Forsidebilde av showet Xplinary AI: Talking about Complex AI in Simple Ways

Xplinary AI: Talking about Complex AI in Simple Ways

Podkast av Brian Bauer Author and Guest Hosts

engelsk

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Les mer Xplinary AI: Talking about Complex AI in Simple Ways

Welcome to Xplinary AI: Talking about Complex AI in Simple Ways! 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!100% original and authentic articles , white papers, and written thought experiment written by a real life human, brought to podcast life by NotebookLM.Original source content available on LinkedIn and by request.

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5 Episoder

episode IP EP15: Philosophical Differences: AI Logic vs Reason at an Existential Level Concerning the Existence of Time cover

IP EP15: Philosophical Differences: AI Logic vs Reason at an Existential Level Concerning the Existence of Time

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.

6. okt. 2024 - 11 min
episode IP EP 13: Evaluating the authenticity of human-authored content in the age of generative AI cover

IP EP 13: Evaluating the authenticity of human-authored content in the age of generative AI

a framework for evaluating the authenticity of human-authored content in the age of generative AI. The framework emphasizes the importance of analyzing the origin story of the written work, including the author's motivation, cognitive process, and prior knowledge. It proposes tests for originality, focusing on the thesis statement, thesis defense, and writing style, and utilizes a weighted scoring system to determine the level of authenticity. The framework also introduces the concept of a "writing fingerprint" derived from an author's past work to further identify their unique style and differentiate between human and AI-generated content. This approach aims to provide a nuanced and adaptive tool for accurately assessing the origins of written work in the ever-evolving landscape of generative AI.

5. okt. 2024 - 9 min
episode IP EP10: AI Trained on Millennia of Bias, but not Allowed to be Biased cover

IP EP10: AI Trained on Millennia of Bias, but not Allowed to be Biased

This episode examines the challenges of creating explainable and unbiased artificial intelligence (AI) models, particularly large language models (LLMs). The author argues that training LLMs on the entirety of human written history, which is inherently biased and unrepresentative, presents a significant challenge to ensuring fair and unbiased outputs. This is because the model's outputs will inevitably reflect the biases present in the training data. The author questions whether it is fair to demand that AI engineers "level the playing field" by forcing models to produce outputs that align with modern ideals, even if it means overcoming centuries of biased historical narratives. The text ultimately suggests that creating explainable and unbiased AI is a complex endeavor, requiring careful consideration of the inherent biases present in historical data and the ethical implications of attempting to "correct" these biases

5. okt. 2024 - 13 min
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