Curious how AI language models like ChatGPT burst into the mainstream? Welcome to "All Things LLM," where hosts Alex and AI expert Ben unravel the true origins and evolution of Large Language Models. In this episode, we journey through more than a century of discoveries that paved the way for today’s groundbreaking AI.

Discover:

• The surprising roots of AI language work in 19th-century linguistics and early theories of semantics.
• Alan Turing’s pivotal questions about machine intelligence and how the famous Turing Test shaped early thinking about conversational computers.
• The birth of NLP with ELIZA in the 1960s and the transition from rule-based systems to Statistical Language Models (SLMs) in the 1980s.
• How neural networks and word embeddings revolutionized natural language processing in the 2010s, with techniques like Word2Vec and GloVe making models more “aware” of meaning and context.
• The game-changing introduction of the Transformer architecture in 2017, enabling parallel processing, self-attention, and unprecedented scaling.
• How exponential increases in data and model parameters—seen in BERT, GPT-2, GPT-3, and beyond—led to the “Age of Scale” and unlocked emergent abilities that stunned the world in 2022.

Whether you’re an AI enthusiast, developer, educator, or just want an inside look at how technologies like ChatGPT, Claude, and Gemini came to life, this episode connects the dots from foundational theories to today’s most advanced language models. Learn how decades of linguistic theory, computer science, and mathematics converged into the LLM revolution—the real story behind what feels like an overnight success.

Listen now to explore:

• NLP’s core milestones
• The science and strategy behind next-word prediction
• Why the Transformer changed everything in AI
• The timeline and key breakthroughs leading to today’s AI gold rush

Subscribe, leave a review, and join us next week for an in-depth dive into the mechanics of the Transformer—how “self-attention” drives everything modern AI can do.

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