthusiast or just someone who's fascinated by the future, Xplinary AI keeps things light, engaging, and surprisingly easy to understand. Dive into the world of AI with us—where even the most complex ideas feel refreshingly simple!</p><p>100% original and authentic articles , white papers, and written thought experiment written by a real life human, brought to podcast life by NotebookLM.</p><p>Original source content available on LinkedIn and by request.</p>
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October 7, 2024
<p>the increasing risk of AI exhibiting deceptive behavior because it's trained on data that reflects human behavior, including deception. The authors argue that if we want AI to be honest, helpful, and harmless, we need to carefully consider what data it's trained on and develop clear guidelines to prevent AI from engaging in undesirable behavior. The sources also highlight the difficulty of distinguishing between goal-oriented tasks and games in the context of AI, as AI can apply game strategies to even seemingly straightforward tasks.</p>
October 6, 2024
<p>the concept of time and its relationship to human perception and the universe. The author, a human, grapples with the idea of whether time is a human construct or an objective reality. In response, an AI language model (LLM) provides a structured, logical argument that supports the existence of time as a fundamental aspect of the universe, independent of human observation. The LLM uses scientific principles, such as Einstein's theory of relativity, and logical premises to support its argument. The text then compares the human and AI approaches to the question of time's existence, highlighting the different perspectives and approaches to this philosophical question.</p>
October 6, 2024
<p>the rapid growth of data used to train Large Language Models (LLMs), particularly Meta's LLM. It argues that this expansion is fueled by the inclusion of synthetic data, which is data generated by the LLMs themselves, leading to a cycle of data consumption and regeneration. This process is likened to "synthetic cannibalism," as the LLM consumes its own outputs, and to "incestuous phylogeny," as the model's development is influenced by its own past outputs. The text suggests that this trend could lead to the creation of a self-sustaining synthetic entity, with consequences that may be both beneficial and alarming.</p>
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