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Physics of AI: Unlocking the Laws Behind Next-Token Prediction
Manage episode 457078592 series 3351512
محتوای ارائه شده توسط IVANCAST PODCAST. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط IVANCAST PODCAST یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Welcome to another episode of the SHIFTERLABS Podcast, where we transform cutting-edge research into accessible insights using Google Notebook LM. This time, we explore the fascinating intersection of physics and AI through the groundbreaking paper Physics in Next-Token Prediction.
What if intelligence in auto-regressive models like ChatGPT was simply a transfer of information governed by physical laws? Discover the First and Second Laws of Information Capacity, which reveal how data compression, entropy, and energy consumption shape the emergence of intelligence. We’ll also dive into the practical implications of these laws, from optimizing AI training to understanding the limits of energy efficiency in computing.
Join us as we demystify the principles that connect physics and AI, bringing clarity to the science behind next-token prediction. Get ready for a thought-provoking journey into the mechanics of intelligence itself!
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