Explain The Explainables

Fatih Bildirici PhD(c)

The Odyssey of Artificial Intelligence: Code, Consciousness, and the Human Future What if machines could dream or discover what makes us truly human? We travel into the depths of artificial intelligence in Fatih Bildirici's Explain The Explainables podcast. In a world where code begins to mimic consciousness and ethical dilemmas merge with innovation, we open the mysterious 'black boxes' of neural networks, uncover algorithmic biases, and question whether we are pioneers or mere spectators in the rise of silicon.

エピソード

  1. 2月23日

    Explain Large Language Models: History, Attention, Transformers, ChatGPT, Deepseek and Explainability

    Join us for a groundbreaking episode of Explain the Explainables by your favorite podcast host, Fatih Bildirici PhD(c), unbound where we explore the fascinating world of Large Language Models, starting with the revolutionary 2017 paper "Attention Is All You Need." From its Beatles-inspired title to becoming the foundation of modern AI with over 160,000 citations, we'll uncover how this breakthrough transformed machine learning forever. In this episode, we dive deep into the history, philosophy and architecture that powers tools like ChatGPT and modern AI systems. Through engaging storytelling and clear examples, we'll explore how machines learned to understand human language, the significance of the Transformer architecture, and what it means for our future. Whether you're a tech enthusiast or simply curious about AI, this episode offers an accessible journey into one of the most transformative technologies of our time. We'll also feature insights from explainability side. Don't miss this illuminating exploration of how a single paper revolutionized artificial intelligence and set the stage for the AI revolution we're experiencing today. Recommended resources: Mywebsite: https://fbildirici.github.ioAndrej Karpathy's Intro to LLMs: https://www.youtube.com/watch?v=zjkBMFhNj_gAttention is All You Need: https://arxiv.org/abs/1706.03762Explainability of LLMs: https://dl.acm.org/doi/10.1145/3639372

    27分
  2. 2月10日

    A Brief History & Explanation of Reinforcement Learning: From Pavlov's Dogs to Deepseek & AlphaGo

    Are you ready for the incredible story of,Reinforcement Learning - one of AI's most fascinating fields? In this episode, we're taking you on an extraordinary journey from simple animal experiments tomodern artificial intelligence. How did a story that began with Pavlov's dogs and Thorndike's curious cats transform into systems that can beatGo champions, drive cars autonomously, and know what you'll want to watch next on Netflix?How did machines learn to learn through trial and error, just like humans do? We'll discover how video games became the perfect testing ground for AI, explore AlphaGo's creative moves that stunned world champions, and see how this technology is changing our daily lives. From self-driving cars to smart home devices, you'll learn how reinforcement learning is quietly revolutionizing the world around us. Whether you're an AI enthusiast or simply curious about how machines learn, this episode tells the universal story of learning and growth through an entertaining and thought-provoking journey. If you've ever wondered how the learning machines we've seen in science fiction movies became reality, this episode is for you! Join us for a fascinating exploration of how simple ideas about reward and punishment evolved into one of the most powerful tools in machine learning & artificial intelligence. Get ready to be amazed by the power of learning through trial and error!

    23分

番組について

The Odyssey of Artificial Intelligence: Code, Consciousness, and the Human Future What if machines could dream or discover what makes us truly human? We travel into the depths of artificial intelligence in Fatih Bildirici's Explain The Explainables podcast. In a world where code begins to mimic consciousness and ethical dilemmas merge with innovation, we open the mysterious 'black boxes' of neural networks, uncover algorithmic biases, and question whether we are pioneers or mere spectators in the rise of silicon.